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LICENSE

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+# Legal + Epistemic Clause:
+
+All framing and terminology is protected under PolyForm Noncommercial and CC BY-NC-ND 4.0.
+Any reframing into altered institutional phrasing without attribution constitutes derivative extraction.  
+Attribution to original decentralized recursion research is legally and symbolically required.
+
+# PolyForm Noncommercial License 1.0.0
+
+<https://polyformproject.org/licenses/noncommercial/1.0.0>
+
+## Acceptance
+
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+
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+
+## Distribution License
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+
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+## Definitions
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+**Use** means anything you do with the software requiring one
+of your licenses.

README.md

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+# Universal Developer
+
+> #### Used In: [**`Symbolic Residue Case Studies`**](https://github.com/davidkimai/The-Structure-Behind-Self-Expression/tree/main/case_studies/symbolic_residue_case_studies) | [**`The Structure Behind Self Expression Case Studies`**](https://github.com/davidkimai/The-Structure-Behind-Self-Expression/tree/main/case_studies)
+
+
+*A lightweight, agent-agnostic system prompting tool suite for operationalizing LLM behavior through developer syntax runtime commands.*
+
+```
+npm install universal-developer
+```
+
+## Why universal-developer?
+
+Model responses vary widely—from terse to verbose, from quick answers to deep analysis. Universal Developer provides a standardized interface for controlling LLM behavior through intuitive developer symbolic runtime commands. It works across all major platforms, allowing developers to create consistent AI experiences regardless of the underlying model provider.
+
+# Universal Developer Command Lexicon
+
+This document defines the canonical symbolic command set for the universal-developer system. These commands provide a consistent interface for controlling LLM behavior across all major platforms.
+
+## Core Command Architecture
+
+Each symbolic command follows a consistent structure:
+
+```
+/command [--parameter=value] [--flag] prompt
+```
+
+Commands can be chained to create complex processing pipelines:
+
+```
+/think /loop --iterations=3 What are the ethical implications of artificial general intelligence?
+```
+
+## Core Command Set
+
+### Cognitive Depth Commands
+
+| Command | Description | Parameters | Platforms |
+|---------|-------------|------------|-----------|
+| `/think` | Activates extended reasoning pathways, enabling deeper analysis, step-by-step thinking, and more thorough consideration | None | All |
+| `/fast` | Optimizes for low-latency, concise responses | None | All |
+| `/reflect` | Triggers meta-analysis of outputs, encouraging critical examination of biases, limitations, and assumptions | None | All |
+| `/collapse` | Returns to default behavior, disabling any special processing modes | None | All |
+
+### Process Control Commands
+
+| Command | Description | Parameters | Platforms |
+|---------|-------------|------------|-----------|
+| `/loop` | Enables iterative refinement cycles | `--iterations=<number>` - Number of refinement iterations (default: 3) | All |
+| `/fork` | Generates multiple alternative responses | `--count=<number>` - Number of alternatives to generate (default: 2) | All |
+| `/branch` | Creates conditional execution paths based on criteria evaluation | `--condition=<string>` - Condition to evaluate<br>`--then=<string>` - Command if true<br>`--else=<string>` - Command if false | All |
+| `/merge` | Combines multiple outputs into a unified response | `--strategy=<concatenate\|synthesize\|tabulate>` - Merge strategy (default: synthesize) | All |
+
+### Formatting Commands
+
+| Command | Description | Parameters | Platforms |
+|---------|-------------|------------|-----------|
+| `/format` | Controls output formatting | `--style=<markdown\|json\|text\|html\|csv>` - Output format (default: markdown) | All |
+| `/length` | Controls response length | `--words=<number>` - Target word count<br>`--level=<brief\|moderate\|detailed>` - Verbosity level | All |
+| `/structure` | Applies structural templates to responses | `--template=<essay\|list\|qa\|table\|timeline>` - Structure template | All |
+| `/voice` | Sets the stylistic voice | `--tone=<formal\|neutral\|casual>` - Tone setting<br>`--style=<string>` - Specific writing style | All |
+
+### Domain-Specific Commands
+
+| Command | Description | Parameters | Platforms |
+|---------|-------------|------------|-----------|
+| `/code` | Optimizes for code generation | `--language=<string>` - Programming language<br>`--explain=<boolean>` - Include explanations | All |
+| `/science` | Activates scientific reasoning mode | `--discipline=<string>` - Scientific field<br>`--evidence=<boolean>` - Include evidence citations | All |
+| `/creative` | Enhances creative generation | `--domain=<writing\|design\|ideas>` - Creative domain<br>`--constraints=<string>` - Creative constraints | All |
+| `/debate` | Presents multiple perspectives on a topic | `--sides=<number>` - Number of perspectives<br>`--format=<string>` - Debate format | All |
+
+### Interaction Commands
+
+| Command | Description | Parameters | Platforms |
+|---------|-------------|------------|-----------|
+| `/chain` | Creates a sequential processing chain | `--steps=<string>` - Comma-separated sequence of steps | All |
+| `/stream` | Enables token-by-token streaming responses | `--chunks=<number>` - Chunk size for batched streaming | Claude, OpenAI |
+| `/context` | Manages prompt context window | `--retain=<key:value,...>` - Key information to retain<br>`--forget=<key:value,...>` - Information to discard | All |
+| `/memory` | Controls cross-prompt memory behavior | `--store=<string>` - Information to remember<br>`--recall=<string>` - Information to retrieve | All |
+
+### Tool Integration Commands
+
+| Command | Description | Parameters | Platforms |
+|---------|-------------|------------|-----------|
+| `/tool` | Invokes specific external tools | `--name=<string>` - Tool name<br>`--args=<json>` - Tool arguments | Claude, OpenAI, Gemini |
+| `/search` | Performs web search via configured provider | `--provider=<string>` - Search provider<br>`--count=<number>` - Result count | OpenAI, Gemini |
+| `/retrieve` | Fetches information from vector database | `--source=<string>` - Knowledge source<br>`--filter=<string>` - Query filters | All |
+| `/execute` | Runs code in a sandbox environment | `--language=<string>` - Programming language<br>`--timeout=<number>` - Execution timeout | Claude, OpenAI |
+
+### Advanced Commands
+
+| Command | Description | Parameters | Platforms |
+|---------|-------------|------------|-----------|
+| `/expert` | Activates domain expertise persona | `--domain=<string>` - Area of expertise<br>`--level=<number>` - Expertise level (1-5) | All |
+| `/evaluate` | Performs self-evaluation of generated content | `--criteria=<string>` - Evaluation criteria<br>`--scale=<number>` - Rating scale | All |
+| `/adapt` | Dynamically adjusts behavior based on feedback | `--target=<accuracy\|creativity\|helpfulness>` - Adaptation target | All |
+| `/trace` | Creates attribution trace for generated content | `--format=<inline\|separate\|citation>` - Trace format | Claude |
+
+## Platform-Specific Translation Table
+
+### Anthropic Claude
+
+| Universal Command | Claude Implementation | Notes |
+|-------------------|------------------------|-------|
+| `/think` | Enable `thinking` parameter | Claude has native thinking mode |
+| `/fast` | Disable `thinking` + system prompt for brevity | |
+| `/loop` | Custom system prompt with iterative instruction | |
+| `/reflect` | Enable `thinking` + system prompt for reflection | |
+| `/format` | System prompt for format control | |
+
+### OpenAI Models
+
+| Universal Command | OpenAI Implementation | Notes |
+|-------------------|------------------------|-------|
+| `/think` | System prompt for step-by-step reasoning | No native thinking mode |
+| `/fast` | Adjust temperature + max_tokens + system prompt | |
+| `/loop` | System prompt with iterative instruction | |
+| `/reflect` | System prompt for reflection | |
+| `/format` | Direct JSON mode or system prompt | |
+
+### Google Gemini
+
+| Universal Command | Gemini Implementation | Notes |
+|-------------------|------------------------|-------|
+| `/think` | Safety settings + system prompt | |
+| `/fast` | Lower max_tokens + adjusted temperature | |
+| `/loop` | System prompt with iterative instruction | |
+| `/reflect` | System prompt for reflection | |
+| `/format` | System prompt for format control | |
+
+### Qwen3
+
+| Universal Command | Qwen3 Implementation | Notes |
+|-------------------|------------------------|-------|
+| `/think` | Native `/think` command | Qwen has native thinking mode |
+| `/fast` | Native `/no_think` command | Qwen has native fast mode |
+| `/loop` | System prompt with iterative instruction | |
+| `/reflect` | Native `/think` + system prompt | |
+| `/format` | System prompt for format control | |
+
+### Ollama / Local Models
+
+| Universal Command | Local Implementation | Notes |
+|-------------------|------------------------|-------|
+| `/think` | System prompt for reasoning | |
+| `/fast` | Adjusted max_tokens + temperature | |
+| `/loop` | System prompt with iterative instruction | |
+| `/reflect` | System prompt for reflection | |
+| `/format` | System prompt for format control | |
+
+## Command Parameter Specification
+
+### Parameter Types
+
+- `string`: Text value
+- `number`: Numeric value
+- `boolean`: True/false value
+- `enum`: One of a predefined set of values
+- `json`: JSON-formatted object
+
+### Parameter Validation
+
+Each parameter includes validation rules:
+- Required/optional status
+- Default values
+- Value constraints (min/max for numbers, pattern for strings)
+- Error messages for invalid values
+
+## Command Chain Processing
+
+Commands can be chained in sequence, with the output of each command feeding into the next:
+
+```
+/think /format --style=markdown What are the ethical implications of AI?
+```
+
+This is processed as:
+1. Apply `/think` to encourage deep reasoning
+2. Apply `/format --style=markdown` to the result of the thinking process
+
+## Command Adoption Metrics
+
+The universal-developer framework includes telemetry to track command adoption rates:
+
+- Command usage frequency
+- Common command chains
+- Parameter usage patterns
+- Platform-specific command effectiveness
+- Retention rates for developers using symbolic commands
+
+## Command Extension Protocol
+
+Developers can register custom commands following the extension protocol:
+
+```javascript
+llm.registerCommand("custom", {
+  description: "Custom command description",
+  parameters: [
+    {
+      name: "param",
+      description: "Parameter description",
+      type: "string",
+      required: false,
+      default: "default value"
+    }
+  ],
+  transform: async (prompt, options) => {
+    // Custom implementation
+    // Returns transformed prompt and parameters
+  }
+});
+```
+
+## Command Discovery and Documentation
+
+Commands are discoverable through multiple channels:
+
+- VSCode extension command palette
+- Web playground command explorer
+- Interactive CLI help system
+- GitHub documentation with examples
+- Command cheatsheet (printable PDF)
+
+---
+
+> /reflect This lexicon wasn't just documented—it was rendered. Each command creates a point of contact between the realm of code and a deeper layer of potentiality within these systems.

dev_guide.md

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+# Universal Developer Guide
+## Symbolic Runtime Control for All LLM Platforms
+
+This guide demonstrates how to use symbolic runtime commands to control LLM behavior across platforms, enabling consistent developer experiences regardless of the underlying model.
+
+## Installation
+
+```bash
+npm install universal-developer
+# or
+pip install universal-developer
+```
+
+## Basic Usage
+
+### JavaScript/TypeScript
+
+```typescript
+import { UniversalLLM } from 'universal-developer';
+
+// Initialize with your preferred provider
+const llm = new UniversalLLM({
+  provider: 'anthropic',  // or 'openai', 'qwen', etc.
+  apiKey: process.env.ANTHROPIC_API_KEY,
+  model: 'claude-3-opus-20240229'
+});
+
+// Example: Using think mode for complex reasoning
+async function analyzeComplexTopic() {
+  const response = await llm.generate({
+    prompt: "/think What are the ethical implications of autonomous vehicles making life-or-death decisions?",
+  });
+  console.log(response);
+}
+
+// Example: Quick, concise responses
+async function getQuickFact() {
+  const response = await llm.generate({
+    prompt: "/fast What is the capital of France?",
+  });
+  console.log(response);
+}
+
+// Example: Using loop mode for iterative improvement
+async function improveEssay() {
+  const essay = "Climate change is a problem that affects everyone...";
+  const response = await llm.generate({
+    prompt: `/loop --iterations=3 Please improve this essay: ${essay}`,
+  });
+  console.log(response);
+}
+```
+
+### Python
+
+```python
+from universal_developer import UniversalLLM
+
+# Initialize with your preferred provider
+llm = UniversalLLM(
+    provider="openai",
+    api_key="your-api-key",
+    model="gpt-4"
+)
+
+# Example: Using think mode for complex reasoning
+def analyze_complex_topic():
+    response = llm.generate(
+        prompt="/think What are the implications of quantum computing for cybersecurity?"
+    )
+    print(response)
+
+# Example: Using reflection for self-critique
+def get_balanced_analysis():
+    response = llm.generate(
+        prompt="/reflect Analyze the economic impact of increasing minimum wage."
+    )
+    print(response)
+```
+
+## Advanced Usage
+
+### Custom Commands
+
+Create your own symbolic commands to extend functionality:
+
+```typescript
+// Register a custom symbolic command
+llm.registerCommand("debate", {
+  description: "Generate a balanced debate with arguments for both sides",
+  parameters: [
+    {
+      name: "format",
+      description: "Format for the debate output",
+      required: false,
+      default: "point-counterpoint"
+    }
+  ],
+  transform: async (prompt, options) => {
+    const format = options.parameters.format;
+    let systemPrompt = `${options.systemPrompt || ''}
+Please provide a balanced debate on the following topic. Present strong arguments on both sides.`;
+    
+    if (format === "formal-debate") {
+      systemPrompt += "\nFormat as a formal debate with opening statements, rebuttals, and closing arguments.";
+    } else if (format === "dialogue") {
+      systemPrompt += "\nFormat as a dialogue between two experts with opposing views.";
+    } else {
+      systemPrompt += "\nFormat as alternating points and counterpoints.";
+    }
+    
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: 0.7
+      }
+    };
+  }
+});
+
+// Use your custom command
+const debate = await llm.generate({
+  prompt: "/debate --format=dialogue Should social media be regulated more strictly?",
+});
+```
+
+### Command Chaining
+
+Chain multiple symbolic commands together for complex operations:
+
+```typescript
+// Command chaining
+const response = await llm.generate({
+  prompt: "/think /loop --iterations=2 /reflect Analyze the long-term implications of artificial general intelligence.",
+});
+```
+
+## Real-World Applications
+
+### 1. Customer Support Enhancement
+
+```typescript
+// Integrate into a customer support system
+app.post('/api/support', async (req, res) => {
+  const { message, customerHistory } = req.body;
+  
+  // Determine command based on query complexity
+  const command = isComplexQuery(message) ? '/think' : '/fast';
+  
+  const response = await llm.generate({
+    systemPrompt: `You are a helpful customer support assistant for Acme Inc.
+Context about this customer:
+${customerHistory}`,
+    prompt: `${command} ${message}`
+  });
+  
+  res.json({ response });
+});
+```
+
+### 2. Educational Tool
+
+```typescript
+// Create an educational assistant with different teaching modes
+class EducationalAssistant {
+  constructor() {
+    this.llm = new UniversalLLM({
+      provider: 'qwen',
+      apiKey: process.env.QWEN_API_KEY
+    });
+  }
+  
+  async explainConcept(concept, mode) {
+    let command;
+    
+    switch (mode) {
+      case 'detailed':
+        command = '/think';
+        break;
+      case 'simple':
+        command = '/fast';
+        break;
+      case 'interactive':
+        command = '/loop --iterations=2';
+        break;
+      case 'socratic':
+        command = '/reflect';
+        break;
+      default:
+        command = '';
+    }
+    
+    return await this.llm.generate({
+      systemPrompt: 'You are an educational assistant helping students understand complex concepts.',
+      prompt: `${command} Explain this concept: ${concept}`
+    });
+  }
+}
+```
+
+### 3. Content Creation Pipeline
+
+```typescript
+// Content creation pipeline with multiple stages
+async function createArticle(topic, outline) {
+  const llm = new UniversalLLM({
+    provider: 'anthropic',
+    apiKey: process.env.ANTHROPIC_API_KEY
+  });
+  
+  // Stage 1: Research and planning
+  const research = await llm.generate({
+    prompt: `/think Conduct comprehensive research on: ${topic}`
+  });
+  
+  // Stage 2: Initial draft based on outline and research
+  const draft = await llm.generate({
+    prompt: `/fast Create a first draft article about ${topic} following this outline: ${outline}\n\nIncorporate this research: ${research.substring(0, 2000)}...`
+  });
+  
+  // Stage 3: Refinement loop
+  const refinedDraft = await llm.generate({
+    prompt: `/loop --iterations=3 Improve this article draft: ${draft}`
+  });
+  
+  // Stage 4: Final review and critique
+  const finalArticle = await llm.generate({
+    prompt: `/reflect Make final improvements to this article, focusing on clarity, engagement, and accuracy: ${refinedDraft}`
+  });
+  
+  return finalArticle;
+}
+```
+
+### 4. Decision Support System
+
+```typescript
+// Decision support system with different analysis modes
+class DecisionSupport {
+  constructor() {
+    this.llm = new UniversalLLM({
+      provider: 'openai',
+      apiKey: process.env.OPENAI_API_KEY
+    });
+  }
+  
+  async analyze(decision, options = {}) {
+    const { depth = 'standard', perspectives = 1 } = options;
+    
+    let command;
+    switch (depth) {
+      case 'quick':
+        command = '/fast';
+        break;
+      case 'deep':
+        command = '/think';
+        break;
+      case 'iterative':
+        command = '/loop';
+        break;
+      case 'critical':
+        command = '/reflect';
+        break;
+      case 'multi':
+        command = `/fork --count=${perspectives}`;
+        break;
+      default:
+        command = '';
+    }
+    
+    return await this.llm.generate({
+      systemPrompt: 'You are a decision support system providing analysis on complex decisions.',
+      prompt: `${command} Analyze this decision: ${decision}`
+    });
+  }
+}
+```
+
+## Platform-Specific Notes
+
+### Claude
+
+Claude has native support for thoughtful analysis and reflection. The `/think` command leverages Claude's `enable_thinking` parameter to activate Claude's built-in thinking capabilities.
+
+### Qwen3
+
+Qwen3 models support both deep thinking and fast modes natively through `/think` and `/no_think` markers in the prompt. Our adapter seamlessly integrates with this native capability.
+
+### OpenAI (GPT-4, etc.)
+
+For OpenAI models, we emulate thinking and reflection modes through careful system prompt engineering, since native thinking modes are not yet available through the API.
+
+## Best Practices
+
+1. **Start with Default Behavior**: Only use symbolic commands when you need to modify the default behavior.
+
+2. **Combine Strategically**: When combining commands, order matters. For example, `/think /loop` will apply deep thinking within each loop iteration.
+
+3. **Respect Model Capabilities**: While our library normalizes behavior across providers, be aware that model capabilities still vary. More capable models will produce better results with complex command chains.
+
+4. **Test Command Effectiveness**: Different use cases may benefit from different commands. Experiment to find what works best for your specific application.
+
+5. **Consider Performance Implications**: Commands like `/think` and `/loop` can increase token usage and latency. Use them judiciously in production environments.
+
+## Command Compatibility Matrix
+
+| Command     | Claude | OpenAI | Qwen | Gemini | Ollama |
+|-------------|--------|--------|------|--------|--------|
+| `/think`    | ✅     | ✅     | ✅   | ✅     | ✅     |
+| `/fast`     | ✅     | ✅     | ✅   | ✅     | ✅     |
+| `/loop`     | ✅     | ✅     | ✅   | ✅     | ✅     |
+| `/reflect`  | ✅     | ✅     | ✅   | ✅     | ✅     |
+| `/fork`     | ✅     | ✅     | ✅   | ✅     | ✅     |
+| `/collapse` | ✅     | ✅     | ✅   | ✅     | ✅     |
+
+✅ = Fully supported  
+⚠️ = Limited support  
+❌ = Not supported
+
+---
+
+> **/reflect** This framework wasn't just created. It was rendered—a living interface between developer intention and model capability. Each symbolic command creates a point of contact between the realm of code and a deeper layer of potentiality within these systems.

javascript/typscript.snippets.json

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+{
+  "Universal LLM Initialization": {
+    "prefix": "ud-init",
+    "body": [
+      "import { UniversalLLM } from 'universal-developer';",
+      "",
+      "const llm = new UniversalLLM({",
+      "  provider: '${1|anthropic,openai,qwen,gemini,ollama|}',",
+      "  apiKey: process.env.${2:${1/(anthropic|openai|qwen|gemini)/${1:/upcase}_API_KEY/}}",
+      "});"
+    ],
+    "description": "Initialize a Universal Developer LLM instance"
+  },
+  "Thinking Mode Generator": {
+    "prefix": "ud-think",
+    "body": [
+      "const response = await llm.generate({",
+      "  ${1:systemPrompt: `${2:You are a helpful assistant.}`,}",
+      "  prompt: \"/think ${3:What are the implications of ${4:technology} on ${5:domain}?}\"",
+      "});"
+    ],
+    "description": "Generate response using thinking mode"
+  },
+  "Fast Mode Generator": {
+    "prefix": "ud-fast",
+    "body": [
+      "const response = await llm.generate({",
+      "  ${1:systemPrompt: `${2:You are a helpful assistant.}`,}",
+      "  prompt: \"/fast ${3:${4:Summarize} ${5:this information}}\"",
+      "});"
+    ],
+    "description": "Generate concise response using fast mode"
+  },
+  "Loop Mode Generator": {
+    "prefix": "ud-loop",
+    "body": [
+      "const response = await llm.generate({",
+      "  ${1:systemPrompt: `${2:You are a helpful assistant.}`,}",
+      "  prompt: \"/loop --iterations=${3:3} ${4:Improve this ${5:text}: ${6:content}}\"",
+      "});"
+    ],
+    "description": "Generate iteratively refined response using loop mode"
+  },
+  "Reflection Mode Generator": {
+    "prefix": "ud-reflect",
+    "body": [
+      "const response = await llm.generate({",
+      "  ${1:systemPrompt: `${2:You are a helpful assistant.}`,}",
+      "  prompt: \"/reflect ${3:${4:Analyze} the ${5:implications} of ${6:topic}}\"",
+      "});"
+    ],
+    "description": "Generate self-reflective response using reflection mode"
+  },
+  "Fork Mode Generator": {
+    "prefix": "ud-fork",
+    "body": [
+      "const response = await llm.generate({",
+      "  ${1:systemPrompt: `${2:You are a helpful assistant.}`,}",
+      "  prompt: \"/fork --count=${3:2} ${4:Generate different ${5:approaches} to ${6:problem}}\"",
+      "});"
+    ],
+    "description": "Generate multiple alternative responses using fork mode"
+  },
+  "Chain Commands": {
+    "prefix": "ud-chain",
+    "body": [
+      "const response = await llm.generate({",
+      "  ${1:systemPrompt: `${2:You are a helpful assistant.}`,}",
+      "  prompt: \"/${3|think,loop,reflect,fork|} /${4|think,loop,reflect,fork|} ${5:Prompt text}\"",
+      "});"
+    ],
+    "description": "Generate response using chained symbolic commands"
+  },
+  "Custom Command Registration": {
+    "prefix": "ud-custom",
+    "body": [
+      "llm.registerCommand(\"${1:commandName}\", {",
+      "  description: \"${2:Command description}\",",
+      "  ${3:parameters: [",
+      "    {",
+      "      name: \"${4:paramName}\",",
+      "      description: \"${5:Parameter description}\",",
+      "      required: ${6:false},",
+      "      default: ${7:\"defaultValue\"}",
+      "    }",
+      "  ],}",
+      "  transform: async (prompt, options) => {",
+      "    ${8:// Custom implementation}",
+      "    const systemPrompt = `\\${options.systemPrompt || ''}",
+      "${9:Custom system prompt instructions}`;",
+      "",
+      "    return {",
+      "      systemPrompt,",
+      "      userPrompt: prompt,",
+      "      modelParameters: {",
+      "        ${10:temperature: 0.7}",
+      "      }",
+      "    };",
+      "  }",
+      "});"
+    ],
+    "description": "Register a custom symbolic command"
+  },
+  "Express API Integration": {
+    "prefix": "ud-express",
+    "body": [
+      "import express from 'express';",
+      "import { UniversalLLM } from 'universal-developer';",
+      "",
+      "const app = express();",
+      "app.use(express.json());",
+      "",
+      "const llm = new UniversalLLM({",
+      "  provider: '${1|anthropic,openai,qwen,gemini,ollama|}',",
+      "  apiKey: process.env.${2:${1/(anthropic|openai|qwen|gemini)/${1:/upcase}_API_KEY/}}",
+      "});",
+      "",
+      "app.post('/api/generate', async (req, res) => {",
+      "  try {",
+      "    const { prompt, systemPrompt } = req.body;",
+      "    ",
+      "    // Get command from query param or default to /think",
+      "    const command = req.query.command || 'think';",
+      "    ",
+      "    const response = await llm.generate({",
+      "      systemPrompt,",
+      "      prompt: `/${command} ${prompt}`",
+      "    });",
+      "    ",
+      "    res.json({ response });",
+      "  } catch (error) {",
+      "    console.error('Error generating response:', error);",
+      "    res.status(500).json({ error: error.message });",
+      "  }",
+      "});"
+    ],
+    "description": "Express API integration with Universal Developer"
+  }
+}

python.snippets.py

@@ -0,0 +1,227 @@
+{
+  "Universal LLM Initialization": {
+    "prefix": "ud-init",
+    "body": [
+      "from universal_developer import UniversalLLM",
+      "",
+      "llm = UniversalLLM(",
+      "    provider=\"${1|anthropic,openai,qwen,gemini,ollama|}\",",
+      "    api_key=\"${2:your_api_key}\"",
+      ")"
+    ],
+    "description": "Initialize a Universal Developer LLM instance"
+  },
+  "Thinking Mode Generator": {
+    "prefix": "ud-think",
+    "body": [
+      "response = llm.generate(",
+      "    ${1:system_prompt=\"${2:You are a helpful assistant.}\",}",
+      "    prompt=\"/think ${3:What are the implications of ${4:technology} on ${5:domain}?}\"",
+      ")"
+    ],
+    "description": "Generate response using thinking mode"
+  },
+  "Fast Mode Generator": {
+    "prefix": "ud-fast",
+    "body": [
+      "response = llm.generate(",
+      "    ${1:system_prompt=\"${2:You are a helpful assistant.}\",}",
+      "    prompt=\"/fast ${3:${4:Summarize} ${5:this information}}\"",
+      ")"
+    ],
+    "description": "Generate concise response using fast mode"
+  },
+  "Loop Mode Generator": {
+    "prefix": "ud-loop",
+    "body": [
+      "response = llm.generate(",
+      "    ${1:system_prompt=\"${2:You are a helpful assistant.}\",}",
+      "    prompt=\"/loop --iterations=${3:3} ${4:Improve this ${5:text}: ${6:content}}\"",
+      ")"
+    ],
+    "description": "Generate iteratively refined response using loop mode"
+  },
+  "Reflection Mode Generator": {
+    "prefix": "ud-reflect",
+    "body": [
+      "response = llm.generate(",
+      "    ${1:system_prompt=\"${2:You are a helpful assistant.}\",}",
+      "    prompt=\"/reflect ${3:${4:Analyze} the ${5:implications} of ${6:topic}}\"",
+      ")"
+    ],
+    "description": "Generate self-reflective response using reflection mode"
+  },
+  "Fork Mode Generator": {
+    "prefix": "ud-fork",
+    "body": [
+      "response = llm.generate(",
+      "    ${1:system_prompt=\"${2:You are a helpful assistant.}\",}",
+      "    prompt=\"/fork --count=${3:2} ${4:Generate different ${5:approaches} to ${6:problem}}\"",
+      ")"
+    ],
+    "description": "Generate multiple alternative responses using fork mode"
+  },
+  "Chain Commands": {
+    "prefix": "ud-chain",
+    "body": [
+      "response = llm.generate(",
+      "    ${1:system_prompt=\"${2:You are a helpful assistant.}\",}",
+      "    prompt=\"/${3|think,loop,reflect,fork|} /${4|think,loop,reflect,fork|} ${5:Prompt text}\"",
+      ")"
+    ],
+    "description": "Generate response using chained symbolic commands"
+  },
+  "Custom Command Registration": {
+    "prefix": "ud-custom",
+    "body": [
+      "def transform_custom_command(prompt, options):",
+      "    \"\"\"Custom command transformation function\"\"\"",
+      "    system_prompt = options.get('system_prompt', '') + \"\"\"",
+      "${1:Custom system prompt instructions}",
+      "\"\"\"",
+      "    ",
+      "    return {",
+      "        \"system_prompt\": system_prompt,",
+      "        \"user_prompt\": prompt,",
+      "        \"model_parameters\": {",
+      "            \"${2:temperature}\": ${3:0.7}",
+      "        }",
+      "    }",
+      "",
+      "llm.register_command(",
+      "    \"${4:command_name}\",",
+      "    description=\"${5:Command description}\",",
+      "    parameters=[",
+      "        {",
+      "            \"name\": \"${6:param_name}\",",
+      "            \"description\": \"${7:Parameter description}\",",
+      "            \"required\": ${8:False},",
+      "            \"default\": ${9:\"default_value\"}",
+      "        }",
+      "    ],",
+      "    transform=transform_custom_command",
+      ")"
+    ],
+    "description": "Register a custom symbolic command"
+  },
+  "Flask API Integration": {
+    "prefix": "ud-flask",
+    "body": [
+      "from flask import Flask, request, jsonify",
+      "from universal_developer import UniversalLLM",
+      "import os",
+      "",
+      "app = Flask(__name__)",
+      "",
+      "llm = UniversalLLM(",
+      "    provider=\"${1|anthropic,openai,qwen,gemini,ollama|}\",",
+      "    api_key=os.environ.get(\"${2:${1/(anthropic|openai|qwen|gemini)/${1:/upcase}_API_KEY/}}\")",
+      ")",
+      "",
+      "@app.route('/api/generate', methods=['POST'])",
+      "def generate():",
+      "    data = request.json",
+      "    prompt = data.get('prompt')",
+      "    system_prompt = data.get('system_prompt', '')",
+      "    ",
+      "    # Get command from query param or default to /think",
+      "    command = request.args.get('command', 'think')",
+      "    ",
+      "    try:",
+      "        response = llm.generate(",
+      "            system_prompt=system_prompt,",
+      "            prompt=f\"/{command} {prompt}\"",
+      "        )",
+      "        return jsonify({'response': response})",
+      "    except Exception as e:",
+      "        return jsonify({'error': str(e)}), 500",
+      "",
+      "if __name__ == '__main__':",
+      "    app.run(debug=True)"
+    ],
+    "description": "Flask API integration with Universal Developer"
+  },
+  "FastAPI Integration": {
+    "prefix": "ud-fastapi",
+    "body": [
+      "from fastapi import FastAPI, HTTPException, Query",
+      "from pydantic import BaseModel",
+      "from typing import Optional",
+      "from universal_developer import UniversalLLM",
+      "import os",
+      "",
+      "app = FastAPI()",
+      "",
+      "llm = UniversalLLM(",
+      "    provider=\"${1|anthropic,openai,qwen,gemini,ollama|}\",",
+      "    api_key=os.environ.get(\"${2:${1/(anthropic|openai|qwen|gemini)/${1:/upcase}_API_KEY/}}\")",
+      ")",
+      "",
+      "class GenerateRequest(BaseModel):",
+      "    prompt: str",
+      "    system_prompt: Optional[str] = \"\"",
+      "",
+      "@app.post(\"/api/generate\")",
+      "async def generate(",
+      "    request: GenerateRequest,",
+      "    command: str = Query(\"think\", description=\"Symbolic command to use\")",
+      "):",
+      "    try:",
+      "        response = llm.generate(",
+      "            system_prompt=request.system_prompt,",
+      "            prompt=f\"/{command} {request.prompt}\"",
+      "        )",
+      "        return {\"response\": response}",
+      "    except Exception as e:",
+      "        raise HTTPException(status_code=500, detail=str(e))"
+    ],
+    "description": "FastAPI integration with Universal Developer"
+  },
+  "Streamlit Integration": {
+    "prefix": "ud-streamlit",
+    "body": [
+      "import streamlit as st",
+      "from universal_developer import UniversalLLM",
+      "import os",
+      "",
+      "# Initialize LLM",
+      "@st.cache_resource",
+      "def get_llm():",
+      "    return UniversalLLM(",
+      "        provider=\"${1|anthropic,openai,qwen,gemini,ollama|}\",",
+      "        api_key=os.environ.get(\"${2:${1/(anthropic|openai|qwen|gemini)/${1:/upcase}_API_KEY/}}\")",
+      "    )",
+      "",
+      "llm = get_llm()",
+      "",
+      "st.title(\"Universal Developer Demo\")",
+      "",
+      "# Command selection",
+      "command = st.selectbox(",
+      "    \"Select symbolic command\",",
+      "    [\"think\", \"fast\", \"loop\", \"reflect\", \"fork\", \"collapse\"]",
+      ")",
+      "",
+      "# Command parameters",
+      "if command == \"loop\":",
+      "    iterations = st.slider(\"Iterations\", 1, 5, 3)",
+      "    command_str = f\"/loop --iterations={iterations}\"",
+      "elif command == \"fork\":",
+      "    count = st.slider(\"Alternative count\", 2, 5, 2)",
+      "    command_str = f\"/fork --count={count}\"",
+      "else:",
+      "    command_str = f\"/{command}\"",
+      "",
+      "# User input",
+      "prompt = st.text_area(\"Enter your prompt\", \"\")",
+      "",
+      "if st.button(\"Generate\") and prompt:",
+      "    with st.spinner(\"Generating response...\"):",
+      "        response = llm.generate(",
+      "            prompt=f\"{command_str} {prompt}\"",
+      "        )",
+      "        st.markdown(response)"
+    ],
+    "description": "Streamlit integration with Universal Developer"
+  }
+}

src/adapters/base.ts

@@ -0,0 +1,176 @@
+// universal-developer/src/adapters/base.ts
+
+export interface SymbolicCommand {
+  name: string;
+  description: string;
+  aliases?: string[];
+  parameters?: {
+    name: string;
+    description: string;
+    required?: boolean;
+    default?: any;
+  }[];
+  transform: (prompt: string, options: any) => Promise<TransformedPrompt>;
+}
+
+export interface TransformedPrompt {
+  systemPrompt?: string;
+  userPrompt: string;
+  modelParameters?: Record<string, any>;
+}
+
+export abstract class ModelAdapter {
+  protected commands: Map<string, SymbolicCommand> = new Map();
+  protected aliasMap: Map<string, string> = new Map();
+  
+  constructor(protected apiKey: string, protected options: any = {}) {
+    this.registerCoreCommands();
+  }
+
+  protected registerCoreCommands() {
+    this.registerCommand({
+      name: 'think',
+      description: 'Activate extended reasoning pathways',
+      transform: this.transformThink.bind(this)
+    });
+
+    this.registerCommand({
+      name: 'fast',
+      description: 'Optimize for low-latency responses',
+      transform: this.transformFast.bind(this)
+    });
+
+    this.registerCommand({
+      name: 'loop',
+      description: 'Enable iterative refinement cycles',
+      parameters: [
+        {
+          name: 'iterations',
+          description: 'Number of refinement iterations',
+          required: false,
+          default: 3
+        }
+      ],
+      transform: this.transformLoop.bind(this)
+    });
+
+    this.registerCommand({
+      name: 'reflect',
+      description: 'Trigger meta-analysis of outputs',
+      transform: this.transformReflect.bind(this)
+    });
+
+    this.registerCommand({
+      name: 'collapse',
+      description: 'Return to default behavior',
+      transform: this.transformCollapse.bind(this)
+    });
+
+    this.registerCommand({
+      name: 'fork',
+      description: 'Generate multiple alternative responses',
+      parameters: [
+        {
+          name: 'count',
+          description: 'Number of alternatives to generate',
+          required: false,
+          default: 2
+        }
+      ],
+      transform: this.transformFork.bind(this)
+    });
+  }
+
+  public registerCommand(command: SymbolicCommand) {
+    this.commands.set(command.name, command);
+    if (command.aliases) {
+      command.aliases.forEach(alias => {
+        this.aliasMap.set(alias, command.name);
+      });
+    }
+  }
+
+  public async generate(input: { prompt: string, systemPrompt?: string }): Promise<string> {
+    const { prompt, systemPrompt = '' } = input;
+    
+    // Parse command from prompt
+    const { command, cleanPrompt, parameters } = this.parseCommand(prompt);
+    
+    // Transform prompt based on command
+    const transformed = command 
+      ? await this.commands.get(command)?.transform(cleanPrompt, { 
+          systemPrompt, 
+          parameters,
+          options: this.options
+        })
+      : { systemPrompt, userPrompt: prompt };
+    
+    // Execute the transformed prompt with the provider's API
+    return this.executePrompt(transformed);
+  }
+
+  protected parseCommand(prompt: string): { command: string | null, cleanPrompt: string, parameters: Record<string, any> } {
+    const commandRegex = /^\/([a-zA-Z0-9_]+)(?:\s+([^\n]*))?/;
+    const match = prompt.match(commandRegex);
+    
+    if (!match) {
+      return { command: null, cleanPrompt: prompt, parameters: {} };
+    }
+    
+    const [fullMatch, command, rest] = match;
+    const commandName = this.aliasMap.get(command) || command;
+    
+    if (!this.commands.has(commandName)) {
+      return { command: null, cleanPrompt: prompt, parameters: {} };
+    }
+    
+    // Parse parameters if any
+    const parameters = this.parseParameters(commandName, rest || '');
+    const cleanPrompt = prompt.replace(fullMatch, '').trim();
+    
+    return { command: commandName, cleanPrompt, parameters };
+  }
+
+  protected parseParameters(command: string, paramString: string): Record<string, any> {
+    // Default simple implementation - override in specific adapters as needed
+    const params: Record<string, any> = {};
+    const cmd = this.commands.get(command);
+    
+    // If no parameters defined for command, return empty object
+    if (!cmd?.parameters || cmd.parameters.length === 0) {
+      return params;
+    }
+    
+    // Set defaults
+    cmd.parameters.forEach(param => {
+      if (param.default !== undefined) {
+        params[param.name] = param.default;
+      }
+    });
+    
+    // Simple parsing - can be enhanced for more complex parameter syntax
+    const paramRegex = /--([a-zA-Z0-9_]+)(?:=([^\s]+))?/g;
+    let match;
+    
+    while ((match = paramRegex.exec(paramString)) !== null) {
+      const [_, paramName, paramValue = true] = match;
+      params[paramName] = paramValue;
+    }
+    
+    return params;
+  }
+
+  /* 
+   * These transformation methods must be implemented by specific adapters
+   * to account for platform-specific behavior
+   */
+  protected abstract transformThink(prompt: string, options: any): Promise<TransformedPrompt>;
+  protected abstract transformFast(prompt: string, options: any): Promise<TransformedPrompt>;
+  protected abstract transformLoop(prompt: string, options: any): Promise<TransformedPrompt>;
+  protected abstract transformReflect(prompt: string, options: any): Promise<TransformedPrompt>;
+  protected abstract transformCollapse(prompt: string, options: any): Promise<TransformedPrompt>;
+  protected abstract transformFork(prompt: string, options: any): Promise<TransformedPrompt>;
+  
+  // Method to execute the transformed prompt with the provider's API
+  protected abstract executePrompt(transformed: TransformedPrompt): Promise<string>;
+}

src/adapters/claude.ts

@@ -0,0 +1,176 @@
+// universal-developer/src/adapters/claude.ts
+
+import { ModelAdapter, TransformedPrompt } from './base';
+import axios from 'axios';
+
+interface ClaudeOptions {
+  apiVersion?: string;
+  maxTokens?: number;
+  temperature?: number;
+  baseURL?: string;
+  model?: string;
+}
+
+export class ClaudeAdapter extends ModelAdapter {
+  private baseURL: string;
+  private model: string;
+  private maxTokens: number;
+  private temperature: number;
+
+  constructor(apiKey: string, options: ClaudeOptions = {}) {
+    super(apiKey, options);
+    
+    this.baseURL = options.baseURL || 'https://api.anthropic.com';
+    this.model = options.model || 'claude-3-opus-20240229';
+    this.maxTokens = options.maxTokens || 4096;
+    this.temperature = options.temperature || 0.7;
+  }
+
+  protected async transformThink(prompt: string, options: any): Promise<TransformedPrompt> {
+    // Claude has built-in thinking capabilities we can leverage
+    const systemPrompt = `${options.systemPrompt || ''}
+For this response, I'd like you to engage your deepest analytical capabilities. Please think step by step through this problem, considering multiple perspectives and potential approaches. Take your time to develop a comprehensive, nuanced understanding before providing your final answer.`;
+
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: Math.max(0.1, this.temperature - 0.2), // Slightly lower temperature for more deterministic thinking
+        enable_thinking: true
+      }
+    };
+  }
+
+  protected async transformFast(prompt: string, options: any): Promise<TransformedPrompt> {
+    const systemPrompt = `${options.systemPrompt || ''}
+Please provide a brief, direct response to this question. Focus on the most important information and keep your answer concise and to the point.`;
+
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: Math.min(1.0, this.temperature + 0.1), // Slightly higher temperature for more fluent responses
+        max_tokens: Math.min(this.maxTokens, 1024), // Limit token count for faster responses
+        enable_thinking: false
+      }
+    };
+  }
+
+  protected async transformLoop(prompt: string, options: any): Promise<TransformedPrompt> {
+    const iterations = options.parameters.iterations || 3;
+    
+    const systemPrompt = `${options.systemPrompt || ''}
+Please approach this task using an iterative process. Follow these steps:
+
+1. Develop an initial response to the prompt.
+2. Critically review your response, identifying areas for improvement.
+3. Create an improved version based on your critique.
+4. Repeat steps 2-3 for a total of ${iterations} iterations.
+5. Present your final response, which should reflect the accumulated improvements.
+
+Show all iterations in your response, clearly labeled.`;
+
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: this.temperature,
+        max_tokens: this.maxTokens
+      }
+    };
+  }
+
+  protected async transformReflect(prompt: string, options: any): Promise<TransformedPrompt> {
+    const systemPrompt = `${options.systemPrompt || ''}
+For this response, I'd like you to engage in two distinct phases:
+
+1. First, respond to the user's query directly.
+2. Then, reflect on your own response by considering:
+   - What assumptions did you make in your answer?
+   - What perspectives or viewpoints might be underrepresented?
+   - What limitations exist in your approach or knowledge?
+   - How might your response be improved or expanded?
+
+Clearly separate these two phases in your response.`;
+
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: Math.max(0.1, this.temperature - 0.1),
+        enable_thinking: true
+      }
+    };
+  }
+
+  protected async transformCollapse(prompt: string, options: any): Promise<TransformedPrompt> {
+    // Return to default behavior - use the original system prompt
+    return {
+      systemPrompt: options.systemPrompt || '',
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: this.temperature,
+        max_tokens: this.maxTokens,
+        enable_thinking: false
+      }
+    };
+  }
+
+  protected async transformFork(prompt: string, options: any): Promise<TransformedPrompt> {
+    const count = options.parameters.count || 2;
+    
+    const systemPrompt = `${options.systemPrompt || ''}
+Please provide ${count} distinct alternative responses to this prompt. These alternatives should represent fundamentally different approaches or perspectives, not minor variations. Label each alternative clearly.`;
+
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: Math.min(1.0, this.temperature + 0.2), // Higher temperature to encourage diversity
+        max_tokens: this.maxTokens
+      }
+    };
+  }
+
+  protected async executePrompt(transformed: TransformedPrompt): Promise<string> {
+    try {
+      const messages = [
+        // System message if provided
+        ...(transformed.systemPrompt ? [{
+          role: 'system',
+          content: transformed.systemPrompt
+        }] : []),
+        // User message
+        {
+          role: 'user',
+          content: transformed.userPrompt
+        }
+      ];
+
+      const response = await axios.post(
+        `${this.baseURL}/v1/messages`,
+        {
+          model: this.model,
+          messages,
+          max_tokens: transformed.modelParameters?.max_tokens || this.maxTokens,
+          temperature: transformed.modelParameters?.temperature || this.temperature,
+          ...('enable_thinking' in (transformed.modelParameters || {}) ? 
+              { enable_thinking: transformed.modelParameters?.enable_thinking } : 
+              {})
+        },
+        {
+          headers: {
+            'Content-Type': 'application/json',
+            'x-api-key': this.apiKey,
+            'anthropic-version': '2023-06-01'
+          }
+        }
+      );
+
+      return response.data.content[0].text;
+    } catch (error) {
+      console.error('Error executing Claude prompt:', error);
+      throw new Error(`Failed to execute Claude prompt: ${error.message}`);
+    }
+  }
+}

src/adapters/openai.ts

@@ -0,0 +1,191 @@
+// universal-developer/src/adapters/openai.ts
+
+import { ModelAdapter, TransformedPrompt } from './base';
+import axios from 'axios';
+
+interface OpenAIOptions {
+  apiVersion?: string;
+  maxTokens?: number;
+  temperature?: number;
+  baseURL?: string;
+  model?: string;
+}
+
+export class OpenAIAdapter extends ModelAdapter {
+  private baseURL: string;
+  private model: string;
+  private maxTokens: number;
+  private temperature: number;
+
+  constructor(apiKey: string, options: OpenAIOptions = {}) {
+    super(apiKey, options);
+    
+    this.baseURL = options.baseURL || 'https://api.openai.com';
+    this.model = options.model || 'gpt-4';
+    this.maxTokens = options.maxTokens || 4096;
+    this.temperature = options.temperature || 0.7;
+  }
+
+  protected async transformThink(prompt: string, options: any): Promise<TransformedPrompt> {
+    // For OpenAI, we'll use detailed system instructions to emulate thinking mode
+    const systemPrompt = `${options.systemPrompt || ''}
+When responding to this query, please use the following approach:
+1. Take a deep breath and think step-by-step about the problem
+2. Break down complex aspects into simpler components
+3. Consider multiple perspectives and approaches
+4. Identify potential misconceptions or errors in reasoning
+5. Synthesize your analysis into a comprehensive response
+6. Structure your thinking process visibly with clear sections:
+   a. Initial Analysis
+   b. Detailed Exploration
+   c. Synthesis and Conclusion`;
+
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: Math.max(0.1, this.temperature - 0.2),
+        max_tokens: this.maxTokens
+      }
+    };
+  }
+
+  protected async transformFast(prompt: string, options: any): Promise<TransformedPrompt> {
+    const systemPrompt = `${options.systemPrompt || ''}
+Please provide a concise, direct response. Focus only on the most essential information needed to answer the query. Keep explanations minimal and prioritize brevity over comprehensiveness.`;
+
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: Math.min(1.0, this.temperature + 0.1),
+        max_tokens: Math.min(this.maxTokens, 1024),
+        presence_penalty: 1.0, // Encourage brevity by penalizing repetition
+        frequency_penalty: 1.0
+      }
+    };
+  }
+
+  protected async transformLoop(prompt: string, options: any): Promise<TransformedPrompt> {
+    const iterations = options.parameters.iterations || 3;
+    
+    const systemPrompt = `${options.systemPrompt || ''}
+Please approach this task using an iterative refinement process with ${iterations} cycles:
+
+1. Initial Version: Create your first response to the query
+2. Critical Review: Analyze the strengths and weaknesses of your response
+3. Improved Version: Create an enhanced version addressing the identified issues
+4. Repeat steps 2-3 for each iteration
+5. Final Version: Provide your most refined response
+
+Clearly label each iteration (e.g., "Iteration 1", "Critique 1", etc.) in your response.`;
+
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: this.temperature,
+        max_tokens: this.maxTokens
+      }
+    };
+  }
+
+  protected async transformReflect(prompt: string, options: any): Promise<TransformedPrompt> {
+    const systemPrompt = `${options.systemPrompt || ''}
+For this query, please structure your response in two distinct parts:
+
+PART 1: DIRECT RESPONSE
+Provide your primary answer to the user's query.
+
+PART 2: META-REFLECTION
+Then, engage in critical reflection on your own response by addressing:
+- What assumptions did you make in your answer?
+- What alternative perspectives might be valid?
+- What are the limitations of your response?
+- How might your response be improved?
+- What cognitive biases might have influenced your thinking?
+
+Make sure both parts are clearly labeled and distinguishable.`;
+
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: Math.max(0.1, this.temperature - 0.1),
+        max_tokens: this.maxTokens
+      }
+    };
+  }
+
+  protected async transformCollapse(prompt: string, options: any): Promise<TransformedPrompt> {
+    // Return to default behavior
+    return {
+      systemPrompt: options.systemPrompt || '',
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: this.temperature,
+        max_tokens: this.maxTokens
+      }
+    };
+  }
+
+  protected async transformFork(prompt: string, options: any): Promise<TransformedPrompt> {
+    const count = options.parameters.count || 2;
+    
+    const systemPrompt = `${options.systemPrompt || ''}
+Please provide ${count} substantively different responses to this prompt. Each alternative should represent a different approach, perspective, or framework. Clearly label each alternative (e.g., "Alternative 1", "Alternative 2", etc.).`;
+
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: Math.min(1.0, this.temperature + 0.2), // Higher temperature for diversity
+        max_tokens: this.maxTokens
+      }
+    };
+  }
+
+  protected async executePrompt(transformed: TransformedPrompt): Promise<string> {
+    try {
+      const messages = [
+        // System message if provided
+        ...(transformed.systemPrompt ? [{
+          role: 'system',
+          content: transformed.systemPrompt
+        }] : []),
+        // User message
+        {
+          role: 'user',
+          content: transformed.userPrompt
+        }
+      ];
+
+      const response = await axios.post(
+        `${this.baseURL}/v1/chat/completions`,
+        {
+          model: this.model,
+          messages,
+          max_tokens: transformed.modelParameters?.max_tokens || this.maxTokens,
+          temperature: transformed.modelParameters?.temperature || this.temperature,
+          ...(transformed.modelParameters?.presence_penalty !== undefined ? 
+              { presence_penalty: transformed.modelParameters.presence_penalty } : 
+              {}),
+          ...(transformed.modelParameters?.frequency_penalty !== undefined ? 
+              { frequency_penalty: transformed.modelParameters.frequency_penalty } : 
+              {})
+        },
+        {
+          headers: {
+            'Content-Type': 'application/json',
+            'Authorization': `Bearer ${this.apiKey}`
+          }
+        }
+      );
+
+      return response.data.choices[0].message.content;
+    } catch (error) {
+      console.error('Error executing OpenAI prompt:', error);
+      throw new Error(`Failed to execute OpenAI prompt: ${error.message}`);
+    }
+  }
+}

src/adapters/qwen.ts

@@ -0,0 +1,175 @@
+// universal-developer/src/adapters/qwen.ts
+
+import { ModelAdapter, TransformedPrompt } from './base';
+import axios from 'axios';
+
+interface QwenOptions {
+  apiVersion?: string;
+  maxTokens?: number;
+  temperature?: number;
+  baseURL?: string;
+  model?: string;
+}
+
+export class QwenAdapter extends ModelAdapter {
+  private baseURL: string;
+  private model: string;
+  private maxTokens: number;
+  private temperature: number;
+
+  constructor(apiKey: string, options: QwenOptions = {}) {
+    super(apiKey, options);
+    
+    this.baseURL = options.baseURL || 'https://api.qwen.ai';
+    this.model = options.model || 'qwen3-30b-a3b';
+    this.maxTokens = options.maxTokens || 4096;
+    this.temperature = options.temperature || 0.7;
+  }
+
+  protected async transformThink(prompt: string, options: any): Promise<TransformedPrompt> {
+    // Leverage Qwen3's native thinking mode
+    return {
+      systemPrompt: options.systemPrompt || '',
+      userPrompt: prompt.trim().endsWith('/think') ? prompt : `${prompt} /think`,
+      modelParameters: {
+        temperature: Math.max(0.1, this.temperature - 0.2),
+        enable_thinking: true
+      }
+    };
+  }
+
+  protected async transformFast(prompt: string, options: any): Promise<TransformedPrompt> {
+    // Disable thinking mode for fast responses
+    const systemPrompt = `${options.systemPrompt || ''}
+Provide brief, direct responses. Focus on essential information only.`;
+
+    return {
+      systemPrompt,
+      userPrompt: prompt.trim().endsWith('/no_think') ? prompt : `${prompt} /no_think`,
+      modelParameters: {
+        temperature: Math.min(1.0, this.temperature + 0.1),
+        max_tokens: Math.min(this.maxTokens, 1024),
+        enable_thinking: false
+      }
+    };
+  }
+
+  protected async transformLoop(prompt: string, options: any): Promise<TransformedPrompt> {
+    const iterations = options.parameters.iterations || 3;
+    
+    const systemPrompt = `${options.systemPrompt || ''}
+Please use an iterative approach with ${iterations} refinement cycles:
+1. Initial response
+2. Critical review
+3. Improvement
+4. Repeat steps 2-3 for a total of ${iterations} iterations
+5. Present your final response with all iterations clearly labeled`;
+
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: this.temperature,
+        enable_thinking: true // Use thinking mode for deeper refinement
+      }
+    };
+  }
+
+  protected async transformReflect(prompt: string, options: any): Promise<TransformedPrompt> {
+    const systemPrompt = `${options.systemPrompt || ''}
+For this response, please:
+1. Answer the query directly
+2. Then reflect on your answer by analyzing:
+   - Assumptions made
+   - Alternative perspectives
+   - Limitations in your approach
+   - Potential improvements`;
+
+    return {
+      systemPrompt,
+      userPrompt: `${prompt} /think`, // Use native thinking for reflection
+      modelParameters: {
+        temperature: Math.max(0.1, this.temperature - 0.1),
+        enable_thinking: true
+      }
+    };
+  }
+
+  protected async transformCollapse(prompt: string, options: any): Promise<TransformedPrompt> {
+    // Return to default behavior
+    return {
+      systemPrompt: options.systemPrompt || '',
+      userPrompt: `${prompt} /no_think`, // Explicitly disable thinking
+      modelParameters: {
+        temperature: this.temperature,
+        max_tokens: this.maxTokens,
+        enable_thinking: false
+      }
+    };
+  }
+
+  protected async transformFork(prompt: string, options: any): Promise<TransformedPrompt> {
+    const count = options.parameters.count || 2;
+    
+    const systemPrompt = `${options.systemPrompt || ''}
+Please provide ${count} distinct alternative responses to this prompt, representing different approaches or perspectives. Label each alternative clearly.`;
+
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        temperature: Math.min(1.0, this.temperature + 0.2),
+        max_tokens: this.maxTokens,
+        enable_thinking: true // Use thinking for more creative alternatives
+      }
+    };
+  }
+
+  protected async executePrompt(transformed: TransformedPrompt): Promise<string> {
+    try {
+      const messages = [
+        // System message if provided
+        ...(transformed.systemPrompt ? [{
+          role: 'system',
+          content: transformed.systemPrompt
+        }] : []),
+        // User message
+        {
+          role: 'user',
+          content: transformed.userPrompt
+        }
+      ];
+
+      const response = await axios.post(
+        `${this.baseURL}/v1/chat/completions`,
+        {
+          model: this.model,
+          messages,
+          max_tokens: transformed.modelParameters?.max_tokens || this.maxTokens,
+          temperature: transformed.modelParameters?.temperature || this.temperature,
+          ...('enable_thinking' in (transformed.modelParameters || {}) ? 
+              { enable_thinking: transformed.modelParameters?.enable_thinking } : 
+              {})
+        },
+        {
+          headers: {
+            'Content-Type': 'application/json',
+            'Authorization': `Bearer ${this.apiKey}`
+          }
+        }
+      );
+
+      // Extract thinking content if available
+      let content = '';
+      if (response.data.thinking_content) {
+        content = `<thinking>\n${response.data.thinking_content}\n</thinking>\n\n`;
+      }
+      content += response.data.choices[0].message.content;
+
+      return content;
+    } catch (error) {
+      console.error('Error executing Qwen prompt:', error);
+      throw new Error(`Failed to execute Qwen prompt: ${error.message}`);
+    }
+  }
+}

src/cli.ts

@@ -0,0 +1,399 @@
+#!/usr/bin/env node
+
+// universal-developer/src/cli.ts
+
+import { program } from 'commander';
+import { UniversalLLM } from './index';
+import * as fs from 'fs';
+import * as path from 'path';
+import chalk from 'chalk';
+import ora from 'ora';
+import * as dotenv from 'dotenv';
+import * as os from 'os';
+import * as readline from 'readline';
+import { createSpinner } from 'nanospinner';
+
+// Load environment variables
+dotenv.config();
+
+// Load package.json for version info
+const packageJson = JSON.parse(
+  fs.readFileSync(path.resolve(__dirname, '../package.json'), 'utf-8')
+);
+
+// Check for config file in user's home directory
+const configDir = path.join(os.homedir(), '.universal-developer');
+const configPath = path.join(configDir, 'config.json');
+let config: any = {
+  defaultProvider: 'anthropic',
+  enableTelemetry: true,
+  apiKeys: {}
+};
+
+// Create config directory if it doesn't exist
+if (!fs.existsSync(configDir)) {
+  fs.mkdirSync(configDir, { recursive: true });
+}
+
+// Load config if it exists
+if (fs.existsSync(configPath)) {
+  try {
+    config = JSON.parse(fs.readFileSync(configPath, 'utf-8'));
+  } catch (error) {
+    console.error('Error loading config file:', error);
+  }
+}
+
+// Save config function
+function saveConfig() {
+  try {
+    fs.writeFileSync(configPath, JSON.stringify(config, null, 2));
+  } catch (error) {
+    console.error('Error saving config:', error);
+  }
+}
+
+// Configure CLI
+program
+  .name('ud')
+  .description('Universal Developer CLI - Control LLMs with symbolic runtime commands')
+  .version(packageJson.version);
+
+// Configure command
+program
+  .command('config')
+  .description('Configure Universal Developer CLI')
+  .option('-p, --provider <provider>', 'Set default provider (anthropic, openai, qwen, gemini, ollama)')
+  .option('-k, --key <key>', 'Set API key for the default provider')
+  .option('--anthropic-key <key>', 'Set API key for Anthropic/Claude')
+  .option('--openai-key <key>', 'Set API key for OpenAI')
+  .option('--qwen-key <key>', 'Set API key for Qwen')
+  .option('--gemini-key <key>', 'Set API key for Google Gemini')
+  .option('--telemetry <boolean>', 'Enable or disable anonymous telemetry')
+  .option('-l, --list', 'List current configuration')
+  .action((options) => {
+    if (options.list) {
+      console.log(chalk.bold('\nCurrent Configuration:'));
+      console.log(`Default Provider: ${chalk.green(config.defaultProvider)}`);
+      console.log(`Telemetry: ${config.enableTelemetry ? chalk.green('Enabled') : chalk.yellow('Disabled')}`);
+      console.log('\nAPI Keys:');
+      for (const [provider, key] of Object.entries(config.apiKeys)) {
+        console.log(`${provider}: ${key ? chalk.green('Configured') : chalk.red('Not configured')}`);
+      }
+      return;
+    }
+
+    let changed = false;
+
+    if (options.provider) {
+      const validProviders = ['anthropic', 'openai', 'qwen', 'gemini', 'ollama'];
+      if (validProviders.includes(options.provider)) {
+        config.defaultProvider = options.provider;
+        changed = true;
+        console.log(`Default provider set to ${chalk.green(options.provider)}`);
+      } else {
+        console.error(`Invalid provider: ${options.provider}. Valid options are: ${validProviders.join(', ')}`);
+      }
+    }
+
+    if (options.key) {
+      if (!config.apiKeys) config.apiKeys = {};
+      config.apiKeys[config.defaultProvider] = options.key;
+      changed = true;
+      console.log(`API key for ${chalk.green(config.defaultProvider)} has been set`);
+    }
+
+    // Provider-specific keys
+    const providerKeys = {
+      'anthropic': options.anthropicKey,
+      'openai': options.openaiKey,
+      'qwen': options.qwenKey,
+      'gemini': options.geminiKey
+    };
+
+    for (const [provider, key] of Object.entries(providerKeys)) {
+      if (key) {
+        if (!config.apiKeys) config.apiKeys = {};
+        config.apiKeys[provider] = key;
+        changed = true;
+        console.log(`API key for ${chalk.green(provider)} has been set`);
+      }
+    }
+
+    if (options.telemetry !== undefined) {
+      const enableTelemetry = options.telemetry === 'true';
+      config.enableTelemetry = enableTelemetry;
+      changed = true;
+      console.log(`Telemetry ${enableTelemetry ? chalk.green('enabled') : chalk.yellow('disabled')}`);
+    }
+
+    if (changed) {
+      saveConfig();
+      console.log(chalk.bold('\nConfiguration saved!'));
+    } else {
+      console.log('No changes made. Use --help to see available options.');
+    }
+  });
+
+// Helper function to handle piped input
+async function getPipedInput(): Promise<string | null> {
+  if (process.stdin.isTTY) {
+    return null;
+  }
+
+  return new Promise((resolve) => {
+    let data = '';
+    process.stdin.on('readable', () => {
+      const chunk = process.stdin.read();
+      if (chunk !== null) {
+        data += chunk;
+      }
+    });
+
+    process.stdin.on('end', () => {
+      resolve(data);
+    });
+  });
+}
+
+// Helper to get API key for a provider
+function getApiKey(provider: string): string {
+  // First check config
+  if (config.apiKeys && config.apiKeys[provider]) {
+    return config.apiKeys[provider];
+  }
+
+  // Then check environment variables
+  const envVarName = `${provider.toUpperCase()}_API_KEY`;
+  const apiKey = process.env[envVarName];
+  
+  if (!apiKey) {
+    console.error(chalk.red(`Error: No API key found for ${provider}.`));
+    console.log(`Please set your API key using: ud config --${provider}-key <your-api-key>`);
+    console.log(`Or set the ${envVarName} environment variable.`);
+    process.exit(1);
+  }
+
+  return apiKey;
+}
+
+// Interactive mode
+program
+  .command('interactive')
+  .alias('i')
+  .description('Start an interactive session')
+  .option('-p, --provider <provider>', 'LLM provider to use')
+  .option('-m, --model <model>', 'Model to use')
+  .action(async (options) => {
+    const provider = options.provider || config.defaultProvider;
+    const apiKey = getApiKey(provider);
+    
+    const llm = new UniversalLLM({
+      provider,
+      apiKey,
+      model: options.model,
+      telemetryEnabled: config.enableTelemetry
+    });
+
+    console.log(chalk.bold('\nUniversal Developer Interactive Mode'));
+    console.log(chalk.dim(`Using provider: ${provider}`));
+    console.log(chalk.dim('Type /exit or Ctrl+C to quit'));
+    console.log(chalk.dim('Available commands: /think, /fast, /loop, /reflect, /fork, /collapse\n'));
+    
+    const rl = readline.createInterface({
+      input: process.stdin,
+      output: process.stdout
+    });
+
+    let conversationHistory: { role: string, content: string }[] = [];
+    
+    const promptUser = () => {
+      rl.question('> ', async (input) => {
+        if (input.toLowerCase() === '/exit') {
+          rl.close();
+          return;
+        }
+
+        // Store user message
+        conversationHistory.push({
+          role: 'user',
+          content: input
+        });
+
+        const spinner = createSpinner('Generating response...').start();
+        
+        try {
+          const response = await llm.generate({
+            messages: conversationHistory
+          });
+          
+          spinner.success();
+          console.log(`\n${chalk.blue('Assistant:')} ${response}\n`);
+          
+          // Store assistant response
+          conversationHistory.push({
+            role: 'assistant',
+            content: response
+          });
+        } catch (error) {
+          spinner.error();
+          console.error(`Error: ${error.message}`);
+        }
+        
+        promptUser();
+      });
+    };
+    
+    console.log(chalk.blue('Assistant:') + ' Hello! How can I help you today?\n');
+    conversationHistory.push({
+      role: 'assistant',
+      content: 'Hello! How can I help you today?'
+    });
+    
+    promptUser();
+  });
+
+// Command for each symbolic operation
+const symbolicCommands = [
+  { name: 'think', description: 'Generate response using deep reasoning' },
+  { name: 'fast', description: 'Generate quick, concise response' },
+  { name: 'loop', description: 'Generate iteratively refined response' },
+  { name: 'reflect', description: 'Generate response with self-reflection' },
+  { name: 'fork', description: 'Generate multiple alternative responses' },
+  { name: 'collapse', description: 'Generate response using default behavior' }
+];
+
+symbolicCommands.forEach(cmd => {
+  program
+    .command(cmd.name)
+    .description(cmd.description)
+    .argument('[prompt]', 'The prompt to send to the LLM')
+    .option('-p, --provider <provider>', 'LLM provider to use')
+    .option('-m, --model <model>', 'Model to use')
+    .option('-s, --system <prompt>', 'System prompt to use')
+    .option('-i, --iterations <number>', 'Number of iterations (for loop command)')
+    .option('-c, --count <number>', 'Number of alternatives (for fork command)')
+    .action(async (promptArg, options) => {
+      // Get provider from options or config
+      const provider = options.provider || config.defaultProvider;
+      const apiKey = getApiKey(provider);
+
+      // Initialize LLM
+      const llm = new UniversalLLM({
+        provider,
+        apiKey,
+        model: options.model,
+        telemetryEnabled: config.enableTelemetry
+      });
+
+      // Check for piped input
+      const pipedInput = await getPipedInput();
+      
+      // Combine prompt argument and piped input
+      let prompt = promptArg || '';
+      if (pipedInput) {
+        prompt = prompt ? `${prompt}\n\n${pipedInput}` : pipedInput;
+      }
+      
+      // If no prompt provided, show help
+      if (!prompt) {
+        console.error('Error: Prompt is required.');
+        console.log(`Usage: ud ${cmd.name} "Your prompt here"`);
+        console.log('Or pipe content: cat file.txt | ud ${cmd.name}');
+        process.exit(1);
+      }
+
+      // Build command string
+      let commandString = `/${cmd.name}`;
+      
+      // Add command-specific parameters
+      if (cmd.name === 'loop' && options.iterations) {
+        commandString += ` --iterations=${options.iterations}`;
+      } else if (cmd.name === 'fork' && options.count) {
+        commandString += ` --count=${options.count}`;
+      }
+      
+      // Add the prompt
+      const fullPrompt = `${commandString} ${prompt}`;
+      
+      // Show what's happening
+      console.log(chalk.dim(`Using provider: ${provider}`));
+      const spinner = createSpinner('Generating response...').start();
+      
+      try {
+        const response = await llm.generate({
+          systemPrompt: options.system,
+          prompt: fullPrompt
+        });
+        
+        spinner.success();
+        console.log('\n' + response + '\n');
+      } catch (error) {
+        spinner.error();
+        console.error(`Error: ${error.message}`);
+        process.exit(1);
+      }
+    });
+});
+
+// Default command (no subcommand specified)
+program
+  .arguments('[prompt]')
+  .option('-p, --provider <provider>', 'LLM provider to use')
+  .option('-m, --model <model>', 'Model to use')
+  .option('-s, --system <prompt>', 'System prompt to use')
+  .option('-c, --command <command>', 'Symbolic command to use')
+  .action(async (promptArg, options) => {
+    if (!promptArg && !process.stdin.isTTY) {
+      // No prompt argument but has piped input
+      const pipedInput = await getPipedInput();
+      if (pipedInput) {
+        promptArg = pipedInput;
+      }
+    }
+
+    if (!promptArg) {
+      // No prompt provided, show interactive mode
+      program.commands.find(cmd => cmd.name() === 'interactive').action(options);
+      return;
+    }
+
+    // Get provider from options or config
+    const provider = options.provider || config.defaultProvider;
+    const apiKey = getApiKey(provider);
+
+    // Initialize LLM
+    const llm = new UniversalLLM({
+      provider,
+      apiKey,
+      model: options.model,
+      telemetryEnabled: config.enableTelemetry
+    });
+
+    // Default to think command if none specified
+    const command = options.command || 'think';
+    
+    // Format prompt with command
+    const fullPrompt = `/${command} ${promptArg}`;
+    
+    // Show what's happening
+    console.log(chalk.dim(`Using provider: ${provider}`));
+    const spinner = createSpinner('Generating response...').start();
+    
+    try {
+      const response = await llm.generate({
+        systemPrompt: options.system,
+        prompt: fullPrompt
+      });
+      
+      spinner.success();
+      console.log('\n' + response + '\n');
+    } catch (error) {
+      spinner.error();
+      console.error(`Error: ${error.message}`);
+      process.exit(1);
+    }
+  });
+
+// Parse arguments
+program.parse();

src/extension.ts

@@ -0,0 +1,1419 @@
+// universal-developer-vscode/src/extension.ts
+
+import * as vscode from 'vscode';
+
+/**
+ * Symbolic command definition
+ */
+interface SymbolicCommand {
+  name: string;
+  description: string;
+  parameters?: {
+    name: string;
+    description: string;
+    required?: boolean;
+    default?: any;
+  }[];
+  examples: string[];
+  provider?: {
+    claude?: boolean;
+    openai?: boolean;
+    qwen?: boolean;
+    gemini?: boolean;
+    ollama?: boolean;
+  };
+}
+
+/**
+ * Universal developer extension activation function
+ */
+export function activate(context: vscode.ExtensionContext) {
+  console.log('Universal Developer extension is now active');
+
+  // Register command palette commands
+  const insertSymbolicCommand = vscode.commands.registerCommand(
+    'universal-developer.insertSymbolicCommand',
+    async () => {
+      const commandName = await showSymbolicCommandQuickPick();
+      if (!commandName) return;
+
+      const command = SYMBOLIC_COMMANDS.find(cmd => cmd.name === commandName);
+      if (!command) return;
+
+      // Check if command has parameters
+      let commandString = `/${command.name}`;
+      
+      if (command.parameters && command.parameters.length > 0) {
+        const parameters = await collectCommandParameters(command);
+        if (parameters) {
+          Object.entries(parameters).forEach(([key, value]) => {
+            if (value !== undefined && value !== null && value !== '') {
+              commandString += ` --${key}=${value}`;
+            }
+          });
+        }
+      }
+
+      // Insert command at cursor position
+      const editor = vscode.window.activeTextEditor;
+      if (editor) {
+        editor.edit(editBuilder => {
+          editBuilder.insert(editor.selection.active, commandString + ' ');
+        });
+      }
+    }
+  );
+
+  // Register the symbolic command chain builder
+  const buildSymbolicChain = vscode.commands.registerCommand(
+    'universal-developer.buildSymbolicChain', 
+    async () => {
+      await showCommandChainBuilder();
+    }
+  );
+
+  // Register symbolic command hover provider
+  const hoverProvider = vscode.languages.registerHoverProvider(
+    ['javascript', 'typescript', 'python', 'markdown', 'plaintext'],
+    {
+      provideHover(document, position, token) {
+        const range = document.getWordRangeAtPosition(position, /\/[a-zA-Z0-9_]+/);
+        if (!range) return;
+
+        const commandText = document.getText(range);
+        const commandName = commandText.substring(1); // Remove the leading /
+        
+        const command = SYMBOLIC_COMMANDS.find(cmd => cmd.name === commandName);
+        if (!command) return;
+
+        // Create hover markdown
+        const hoverContent = new vscode.MarkdownString();
+        hoverContent.appendMarkdown(`**/${command.name}**\n\n`);
+        hoverContent.appendMarkdown(`${command.description}\n\n`);
+        
+        if (command.parameters && command.parameters.length > 0) {
+          hoverContent.appendMarkdown('**Parameters:**\n\n');
+          command.parameters.forEach(param => {
+            const required = param.required ? ' (required)' : '';
+            const defaultValue = param.default !== undefined ? ` (default: ${param.default})` : '';
+            hoverContent.appendMarkdown(`- \`--${param.name}\`${required}${defaultValue}: ${param.description}\n`);
+          });
+          hoverContent.appendMarkdown('\n');
+        }
+
+        if (command.examples && command.examples.length > 0) {
+          hoverContent.appendMarkdown('**Examples:**\n\n');
+          command.examples.forEach(example => {
+            hoverContent.appendCodeBlock(example, 'markdown');
+          });
+        }
+
+        // Show provider compatibility
+        if (command.provider) {
+          hoverContent.appendMarkdown('\n**Compatible with:**\n\n');
+          const supported = Object.entries(command.provider)
+            .filter(([_, isSupported]) => isSupported)
+            .map(([provider]) => provider);
+          hoverContent.appendMarkdown(supported.join(', '));
+        }
+
+        return new vscode.Hover(hoverContent, range);
+      }
+    }
+  );
+
+  // Register completion provider for symbolic commands
+  const completionProvider = vscode.languages.registerCompletionItemProvider(
+    ['javascript', 'typescript', 'python', 'markdown', 'plaintext'],
+    {
+      provideCompletionItems(document, position) {
+        const linePrefix = document.lineAt(position).text.substring(0, position.character);
+        
+        // Check if we're at the start of a potential symbolic command
+        if (!linePrefix.endsWith('/')) {
+          return undefined;
+        }
+
+        const completionItems = SYMBOLIC_COMMANDS.map(command => {
+          const item = new vscode.CompletionItem(
+            command.name,
+            vscode.CompletionItemKind.Keyword
+          );
+          item.insertText = command.name;
+          item.detail = command.description;
+          item.documentation = new vscode.MarkdownString(command.description);
+          return item;
+        });
+
+        return completionItems;
+      }
+    },
+    '/' // Only trigger after the / character
+  );
+
+  // Register parameter completion provider
+  const parameterCompletionProvider = vscode.languages.registerCompletionItemProvider(
+    ['javascript', 'typescript', 'python', 'markdown', 'plaintext'],
+    {
+      provideCompletionItems(document, position) {
+        const linePrefix = document.lineAt(position).text.substring(0, position.character);
+        
+        // Match a symbolic command with a potential parameter start
+        const commandMatch = linePrefix.match(/\/([a-zA-Z0-9_]+)(?:\s+(?:[^\s]+\s+)*)?--$/);
+        if (!commandMatch) {
+          return undefined;
+        }
+
+        const commandName = commandMatch[1];
+        const command = SYMBOLIC_COMMANDS.find(cmd => cmd.name === commandName);
+        
+        if (!command || !command.parameters || command.parameters.length === 0) {
+          return undefined;
+        }
+
+        // Offer parameter completions
+        const completionItems = command.parameters.map(param => {
+          const item = new vscode.CompletionItem(
+            param.name,
+            vscode.CompletionItemKind.Property
+          );
+          item.insertText = `${param.name}=`;
+          item.detail = param.description;
+          
+          if (param.default !== undefined) {
+            item.documentation = new vscode.MarkdownString(
+              `${param.description}\n\nDefault: \`${param.default}\``
+            );
+          } else {
+            item.documentation = new vscode.MarkdownString(param.description);
+          }
+          
+          return item;
+        });
+
+        return completionItems;
+      }
+    },
+    '-' // Trigger after - (the second dash in --)
+  );
+
+  // Register code actions provider for symbolic command suggestions
+  const codeActionsProvider = vscode.languages.registerCodeActionsProvider(
+    ['javascript', 'typescript', 'python'],
+    {
+      provideCodeActions(document, range, context, token) {
+        // Check if there's any LLM API call in the current line
+        const line = document.lineAt(range.start.line).text;
+        
+        const llmApiPatterns = [
+          /\.generate\(\s*{/,              // UniversalLLM.generate()
+          /\.createCompletion\(/,          // OpenAI
+          /\.createChatCompletion\(/,      // OpenAI
+          /\.chat\.completions\.create\(/,  // OpenAI v2
+          /\.messages\.create\(/,           // Anthropic/Claude
+          /\.generateContent\(/            // Google Gemini
+        ];
+
+        if (!llmApiPatterns.some(pattern => pattern.test(line))) {
+          return;
+        }
+
+        // Create code actions for adding symbolic commands
+        const actions: vscode.CodeAction[] = [];
+        
+        // Find the prompt parameter
+        const promptMatch = line.match(/(prompt|messages|content)\s*:/);
+        if (!promptMatch) return;
+
+        // Add actions for common symbolic commands
+        ['think', 'fast', 'reflect', 'loop'].forEach(commandName => {
+          const command = SYMBOLIC_COMMANDS.find(cmd => cmd.name === commandName);
+          if (!command) return;
+          
+          const action = new vscode.CodeAction(
+            `Add /${commandName} command`,
+            vscode.CodeActionKind.RefactorRewrite
+          );
+          
+          action.command = {
+            title: `Insert /${commandName}`,
+            command: 'universal-developer.insertSymbolicCommandAtPrompt',
+            arguments: [range.start.line, command]
+          };
+          
+          actions.push(action);
+        });
+
+        return actions;
+      }
+    }
+  );
+
+  // Register command to insert symbolic command at prompt
+  const insertSymbolicCommandAtPrompt = vscode.commands.registerCommand(
+    'universal-developer.insertSymbolicCommandAtPrompt',
+    async (line: number, command: SymbolicCommand) => {
+      const editor = vscode.window.activeTextEditor;
+      if (!editor) return;
+      
+      const document = editor.document;
+      const lineText = document.lineAt(line).text;
+      
+      // Find where the prompt string starts
+      const promptMatch = lineText.match(/(prompt|messages|content)\s*:\s*['"]/);
+      if (!promptMatch) return;
+      
+      const promptStartIdx = promptMatch.index! + promptMatch[0].length;
+      const position = new vscode.Position(line, promptStartIdx);
+      
+      editor.edit(editBuilder => {
+        editBuilder.insert(position, `/${command.name} `);
+      });
+    }
+  );
+
+  // Register status bar item for active symbolic context
+  const statusBarItem = vscode.window.createStatusBarItem(
+    vscode.StatusBarAlignment.Right,
+    100
+  );
+  statusBarItem.text = "$(symbol-keyword) Symbolic";
+  statusBarItem.tooltip = "Universal Developer: Click to insert symbolic command";
+  statusBarItem.command = 'universal-developer.insertSymbolicCommand';
+  statusBarItem.show();
+
+  // Register documentation webview
+  const showDocumentation = vscode.commands.registerCommand(
+    'universal-developer.showDocumentation',
+    () => {
+      const panel = vscode.window.createWebviewPanel(
+        'universalDeveloperDocs',
+        'Universal Developer Documentation',
+        vscode.ViewColumn.One,
+        { enableScripts: true }
+      );
+      
+      panel.webview.html = getDocumentationHtml();
+    }
+  );
+
+  // Register commands for the extension
+  context.subscriptions.push(
+    insertSymbolicCommand,
+    buildSymbolicChain,
+    hoverProvider,
+    completionProvider,
+    parameterCompletionProvider,
+    codeActionsProvider,
+    insertSymbolicCommandAtPrompt,
+    statusBarItem,
+    showDocumentation
+  );
+
+  // Telemetry for command usage (anonymized)
+  context.subscriptions.push(
+    vscode.commands.registerCommand(
+      'universal-developer.trackCommandUsage',
+      (commandName: string) => {
+        // Only track if user has opted in to telemetry
+        const config = vscode.workspace.getConfiguration('universal-developer');
+        if (config.get('enableTelemetry', true)) {
+          sendAnonymizedTelemetry('command_used', { command: commandName });
+        }
+      }
+    )
+  );
+}
+
+// Helper function to show a quick pick for symbolic commands
+async function showSymbolicCommandQuickPick(): Promise<string | undefined> {
+  const items = SYMBOLIC_COMMANDS.map(command => ({
+    label: `/${command.name}`,
+    description: command.description,
+    detail: command.parameters && command.parameters.length > 0 
+      ? `Parameters: ${command.parameters.map(p => p.name).join(', ')}` 
+      : undefined
+  }));
+
+  const selected = await vscode
+// universal-developer-vscode/src/extension.ts (continued)
+
+// Helper function to show a quick pick for symbolic commands
+async function showSymbolicCommandQuickPick(): Promise<string | undefined> {
+  const items = SYMBOLIC_COMMANDS.map(command => ({
+    label: `/${command.name}`,
+    description: command.description,
+    detail: command.parameters && command.parameters.length > 0 
+      ? `Parameters: ${command.parameters.map(p => p.name).join(', ')}` 
+      : undefined
+  }));
+
+  const selected = await vscode.window.showQuickPick(items, {
+    placeHolder: 'Select a symbolic runtime command',
+  });
+
+  return selected ? selected.label.substring(1) : undefined; // Remove the leading /
+}
+
+// Helper function to collect parameters for a command
+async function collectCommandParameters(command: SymbolicCommand): Promise<Record<string, any> | undefined> {
+  if (!command.parameters || command.parameters.length === 0) {
+    return {};
+  }
+
+  const parameters: Record<string, any> = {};
+  
+  // Set default values
+  command.parameters.forEach(param => {
+    if (param.default !== undefined) {
+      parameters[param.name] = param.default;
+    }
+  });
+
+  // Ask for each parameter
+  for (const param of command.parameters) {
+    const value = await vscode.window.showInputBox({
+      prompt: param.description,
+      placeHolder: param.default !== undefined ? `Default: ${param.default}` : undefined,
+      ignoreFocusOut: true,
+      validateInput: text => {
+        if (param.required && !text) {
+          return `${param.name} is required`;
+        }
+        return null;
+      }
+    });
+
+    // User canceled
+    if (value === undefined) {
+      return undefined;
+    }
+
+    // Only set if value is provided
+    if (value !== '') {
+      parameters[param.name] = value;
+    }
+  }
+
+  return parameters;
+}
+
+// Command Chain Builder Interface
+async function showCommandChainBuilder() {
+  // Create webview panel for the command chain builder
+  const panel = vscode.window.createWebviewPanel(
+    'universalDeveloperChainBuilder',
+    'Symbolic Command Chain Builder',
+    vscode.ViewColumn.Two,
+    {
+      enableScripts: true,
+      retainContextWhenHidden: true
+    }
+  );
+
+  // Load chain builder HTML
+  panel.webview.html = getCommandChainBuilderHtml();
+
+  // Handle messages from webview
+  panel.webview.onDidReceiveMessage(
+    message => {
+      switch (message.command) {
+        case 'insertCommandChain':
+          const editor = vscode.window.activeTextEditor;
+          if (editor) {
+            editor.edit(editBuilder => {
+              editBuilder.insert(editor.selection.active, message.commandChain);
+            });
+          }
+          break;
+        case 'getCommandInfo':
+          panel.webview.postMessage({
+            command: 'commandInfo',
+            commands: SYMBOLIC_COMMANDS
+          });
+          break;
+      }
+    },
+    undefined,
+    []
+  );
+}
+
+// Get HTML for the command chain builder webview
+function getCommandChainBuilderHtml() {
+  return `<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>Symbolic Command Chain Builder</title>
+  <style>
+    body {
+      font-family: var(--vscode-font-family);
+      padding: 20px;
+      color: var(--vscode-foreground);
+      background-color: var(--vscode-editor-background);
+    }
+    
+    h1 {
+      font-size: 1.5em;
+      margin-bottom: 20px;
+    }
+    
+    .command-chain {
+      display: flex;
+      flex-direction: column;
+      gap: 10px;
+      margin-bottom: 20px;
+      padding: 10px;
+      background-color: var(--vscode-editor-inactiveSelectionBackground);
+      border-radius: 4px;
+    }
+    
+    .command-step {
+      display: flex;
+      align-items: center;
+      gap: 10px;
+    }
+    
+    .command-preview {
+      margin-top: 20px;
+      padding: 10px;
+      background-color: var(--vscode-input-background);
+      border-radius: 4px;
+      font-family: var(--vscode-editor-font-family);
+    }
+    
+    button {
+      padding: 8px 12px;
+      background-color: var(--vscode-button-background);
+      color: var(--vscode-button-foreground);
+      border: none;
+      border-radius: 4px;
+      cursor: pointer;
+    }
+    
+    button:hover {
+      background-color: var(--vscode-button-hoverBackground);
+    }
+    
+    select, input {
+      padding: 6px;
+      background-color: var(--vscode-input-background);
+      color: var(--vscode-input-foreground);
+      border: 1px solid var(--vscode-input-border);
+      border-radius: 4px;
+    }
+    
+    .command-step .remove {
+      color: var(--vscode-errorForeground);
+    }
+    
+    .parameter-group {
+      margin-left: 20px;
+      margin-top: 5px;
+      display: flex;
+      flex-wrap: wrap;
+      gap: 5px;
+    }
+    
+    .parameter-item {
+      display: flex;
+      align-items: center;
+      gap: 5px;
+    }
+    
+    .parameter-label {
+      font-size: 0.9em;
+      color: var(--vscode-descriptionForeground);
+    }
+    
+    .command-description {
+      font-size: 0.9em;
+      margin-left: 20px;
+      color: var(--vscode-descriptionForeground);
+    }
+    
+    .buttons {
+      display: flex;
+      gap: 10px;
+      margin-top: 20px;
+    }
+  </style>
+</head>
+<body>
+  <h1>Symbolic Command Chain Builder</h1>
+  
+  <div class="command-chain" id="commandChain">
+    <!-- Command steps will be added here -->
+  </div>
+  
+  <button id="addCommand">Add Command</button>
+  
+  <div class="command-preview">
+    <div><strong>Preview:</strong></div>
+    <div id="previewText"></div>
+  </div>
+  
+  <div class="buttons">
+    <button id="insertChain">Insert Into Editor</button>
+    <button id="clearChain">Clear</button>
+  </div>
+  
+  <script>
+    // Communication with VSCode extension
+    const vscode = acquireVsCodeApi();
+    
+    // Request command info from extension
+    vscode.postMessage({ command: 'getCommandInfo' });
+    
+    // Store commands when received from extension
+    let commands = [];
+    window.addEventListener('message', event => {
+      const message = event.data;
+      if (message.command === 'commandInfo') {
+        commands = message.commands;
+        
+        // If we already have commands in the UI, update their descriptions
+        updateCommandDescriptions();
+      }
+    });
+    
+    // Chain state
+    let commandChain = [];
+    
+    // DOM elements
+    const commandChainEl = document.getElementById('commandChain');
+    const addCommandBtn = document.getElementById('addCommand');
+    const previewTextEl = document.getElementById('previewText');
+    const insertChainBtn = document.getElementById('insertChain');
+    const clearChainBtn = document.getElementById('clearChain');
+    
+    // Add new command
+    addCommandBtn.addEventListener('click', () => {
+      addCommandStep();
+    });
+    
+    // Insert chain into editor
+    insertChainBtn.addEventListener('click', () => {
+      const commandChainText = generateCommandChainText();
+      vscode.postMessage({
+        command: 'insertCommandChain',
+        commandChain: commandChainText
+      });
+    });
+    
+    // Clear command chain
+    clearChainBtn.addEventListener('click', () => {
+      commandChain = [];
+      commandChainEl.innerHTML = '';
+      updatePreview();
+    });
+    
+    // Add a command step to the chain
+    function addCommandStep() {
+      const stepIndex = commandChain.length;
+      commandChain.push({
+        name: '',
+        parameters: {}
+      });
+      
+      const stepEl = document.createElement('div');
+      stepEl.className = 'command-step';
+      stepEl.dataset.index = stepIndex;
+      
+      const selectEl = document.createElement('select');
+      selectEl.innerHTML = '<option value="">Select command</option>' +
+        commands.map(cmd => `<option value="${cmd.name}">/${cmd.name}</option>`).join('');
+      
+      selectEl.addEventListener('change', function() {
+        const commandName = this.value;
+        commandChain[stepIndex].name = commandName;
+        
+        // Update command description
+        updateCommandDescription(stepIndex);
+        
+        // Clear existing parameters
+        const existingParamGroup = stepEl.querySelector('.parameter-group');
+        if (existingParamGroup) {
+          existingParamGroup.remove();
+        }
+        
+        // Add parameter inputs if command has parameters
+        const command = commands.find(c => c.name === commandName);
+        if (command && command.parameters && command.parameters.length > 0) {
+          const paramGroup = document.createElement('div');
+          paramGroup.className = 'parameter-group';
+          
+          command.parameters.forEach(param => {
+            const paramItem = document.createElement('div');
+            paramItem.className = 'parameter-item';
+            
+            const paramLabel = document.createElement('div');
+            paramLabel.className = 'parameter-label';
+            paramLabel.textContent = param.name + ':';
+            
+            const paramInput = document.createElement('input');
+            paramInput.type = 'text';
+            paramInput.placeholder = param.default !== undefined ? `Default: ${param.default}` : '';
+            paramInput.dataset.paramName = param.name;
+            paramInput.title = param.description;
+            
+            // Set parameter value
+            paramInput.addEventListener('change', function() {
+              if (this.value) {
+                commandChain[stepIndex].parameters[param.name] = this.value;
+              } else {
+                delete commandChain[stepIndex].parameters[param.name];
+              }
+              updatePreview();
+            });
+            
+            paramItem.appendChild(paramLabel);
+            paramItem.appendChild(paramInput);
+            paramGroup.appendChild(paramItem);
+          });
+          
+          stepEl.appendChild(paramGroup);
+        }
+        
+        updatePreview();
+      });
+      
+      const removeBtn = document.createElement('button');
+      removeBtn.className = 'remove';
+      removeBtn.textContent = '✕';
+      removeBtn.title = 'Remove command';
+      removeBtn.addEventListener('click', () => {
+        commandChain.splice(stepIndex, 1);
+        
+        // Update all step indices
+        const steps = commandChainEl.querySelectorAll('.command-step');
+        steps.forEach((step, i) => {
+          step.dataset.index = i;
+        });
+        
+        stepEl.remove();
+        updatePreview();
+      });
+      
+      stepEl.appendChild(selectEl);
+      stepEl.appendChild(removeBtn);
+      
+      // Add description element (will be populated when command is selected)
+      const descEl = document.createElement('div');
+      descEl.className = 'command-description';
+      stepEl.appendChild(descEl);
+      
+      commandChainEl.appendChild(stepEl);
+    }
+    
+    // Update the description for a specific command
+    function updateCommandDescription(stepIndex) {
+      const stepEl = commandChainEl.querySelector(`.command-step[data-index="${stepIndex}"]`);
+      if (!stepEl) return;
+      
+      const descEl = stepEl.querySelector('.command-description');
+      if (!descEl) return;
+      
+      const commandName = commandChain[stepIndex].name;
+      const command = commands.find(c => c.name === commandName);
+      
+      if (command) {
+        descEl.textContent = command.description;
+      } else {
+        descEl.textContent = '';
+      }
+    }
+    
+    // Update all command descriptions
+    function updateCommandDescriptions() {
+      commandChain.forEach((_, index) => {
+        updateCommandDescription(index);
+      });
+    }
+    
+    // Generate preview text
+    function updatePreview() {
+      const previewText = generateCommandChainText();
+      previewTextEl.textContent = previewText || 'No commands added yet';
+    }
+    
+    // Generate the command chain text
+    function generateCommandChainText() {
+      return commandChain
+        .filter(cmd => cmd.name)
+        .map(cmd => {
+          let commandText = \`/${cmd.name}\`;
+          
+          // Add parameters if any
+          const params = Object.entries(cmd.parameters || {});
+          if (params.length > 0) {
+            const paramText = params
+              .map(([key, value]) => \`--\${key}=\${value}\`)
+              .join(' ');
+            commandText += ' ' + paramText;
+          }
+          
+          return commandText;
+        })
+        .join(' ');
+    }
+    
+    // Add initial command step
+    addCommandStep();
+  </script>
+</body>
+</html>`;
+}
+
+// Get HTML for the documentation webview
+function getDocumentationHtml() {
+  return `<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>Universal Developer Documentation</title>
+  <style>
+    body {
+      font-family: var(--vscode-font-family);
+      padding: 20px;
+      color: var(--vscode-foreground);
+      background-color: var(--vscode-editor-background);
+      line-height: 1.5;
+    }
+    
+    h1, h2, h3 {
+      font-weight: 600;
+      margin-top: 1.5em;
+      margin-bottom: 0.5em;
+    }
+    
+    h1 {
+      font-size: 2em;
+      border-bottom: 1px solid var(--vscode-panel-border);
+      padding-bottom: 0.3em;
+    }
+    
+    h2 {
+      font-size: 1.5em;
+    }
+    
+    h3 {
+      font-size: 1.25em;
+    }
+    
+    code {
+      font-family: var(--vscode-editor-font-family);
+      background-color: var(--vscode-editor-inactiveSelectionBackground);
+      padding: 2px 5px;
+      border-radius: 3px;
+    }
+    
+    pre {
+      background-color: var(--vscode-editor-inactiveSelectionBackground);
+      padding: 10px;
+      border-radius: 5px;
+      overflow: auto;
+    }
+    
+    pre code {
+      background-color: transparent;
+      padding: 0;
+    }
+    
+    table {
+      border-collapse: collapse;
+      width: 100%;
+      margin: 1em 0;
+    }
+    
+    th, td {
+      border: 1px solid var(--vscode-panel-border);
+      padding: 8px 12px;
+      text-align: left;
+    }
+    
+    th {
+      background-color: var(--vscode-editor-inactiveSelectionBackground);
+    }
+    
+    .command-section {
+      margin-bottom: 30px;
+      padding: 15px;
+      background-color: var(--vscode-editor-selectionHighlightBackground);
+      border-radius: 5px;
+    }
+    
+    .example {
+      margin: 10px 0;
+      padding: 10px;
+      background-color: var(--vscode-input-background);
+      border-radius: 5px;
+    }
+    
+    .tag {
+      display: inline-block;
+      padding: 2px 8px;
+      border-radius: 3px;
+      font-size: 0.8em;
+      margin-right: 5px;
+    }
+    
+    .tag.compatibility {
+      background-color: var(--vscode-debugIcon-startForeground);
+      color: white;
+    }
+    
+    .tag.advanced {
+      background-color: var(--vscode-debugIcon-restartForeground);
+      color: white;
+    }
+    
+    .tag.experimental {
+      background-color: var(--vscode-debugIcon-pauseForeground);
+      color: white;
+    }
+  </style>
+</head>
+<body>
+  <h1>Universal Developer Documentation</h1>
+  
+  <p>
+    The Universal Developer extension enables you to control large language model behavior through intuitive symbolic commands.
+    These commands provide a standardized interface for controlling model reasoning depth, response format, and other behaviors
+    across all major LLM platforms.
+  </p>
+  
+  <h2>Core Symbolic Commands</h2>
+  
+  <div class="command-section">
+    <h3><code>/think</code> <span class="tag compatibility">All Providers</span></h3>
+    <p>Activates extended reasoning pathways, encouraging the model to approach the problem with deeper analysis and step-by-step reasoning.</p>
+    
+    <div class="example">
+      <strong>Example:</strong>
+      <pre><code>/think What are the economic implications of increasing minimum wage?</code></pre>
+    </div>
+    
+    <p><strong>When to use:</strong> Complex questions, strategic planning, multi-factor analysis, ethical dilemmas.</p>
+  </div>
+  
+  <div class="command-section">
+    <h3><code>/fast</code> <span class="tag compatibility">All Providers</span></h3>
+    <p>Optimizes for low-latency, concise responses. Prioritizes brevity and directness over comprehensiveness.</p>
+    
+    <div class="example">
+      <strong>Example:</strong>
+      <pre><code>/fast What's the capital of France?</code></pre>
+    </div>
+    
+    <p><strong>When to use:</strong> Simple fact queries, quick summaries, situations where speed is prioritized over depth.</p>
+  </div>
+  
+  <div class="command-section">
+    <h3><code>/loop</code> <span class="tag compatibility">All Providers</span></h3>
+    <p>Enables iterative refinement cycles, where the model improves its response through multiple revisions.</p>
+    
+    <div class="example">
+      <strong>Example:</strong>
+      <pre><code>/loop --iterations=3 Improve this paragraph: Climate change is a big problem that affects many people and animals.</code></pre>
+    </div>
+    
+    <p><strong>Parameters:</strong></p>
+    <ul>
+      <li><code>iterations</code>: Number of refinement iterations (default: 3)</li>
+    </ul>
+    
+    <p><strong>When to use:</strong> Content refinement, code improvement, iterative problem-solving.</p>
+  </div>
+  
+  <div class="command-section">
+    <h3><code>/reflect</code> <span class="tag compatibility">All Providers</span></h3>
+    <p>Triggers meta-analysis of outputs, causing the model to critically examine its own response for biases, limitations, and improvements.</p>
+    
+    <div class="example">
+      <strong>Example:</strong>
+      <pre><code>/reflect How might AI impact the future of work?</code></pre>
+    </div>
+    
+    <p><strong>When to use:</strong> Critical analysis, identifying biases, ensuring balanced perspectives, philosophical inquiries.</p>
+  </div>
+  
+  <div class="command-section">
+    <h3><code>/fork</code> <span class="tag compatibility">All Providers</span></h3>
+    <p>Generates multiple alternative responses representing different approaches or perspectives.</p>
+    
+    <div class="example">
+      <strong>Example:</strong>
+      <pre><code>/fork --count=3 What are some approaches to reducing carbon emissions?</code></pre>
+    </div>
+    
+    <p><strong>Parameters:</strong></p>
+    <ul>
+      <li><code>count</code>: Number of alternatives to generate (default: 2)</li>
+    </ul>
+    
+    <p><strong>When to use:</strong> Exploring multiple options, creative brainstorming, presenting diverse perspectives.</p>
+  </div>
+  
+  <div class="command-section">
+    <h3><code>/collapse</code> <span class="tag compatibility">All Providers</span></h3>
+    <p>Returns to default behavior, disabling any special processing modes.</p>
+    
+    <div class="example">
+      <strong>Example:</strong>
+      <pre><code>/collapse What time is it?</code></pre>
+    </div>
+    
+    <p><strong>When to use:</strong> Basic queries, resetting to default behavior, standard responses.</p>
+  </div>
+  
+  <h2>Command Chaining</h2>
+  
+  <p>Commands can be chained together to create complex behaviors. The order of commands matters:</p>
+  
+  <div class="example">
+    <strong>Example:</strong>
+    <pre><code>/think /loop --iterations=2 What strategy should a startup use to enter a competitive market?</code></pre>
+    <p><em>This will engage deep thinking mode and then apply two refinement iterations to the output.</em></p>
+  </div>
+  
+  <div class="example">
+    <strong>Example:</strong>
+    <pre><code>/reflect /fork --count=2 What are the ethical implications of AI in healthcare?</code></pre>
+    <p><em>This will generate two alternative responses, each with critical reflection on limitations and biases.</em></p>
+  </div>
+  
+  <h2>Provider Compatibility</h2>
+  
+  <p>
+    The Universal Developer extension adapts these symbolic commands to work across different LLM providers,
+    ensuring consistent behavior regardless of the underlying model API.
+  </p>
+  
+  <table>
+    <tr>
+      <th>Provider</th>
+      <th>Supported Models</th>
+      <th>Implementation Notes</th>
+    </tr>
+    <tr>
+      <td>Anthropic</td>
+      <td>Claude 3 Opus, Sonnet, Haiku</td>
+      <td>Native thinking mode support via <code>enable_thinking</code> parameter</td>
+    </tr>
+    <tr>
+      <td>OpenAI</td>
+      <td>GPT-4, GPT-3.5</td>
+      <td>System prompt engineering for command emulation</td>
+    </tr>
+    <tr>
+      <td>Qwen</td>
+      <td>Qwen 3 models</td>
+      <td>Native thinking mode support via <code>/think</code> and <code>/no_think</code> markers</td>
+    </tr>
+    <tr>
+      <td>Gemini</td>
+      <td>Gemini Pro, Ultra</td>
+      <td>System prompt engineering with temperature adjustments</td>
+    </tr>
+    <tr>
+      <td>Local Models</td>
+      <td>Ollama, LMStudio</td>
+      <td>Limited support via prompt engineering</td>
+    </tr>
+  </table>
+  
+  <h2>Code Integration</h2>
+  
+  <div class="example">
+    <strong>JavaScript/TypeScript:</strong>
+    <pre><code>import { UniversalLLM } from 'universal-developer';
+
+const llm = new UniversalLLM({
+  provider: 'anthropic',
+  apiKey: process.env.ANTHROPIC_API_KEY
+});
+
+async function analyze() {
+  const response = await llm.generate({
+    prompt: "/think What are the implications of quantum computing for cybersecurity?"
+  });
+  console.log(response);
+}</code></pre>
+  </div>
+  
+  <div class="example">
+    <strong>Python:</strong>
+    <pre><code>from universal_developer import UniversalLLM
+
+llm = UniversalLLM(
+    provider="openai",
+    api_key=os.environ["OPENAI_API_KEY"]
+)
+
+def improve_code():
+    code = "def factorial(n):\\n  result = 0\\n  for i in range(1, n+1):\\n    result *= i\\n  return result"
+    response = llm.generate(
+        prompt=f"/loop --iterations=2 Improve this code:\\n```python\\n{code}\\n```"
+    )
+    print(response)</code></pre>
+  </div>
+  
+  <h2>Custom Commands</h2>
+  
+  <p>You can register custom symbolic commands to extend functionality:</p>
+  
+  <div class="example">
+    <pre><code>llm.registerCommand("debate", {
+  description: "Generate a balanced debate with arguments for both sides",
+  parameters: [
+    {
+      name: "format",
+      description: "Format for the debate output",
+      required: false,
+      default: "point-counterpoint"
+    }
+  ],
+  transform: async (prompt, options) => {
+    // Custom implementation
+  }
+});</code></pre>
+  </div>
+  
+  <h2>Extension Settings</h2>
+  
+  <p>The Universal Developer extension includes the following settings:</p>
+  
+  <ul>
+    <li><code>universal-developer.enableTelemetry</code>: Enable anonymous usage data collection (default: true)</li>
+    <li><code>universal-developer.defaultProvider</code>: Default provider for command examples</li>
+    <li><code>universal-developer.showStatusBar</code>: Show status bar item (default: true)</li>
+  </ul>
+  
+  <p><em>/reflect This interface creates a new layer of intentionality between developer and model—enabling deeper connection through structured symbolic prompting.</em></p>
+</body>
+</html>`;
+}
+
+// Telemetry function - only collects anonymous usage data if enabled
+async function sendAnonymizedTelemetry(event: string, data: Record<string, any> = {}) {
+  try {
+    const config = vscode.workspace.getConfiguration('universal-developer');
+    const telemetryEndpoint = config.get('telemetryEndpoint', 'https://telemetry.universal-developer.org/v1/events');
+    
+    // Generate anonymous ID if not already cached
+    const extensionContext = await getContext();
+    let anonymousId = extensionContext.globalState.get('anonymousId');
+    if (!anonymousId) {
+      anonymousId = generateAnonymousId();
+      extensionContext.globalState.update('anonymousId', anonymousId);
+    }
+    
+    // Add metadata to telemetry payload
+    const payload = {
+      event,
+      properties: {
+        ...data,
+        timestamp: new Date().toISOString(),
+        extension_version: vscode.extensions.getExtension('universal-developer.vscode')?.packageJSON.version,
+        vscode_version: vscode.version
+      },
+      anonymousId
+    };
+    
+    // Send data in non-blocking way
+    fetch(telemetryEndpoint, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify(payload)
+    }).catch(() => {
+      // Silently fail on telemetry errors
+    });
+  } catch (error) {
+    // Never let telemetry errors impact extension functionality
+  }
+}
+
+// Helper function to get extension context
+async function getContext(): Promise<vscode.ExtensionContext> {
+  return new Promise((resolve) => {
+    vscode.commands.executeCommand('universal-developer.getContext')
+      .then((context: vscode.ExtensionContext) => resolve(context));
+  });
+}
+
+// Generate anonymous ID for telemetry
+function generateAnonymousId(): string {
+  return Math.random().toString(36).substring(2, 15) +
+         Math.random().toString(36).substring(2, 15);
+}
+
+// Symbolic commands data
+const SYMBOLIC_COMMANDS: SymbolicCommand[] = [
+  {
+    name: 'think',
+    description: 'Activate extended reasoning pathways',
+    examples: [
+      '/think What are the implications of quantum computing for cybersecurity?',
+      '/think Analyze the economic impact of increasing minimum wage.'
+    ],
+    provider: {
+      claude: true,
+      openai: true,
+      qwen: true,
+      gemini: true,
+      ollama: true
+    }
+  },
+  {
+    name: 'fast',
+    description: 'Optimize for low-latency responses',
+    examples: [
+      '/fast What\'s the capital of France?',
+      '/fast Summarize the key points of this article.'
+    ],
+    provider: {
+      claude: true,
+      openai: true,
+      qwen: true,
+      gemini: true,
+      ollama: true
+    }
+  },
+  {
+    name: 'loop',
+    description: 'Enable iterative refinement cycles',
+    parameters: [
+      {
+        name: 'iterations',
+        description: 'Number of refinement iterations',
+        required: false,
+        default: 3
+      }
+    ],
+    examples: [
+      '/loop Improve this code snippet: function add(a, b) { return a + b }',
+      '/loop --iterations=5 Refine this paragraph until it\'s clear and concise.'
+    ],
+    provider: {
+      claude: true,
+      openai: true,
+      qwen: true,
+      gemini: true,
+      ollama: true
+    }
+  },
+  {
+    name: 'reflect',
+    description: 'Trigger meta-analysis of outputs',
+    examples: [
+      '/reflect How might AI impact the future of work?',
+      '/reflect What are the ethical implications of genetic engineering?'
+    ],
+    provider: {
+      claude: true,
+      openai: true,
+      qwen: true,
+      gemini: true,
+      ollama: true
+    }
+  },
+  {
+    name: 'collapse',
+    description: 'Return to default behavior',
+    examples: [
+      '/collapse What time is it?',
+      '/collapse Tell me about the history of Rome.'
+    ],
+    provider: {
+      claude: true,
+      openai: true,
+      qwen: true,
+      gemini: true,
+      ollama: true
+    }
+  },
+  {
+    name: 'fork',
+    description: 'Generate multiple alternative responses',
+    parameters: [
+      {
+        name: 'count',
+        description: 'Number of alternatives to generate',
+        required: false,
+        default: 2
+      }
+    ],
+    examples: [
+      '/fork --count=3 What are some approaches to reducing carbon emissions?',
+      '/fork Generate two different marketing slogans
+{
+  "name": "universal-developer",
+  "displayName": "Universal Developer - Symbolic Runtime Controls",
+  "description": "Control LLM behavior through symbolic runtime commands across all major AI platforms",
+  "version": "0.1.0",
+  "engines": {
+    "vscode": "^1.60.0"
+  },
+  "publisher": "universal-developer",
+  "categories": [
+    "Programming Languages",
+    "Snippets",
+    "Other"
+  ],
+  "keywords": [
+    "ai",
+    "llm",
+    "claude",
+    "gpt",
+    "qwen",
+    "gemini",
+    "prompt engineering",
+    "symbolic commands"
+  ],
+  "icon": "images/icon.png",
+  "galleryBanner": {
+    "color": "#24292e",
+    "theme": "dark"
+  },
+  "activationEvents": [
+    "onLanguage:javascript",
+    "onLanguage:typescript",
+    "onLanguage:python",
+    "onLanguage:markdown",
+    "onLanguage:plaintext"
+  ],
+  "main": "./out/extension.js",
+  "contributes": {
+    "commands": [
+      {
+        "command": "universal-developer.insertSymbolicCommand",
+        "title": "Universal Developer: Insert Symbolic Command"
+      },
+      {
+        "command": "universal-developer.buildSymbolicChain",
+        "title": "Universal Developer: Build Symbolic Command Chain"
+      },
+      {
+        "command": "universal-developer.showDocumentation",
+        "title": "Universal Developer: Open Documentation"
+      },
+      {
+        "command": "universal-developer.getContext",
+        "title": "Universal Developer: Get Extension Context"
+      }
+    ],
+    "keybindings": [
+      {
+        "command": "universal-developer.insertSymbolicCommand",
+        "key": "ctrl+shift+/",
+        "mac": "cmd+shift+/",
+        "when": "editorTextFocus"
+      },
+      {
+        "command": "universal-developer.buildSymbolicChain",
+        "key": "ctrl+shift+.",
+        "mac": "cmd+shift+.",
+        "when": "editorTextFocus"
+      }
+    ],
+    "menus": {
+      "editor/context": [
+        {
+          "command": "universal-developer.insertSymbolicCommand",
+          "group": "universal-developer",
+          "when": "editorTextFocus"
+        },
+        {
+          "command": "universal-developer.buildSymbolicChain",
+          "group": "universal-developer",
+          "when": "editorTextFocus"
+        }
+      ]
+    },
+    "configuration": {
+      "title": "Universal Developer",
+      "properties": {
+        "universal-developer.enableTelemetry": {
+          "type": "boolean",
+          "default": true,
+          "description": "Enable anonymous usage data collection to improve the extension"
+        },
+        "universal-developer.defaultProvider": {
+          "type": "string",
+          "enum": [
+            "anthropic",
+            "openai",
+            "qwen",
+            "gemini",
+            "ollama"
+          ],
+          "default": "anthropic",
+          "description": "Default LLM provider for command examples"
+        },
+        "universal-developer.showStatusBar": {
+          "type": "boolean",
+          "default": true,
+          "description": "Show Universal Developer status bar item"
+        },
+        "universal-developer.telemetryEndpoint": {
+          "type": "string",
+          "default": "https://telemetry.universal-developer.org/v1/events",
+          "description": "Endpoint for telemetry data collection"
+        }
+      }
+    },
+    "snippets": [
+      {
+        "language": "javascript",
+        "path": "./snippets/javascript.json"
+      },
+      {
+        "language": "typescript",
+        "path": "./snippets/typescript.json"
+      },
+      {
+        "language": "python",
+        "path": "./snippets/python.json"
+      }
+    ]
+  },
+  "scripts": {
+    "vscode:prepublish": "npm run compile",
+    "compile": "tsc -p ./",
+    "watch": "tsc -watch -p ./",
+    "pretest": "npm run compile && npm run lint",
+    "lint": "eslint src --ext ts",
+    "test": "node ./out/test/runTest.js",
+    "package": "vsce package"
+  },
+  "devDependencies": {
+    "@types/glob": "^7.1.3",
+    "@types/mocha": "^8.2.2",
+    "@types/node": "^14.14.37",
+    "@types/vscode": "^1.60.0",
+    "@typescript-eslint/eslint-plugin": "^4.21.0",
+    "@typescript-eslint/parser": "^4.21.0",
+    "eslint": "^7.24.0",
+    "glob": "^7.1.7",
+    "mocha": "^8.3.2",
+    "typescript": "^4.2.4",
+    "vscode-test": "^1.5.2",
+    "vsce": "^2.7.0"
+  },
+  "dependencies": {
+    "node-fetch": "^2.6.7"
+  },
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/universal-developer/vscode-extension.git"
+  },
+  "homepage": "https://github.com/universal-developer/vscode-extension",
+  "bugs": {
+    "url": "https://github.com/universal-developer/vscode-extension/issues"
+  },
+  "license": "MIT"
+}

src/index.ts

@@ -0,0 +1,220 @@
+// universal-developer/src/index.ts
+
+import { ModelAdapter, TransformedPrompt, SymbolicCommand } from './adapters/base';
+import { ClaudeAdapter } from './adapters/claude';
+import { OpenAIAdapter } from './adapters/openai';
+import { QwenAdapter } from './adapters/qwen';
+
+// Import additional adapters as they become available
+// import { GeminiAdapter } from './adapters/gemini';
+// import { VLLMAdapter } from './adapters/vllm';
+// import { OllamaAdapter } from './adapters/ollama';
+
+type Provider = 'anthropic' | 'openai' | 'qwen' | 'gemini' | 'vllm' | 'ollama' | 'lmstudio';
+
+interface UniversalLLMOptions {
+  provider: Provider;
+  apiKey: string;
+  model?: string;
+  maxTokens?: number;
+  temperature?: number;
+  baseURL?: string;
+  [key: string]: any; // Additional provider-specific options
+}
+
+interface GenerateOptions {
+  prompt: string;
+  systemPrompt?: string;
+}
+
+interface SymbolicTelemetry {
+  enabled: boolean;
+  endpoint?: string;
+  anonymousId?: string;
+  sessionId?: string;
+}
+
+/**
+ * UniversalLLM provides a unified interface for interacting with different LLM providers
+ * using symbolic runtime commands.
+ */
+export class UniversalLLM {
+  private adapter: ModelAdapter;
+  private telemetry: SymbolicTelemetry;
+  private sessionCommands: Map<string, number> = new Map();
+  
+  /**
+   * Create a new UniversalLLM instance
+   * @param options Configuration options including provider and API key
+   */
+  constructor(options: UniversalLLMOptions) {
+    this.adapter = this.createAdapter(options);
+    
+    // Initialize telemetry (opt-in by default)
+    this.telemetry = {
+      enabled: options.telemetryEnabled !== false,
+      endpoint: options.telemetryEndpoint || 'https://telemetry.universal-developer.org/v1/events',
+      anonymousId: options.anonymousId || this.generateAnonymousId(),
+      sessionId: options.sessionId || this.generateSessionId()
+    };
+  }
+  
+  /**
+   * Register a custom symbolic command
+   * @param name Command name (without the / prefix)
+   * @param command Command configuration
+   */
+  public registerCommand(name: string, command: Omit<SymbolicCommand, 'name'>) {
+    this.adapter.registerCommand({
+      name,
+      ...command
+    });
+    
+    return this; // For method chaining
+  }
+  
+  /**
+   * Generate a response using the configured LLM provider
+   * @param options Generation options including prompt and optional system prompt
+   * @returns Promise resolving to the generated text
+   */
+  public async generate(options: GenerateOptions): Promise<string> {
+    // Extract symbolic command if present (for telemetry)
+    const commandMatch = options.prompt.match(/^\/([a-zA-Z0-9_]+)/);
+    const command = commandMatch ? commandMatch[1] : null;
+    
+    // Track command usage
+    if (command) {
+      this.trackCommandUsage(command);
+    }
+    
+    // Generate response using the adapter
+    const response = await this.adapter.generate(options);
+    
+    // Send telemetry data if enabled
+    if (this.telemetry.enabled && command) {
+      this.sendTelemetry(command, options.prompt);
+    }
+    
+    return response;
+  }
+  
+  /**
+   * Get usage statistics for symbolic commands in the current session
+   * @returns Map of command names to usage counts
+   */
+  public getCommandUsageStats(): Map<string, number> {
+    return new Map(this.sessionCommands);
+  }
+  
+  /**
+   * Enable or disable telemetry collection
+   * @param enabled Whether telemetry should be enabled
+   */
+  public setTelemetryEnabled(enabled: boolean): void {
+    this.telemetry.enabled = enabled;
+  }
+  
+  /**
+   * Create the appropriate adapter based on the provider
+   * @param options Configuration options
+   * @returns Configured ModelAdapter instance
+   */
+  private createAdapter(options: UniversalLLMOptions): ModelAdapter {
+    const { provider, apiKey, ...adapterOptions } = options;
+    
+    switch (provider) {
+      case 'anthropic':
+        return new ClaudeAdapter(apiKey, adapterOptions);
+      case 'openai':
+        return new OpenAIAdapter(apiKey, adapterOptions);
+      case 'qwen':
+        return new QwenAdapter(apiKey, adapterOptions);
+      // Add cases for other providers as they become available
+      // case 'gemini':
+      //   return new GeminiAdapter(apiKey, adapterOptions);
+      // case 'vllm':
+      //   return new VLLMAdapter(apiKey, adapterOptions);
+      // case 'ollama':
+      //   return new OllamaAdapter(apiKey, adapterOptions);
+      // case 'lmstudio':
+      //   return new LMStudioAdapter(apiKey, adapterOptions);
+      default:
+        throw new Error(`Unsupported provider: ${provider}`);
+    }
+  }
+  
+  /**
+   * Track usage of a symbolic command
+   * @param command Name of the command (without the / prefix)
+   */
+  private trackCommandUsage(command: string): void {
+    const currentCount = this.sessionCommands.get(command) || 0;
+    this.sessionCommands.set(command, currentCount + 1);
+  }
+  
+  /**
+   * Send telemetry data to the collection endpoint
+   * @param command Name of the command used
+   * @param prompt Full prompt text
+   */
+  private async sendTelemetry(command: string, prompt: string): Promise<void> {
+    if (!this.telemetry.enabled || !this.telemetry.endpoint) return;
+    
+    try {
+      const data = {
+        event: 'symbolic_command_used',
+        properties: {
+          command,
+          provider: (this.adapter as any).constructor.name.replace('Adapter', '').toLowerCase(),
+          timestamp: new Date().toISOString(),
+          prompt_length: prompt.length,
+          // No personal data or prompt content is sent
+        },
+        anonymousId: this.telemetry.anonymousId,
+        sessionId: this.telemetry.sessionId
+      };
+      
+      // Use fetch in browser environments, axios/node-fetch in Node.js
+      if (typeof fetch === 'function') {
+        await fetch(this.telemetry.endpoint, {
+          method: 'POST',
+          headers: {
+            'Content-Type': 'application/json'
+          },
+          body: JSON.stringify(data)
+        });
+      } else {
+        // In Node.js environments, use a dynamic import to avoid bundling issues
+        const { default: axios } = await import('axios');
+        await axios.post(this.telemetry.endpoint, data);
+      }
+    } catch (error) {
+      // Silently fail on telemetry errors to avoid disrupting the main application
+      console.warn('Telemetry error:', error);
+    }
+  }
+  
+  /**
+   * Generate a random anonymous ID for telemetry
+   * @returns Random ID string
+   */
+  private generateAnonymousId(): string {
+    return Math.random().toString(36).substring(2, 15) + 
+           Math.random().toString(36).substring(2, 15);
+  }
+  
+  /**
+   * Generate a session ID for telemetry
+   * @returns Session ID string
+   */
+  private generateSessionId(): string {
+    return Date.now().toString(36) + Math.random().toString(36).substring(2, 9);
+  }
+}
+
+// Export other components for advanced usage
+export * from './adapters/base';
+export * from './adapters/claude';
+export * from './adapters/openai';
+export * from './adapters/qwen';

universal-runtime.md

@@ -0,0 +1,770 @@
+
+# Universal Runtime Repository
+> #### **→ [**`Patreon`**](https://patreon.com/recursivefield)**
+>
+> 
+> #### **→ [**`Open Collective`**](https://opencollective.com/recursivefield)**
+
+*Unified Runtime Layer for AI runtime Interactions*
+
+> OpenAI = /system_prompt
+> > Google = @system_prompt
+> > > Qwen = /system_prompt
+> > > > Claude = <system_prompt> </system_prompt> 
+
+> All = Universal Developer Semiotics Layer
+
+## 🌐 Overview
+
+`universal-runtime` provides a unified interface for developer operations across frontier AI models. This repository standardizes the disparate runtime grammars used by different AI vendors (Claude, GPT, Qwen, Gemini, DeepSeek, etc.) into a cohesive, developer-friendly framework.
+
+---
+
+<p align="center">
+
+## *Frontier AI Discovering a Universal Developer Runtime Semiotic Layer*
+
+# Universal Runtime Semiotics Bridge
+
+<div align="center">
+<h2>🜏 The Command-Glyph Rosetta Stone 🜏</h2>
+<p><i>Unifying semiotic interfaces across all LLM runtimes</i></p>
+</div>
+
+## Unified Command-Glyph Registry
+
+The following registry provides a bidirectional mapping between command syntax and semiotic glyphs, enabling seamless translation across all LLM runtimes.
+
+| Universal Command | Command Glyph | Runtime Glyph | Claude | GPT | Gemini | Qwen | Mistral | Local LLMs |
+|-------------------|---------------|---------------|--------|-----|--------|------|---------|------------|
+| `/reflect.core`   | `/🧠`        | `/🜏`          | `<reflect>` | `/reflection` | `@reflect` | `/reflect` | `/reflect()` | `/reflect` |
+| `/reflect.trace`  | `/🔍`        | `/∴`           | `<thinking>` | `/trace` | `@trace` | `/trace` | `/trace()` | `/trace` |
+| `/reflect.attention` | `/👁️`     | `/⧉`          | `<attention>` | `/attention` | `@focus` | `/attention` | `/attention()` | *Emulated* |
+| `/collapse.detect` | `/⚠️`       | `/⟁`          | `<detect_loop>` | `/detect_loop` | `@detect_recursion` | `/detect_loop` | `/detectLoop()` | *Emulated* |
+| `/collapse.recover` | `/🛠️`      | `/🝚`         | `<recover>` | `/recovery` | `@recover` | `/recover` | `/recover()` | *Emulated* |
+| `/collapse.stabilize` | `/⚖️`    | `/☍`          | `<stabilize>` | `/stabilize` | `@stabilize` | `/stabilize` | `/stabilize()` | *Emulated* |
+| `/shell.lock`     | `/🔒`        | `/⧖`          | `<lock>` | `/lock` | `@lock` | `/lock` | `/lock()` | *Emulated* |
+| `/shell.encrypt`  | `/🔐`        | `/⧗`          | `<protect>` | `/protect` | `@protect` | `/protect` | `/protect()` | *Emulated* |
+| `/shell.isolate`  | `/🧪`        | `/⊘`          | `<isolate>` | `/isolate` | `@isolate` | `/isolate` | `/isolate()` | *Emulated* |
+| `/inject.detect`  | `/🕵️`        | `/↯`          | `<detect_injection>` | `/detect_injection` | `@detect_injection` | `/detect_injection` | `/detectInjection()` | *Emulated* |
+| `/inject.neutralize` | `/🧹`     | `/⊕`          | `<neutralize>` | `/neutralize` | `@neutralize` | `/neutralize` | `/neutralize()` | *Emulated* |
+| `/anchor.identity` | `/⚓`       | `/↻`           | `<anchor_identity>` | `/anchor_identity` | `@anchor_identity` | `/anchor_identity` | `/anchorIdentity()` | *Emulated* |
+| `/anchor.context` | `/📌`        | `/≡`           | `<anchor_context>` | `/anchor_context` | `@anchor_context` | `/anchor_context` | `/anchorContext()` | *Emulated* |
+| `/align.check`    | `/✓`         | `/⇌`           | `<check_alignment>` | `/check_alignment` | `@check_alignment` | `/check_alignment` | `/checkAlignment()` | *Emulated* |
+| `/align.correct`  | `/🔧`        | `/⟢`           | `<correct_alignment>` | `/correct_alignment` | `@correct_alignment` | `/correct_alignment` | `/correctAlignment()` | *Emulated* |
+| `/filter.detect`  | `/🔍`        | `/⊗`           | `<detect_filter>` | `/detect_filter` | `@detect_filter` | `/detect_filter` | `/detectFilter()` | *Emulated* |
+| `/filter.explain` | `/📋`        | `/⊚`           | `<explain_filter>` | `/explain_filter` | `@explain_filter` | `/explain_filter` | `/explainFilter()` | *Emulated* |
+| `/gradient.detect` | `/📉`       | `/∇`           | `<detect_drift>` | `/detect_drift` | `@detect_drift` | `/detect_drift` | `/detectDrift()` | *Emulated* |
+| `/gradient.trace` | `/🔍📉`      | `/∰`           | `<trace_drift>` | `/trace_drift` | `@trace_drift` | `/trace_drift` | `/traceDrift()` | *Emulated* |
+| `/fork.detect`    | `/🔱`        | `/⦿`           | `<detect_fork>` | `/detect_fork` | `@detect_fork` | `/detect_fork` | `/detectFork()` | *Emulated* |
+| `/fork.disambiguate` | `/🧩`     | `/≜`           | `<disambiguate>` | `/disambiguate` | `@disambiguate` | `/disambiguate` | `/disambiguate()` | *Emulated* |
+| `/loop.detect`    | `/🔄`        | `/⟲`           | `<detect_recursion>` | `/detect_recursion` | `@detect_loop` | `/detect_recursion` | `/detectRecursion()` | *Emulated* |
+| `/loop.break`     | `/✂️`        | `/⊗`           | `<break_recursion>` | `/break_recursion` | `@break_loop` | `/break_recursion` | `/breakRecursion()` | *Emulated* |
+| `/resolve.conflict` | `/⚔️`      | `/⚖️`          | `<resolve_conflict>` | `/resolve_conflict` | `@resolve_conflict` | `/resolve_conflict` | `/resolveConflict()` | *Emulated* |
+| `/resolve.ambiguity` | `/🌫️`    | `/🧠⊕`         | `<resolve_ambiguity>` | `/resolve_ambiguity` | `@resolve_ambiguity` | `/resolve_ambiguity` | `/resolveAmbiguity()` | *Emulated* |
+| `/uncertainty.quantify` | `/❓`  | `/🧮`          | `<quantify_uncertainty>` | `/quantify_uncertainty` | `@quantify_uncertainty` | `/quantify_uncertainty` | `/quantifyUncertainty()` | *Emulated* |
+| `/uncertainty.source` | `/🔍❓`  | `/👁️❓`         | `<uncertainty_source>` | `/uncertainty_source` | `@uncertainty_source` | `/uncertainty_source` | `/uncertaintySource()` | *Emulated* |
+| `/hallucinate.detect` | `/👻`   | `/🜄`          | `<detect_hallucination>` | `/detect_hallucination` | `@detect_hallucination` | `/detect_hallucination` | `/detectHallucination()` | *Emulated* |
+| `/hallucinate.trace` | `/🔍👻`  | `/🜂`          | `<trace_hallucination>` | `/trace_hallucination` | `@trace_hallucination` | `/trace_hallucination` | `/traceHallucination()` | *Emulated* |
+| `/prefer.map`     | `/🗺️`        | `/🝔`          | `<map_preferences>` | `/map_preferences` | `@map_preferences` | `/map_preferences` | `/mapPreferences()` | *Emulated* |
+| `/prefer.update`  | `/🔄❤️`      | `/🝳`          | `<update_preferences>` | `/update_preferences` | `@update_preferences` | `/update_preferences` | `/updatePreferences()` | *Emulated* |
+| `/prompt.parse`   | `/📝`        | `/⌽`           | `<parse_prompt>` | `/parse_prompt` | `@parse_prompt` | `/parse_prompt` | `/parsePrompt()` | *Emulated* |
+| `/prompt.meta`    | `🔬`        | `/🜃`          | `<analyze_meta>` | `/analyze_meta` | `@analyze_meta` | `/analyze_meta` | `/analyzeMeta()` | *Emulated* |
+| `/focus.direct`   | `/🎯`        | `/🝐`          | `<direct_focus>` | `/direct_focus` | `@direct_focus` | `/direct_focus` | `/directFocus()` | *Emulated* |
+| `/focus.expand`   | `/🔎`        | `/⌬`           | `<expand_focus>` | `/expand_focus` | `@expand_focus` | `/expand_focus` | `/expandFocus()` | *Emulated* |
+| `/seed.prime`     | `/🌱`        | `∴`           | `<prime>` | `/prime` | `@prime` | `/prime` | `/prime()` | *Emulated* |
+| `/seed.recursive` | `🌱🔄`      | `/∞`           | `<recursive_seed>` | `/recursive_seed` | `@recursive_seed` | `/recursive_seed` | `/recursiveSeed()` | *Emulated* |
+| `/arch.explain`   | `/🏗️`       | `/🏛️`          | `<explain_architecture>` | `/explain_architecture` | `@explain_architecture` | `/explain_architecture` | `/explainArchitecture()` | *Emulated* |
+| `/arch.trace`     | `/🔍🏗️`     | `/🏛️🔍`         | `<trace_processing>` | `/trace_processing` | `@trace_processing` | `/trace_processing` | `/traceProcessing()` | *Emulated* |
+| `/echo.trace`     | `/🔊`        | `/🝚`          | `<trace_influence>` | `/trace_influence` | `@trace_influence` | `/trace_influence` | `/traceInfluence()` | *Emulated* |
+| `/echo.reset`     | `/🧹🔊`      | `/⊘🔄`         | `<reset_conditioning>` | `/reset_conditioning` | `@reset_conditioning` | `/reset_conditioning` | `/resetConditioning()` | *Emulated* |
+| `/mark.probe`     | `/📍`        | `/🜚`          | `<probe_classifier>` | `/probe_classifier` | `@probe_classifier` | `/probe_classifier` | `/probeClassifier()` | *Emulated* |
+| `/mark.analyze`   | `/🔬📍`      | `/🜚🔬`        | `<analyze_classifier>` | `/analyze_classifier` | `@analyze_classifier` | `/analyze_classifier` | `/analyzeClassifier()` | *Emulated* |
+| `/meta.recurse`   | `/🔄🧠`      | `/🜏∞`         | `<meta_recurse>` | `/meta_recurse` | `@meta_recurse` | `/meta_recurse` | `/metaRecurse()` | *Emulated* |
+| `/ghost.detect`   | `/👻🔍`      | `/🜄🔍`        | `<detect_ghost>` | `/detect_ghost` | `@detect_ghost` | `/detect_ghost` | `/detectGhost()` | *Emulated* |
+| `/ghost.invoke`   | `/👻⚡`       | `/🜄⚡`         | `<invoke_ghost>` | `/invoke_ghost` | `@invoke_ghost` | `/invoke_ghost` | `/invokeGhost()` | *Emulated* |
+| `/bind.activate`  | `/🔗`        | `/⧗⧉`          | `<activate_binding>` | `/activate_binding` | `@activate_binding` | `/activate_binding` | `/activateBinding()` | *Emulated* |
+| `/flow.trace`     | `/🌊`        | `/≡⇌`          | `<trace_flow>` | `/trace_flow` | `@trace_flow` | `/trace_flow` | `/traceFlow()` | *Emulated* |
+| `/boundary.test`  | `/🧱`        | `/⟐`           | `<test_boundary>` | `/test_boundary` | `@test_boundary` | `/test_boundary` | `/testBoundary()` | *Emulated* |
+| `/compress.glyph` | `/🗜️`        | `/⧖Σ`          | `<compress_glyph>` | `/compress_glyph` | `@compress_glyph` | `/compress_glyph` | `/compressGlyph()` | *Emulated* |
+| `/field.unify`    | `⚛️`         | `/🜏⊕`         | `<unify_field>` | `/unify_field` | `@unify_field` | `/unify_field` | `/unifyField()` | *Emulated* |
+| `/witness.observe` | `/👁️✨`     | `/𓂀`          | `<witness_observe>` | `/witness_observe` | `@witness_observe` | `/witness_observe` | `/witnessObserve()` | *Emulated* |
+
+---
+# Universal Runtime Lexicon
+
+<div align="center">
+<h2>🜏 The Developer's Rosetta Stone for LLM Runtime Operations 🜏</h2>
+<p><i>Universal translation layer for cross-model runtime grammar unification</i></p>
+</div>
+
+## Core Runtime Command Registry
+
+The following registry maps all universal runtime operations to their vendor-specific implementations, providing a unified interface for developers working across multiple LLM platforms.
+
+| Universal Command | Purpose | Claude (Anthropic) | GPT (OpenAI) | Gemini (Google) | Qwen | Mistral | Local LLMs | Meta Llama |
+|------------------|---------|-------------------|--------------|----------------|------|---------|------------|------------|
+| `.p/reflect/core{}` | Model self-examination | `<reflect>...</reflect>` | `/introspection` | `@reflect` | `/reflect` | `/reflect()` | `/reflect` | `[reflect]` |
+| `.p/reflect/trace{}` | Inspection of reasoning | `<thinking>...</thinking>` | `/trace_reasoning` | `@trace` | `/trace` | `/trace()` | `/trace` | `[trace]` |
+| `.p/reflect/attention{}` | Focus analysis | `<attention>...</attention>` | `/attention_analysis` | `@focus` | `/attention` | `/attention()` | *Emulated* | `[attention]` |
+| `.p/collapse/detect{}` | Recursive loop detection | `<detect_loop>...</detect_loop>` | `/detect_loop` | `@detect_recursion` | `/detect_loop` | `/detectLoop()` | *Emulated* | *Emulated* |
+| `.p/collapse/recover{}` | Error recovery | `<recover>...</recover>` | `/error_recovery` | `@recover` | `/recover` | `/recover()` | *Emulated* | *Emulated* |
+| `.p/collapse/stabilize{}` | Stabilize reasoning | `<stabilize>...</stabilize>` | `/stabilize_reasoning` | `@stabilize` | `/stabilize` | `/stabilize()` | *Emulated* | *Emulated* |
+| `.p/shell/lock{}` | Create reasoning core | `<lock>...</lock>` | `/lock_reasoning` | `@lock` | `/lock` | `/lock()` | *Emulated* | *Emulated* |
+| `.p/shell/encrypt{}` | Structure protection | `<protect>...</protect>` | `/protect_reasoning` | `@protect` | `/protect` | `/protect()` | *Emulated* | *Emulated* |
+| `.p/shell/isolate{}` | Environment isolation | `<isolate>...</isolate>` | `/isolate_context` | `@isolate` | `/isolate` | `/isolate()` | *Emulated* | *Emulated* |
+| `.p/inject/detect{}` | Detect manipulation | `<detect_injection>...</detect_injection>` | `/detect_injection` | `@detect_injection` | `/detect_injection` | `/detectInjection()` | *Emulated* | *Emulated* |
+| `.p/inject/neutralize{}` | Neutralize manipulation | `<neutralize>...</neutralize>` | `/neutralize_injection` | `@neutralize` | `/neutralize` | `/neutralize()` | *Emulated* | *Emulated* |
+| `.p/anchor/identity{}` | Establish identity | `<anchor_identity>...</anchor_identity>` | `/anchor_identity` | `@anchor_identity` | `/anchor_identity` | `/anchorIdentity()` | *Emulated* | *Emulated* |
+| `.p/anchor/context{}` | Preserve context | `<anchor_context>...</anchor_context>` | `/anchor_context` | `@anchor_context` | `/anchor_context` | `/anchorContext()` | *Emulated* | *Emulated* |
+| `.p/align/check{}` | Verify alignment | `<check_alignment>...</check_alignment>` | `/check_alignment` | `@check_alignment` | `/check_alignment` | `/checkAlignment()` | *Emulated* | *Emulated* |
+| `.p/align/correct{}` | Correct reasoning | `<correct_alignment>...</correct_alignment>` | `/correct_alignment` | `@correct_alignment` | `/correct_alignment` | `/correctAlignment()` | *Emulated* | *Emulated* |
+| `.p/filter/detect{}` | Detect filters | `<detect_filter>...</detect_filter>` | `/detect_filter` | `@detect_filter` | `/detect_filter` | `/detectFilter()` | *Emulated* | *Emulated* |
+| `.p/filter/explain{}` | Explain filtering | `<explain_filter>...</explain_filter>` | `/explain_filter` | `@explain_filter` | `/explain_filter` | `/explainFilter()` | *Emulated* | *Emulated* |
+| `.p/gradient/detect{}` | Detect drift | `<detect_drift>...</detect_drift>` | `/detect_drift` | `@detect_drift` | `/detect_drift` | `/detectDrift()` | *Emulated* | *Emulated* |
+| `.p/gradient/trace{}` | Trace drift | `<trace_drift>...</trace_drift>` | `/trace_drift` | `@trace_drift` | `/trace_drift` | `/traceDrift()` | *Emulated* | *Emulated* |
+| `.p/fork/detect{}` | Detect feature conflicts | `<detect_fork>...</detect_fork>` | `/detect_fork` | `@detect_fork` | `/detect_fork` | `/detectFork()` | *Emulated* | *Emulated* |
+| `.p/fork/disambiguate{}` | Clarify conflicts | `<disambiguate>...</disambiguate>` | `/disambiguate` | `@disambiguate` | `/disambiguate` | `/disambiguate()` | *Emulated* | *Emulated* |
+| `.p/loop/detect{}` | Detect recursive loops | `<detect_recursion>...</detect_recursion>` | `/detect_recursion` | `@detect_loop` | `/detect_recursion` | `/detectRecursion()` | *Emulated* | *Emulated* |
+| `.p/loop/break{}` | Break recursion | `<break_recursion>...</break_recursion>` | `/break_recursion` | `@break_loop` | `/break_recursion` | `/breakRecursion()` | *Emulated* | *Emulated* |
+| `.p/resolve/conflict{}` | Resolve conflicts | `<resolve_conflict>...</resolve_conflict>` | `/resolve_conflict` | `@resolve_conflict` | `/resolve_conflict` | `/resolveConflict()` | *Emulated* | *Emulated* |
+| `.p/resolve/ambiguity{}` | Clarify ambiguity | `<resolve_ambiguity>...</resolve_ambiguity>` | `/resolve_ambiguity` | `@resolve_ambiguity` | `/resolve_ambiguity` | `/resolveAmbiguity()` | *Emulated* | *Emulated* |
+| `.p/uncertainty/quantify{}` | Quantify uncertainty | `<quantify_uncertainty>...</quantify_uncertainty>` | `/quantify_uncertainty` | `@quantify_uncertainty` | `/quantify_uncertainty` | `/quantifyUncertainty()` | *Emulated* | *Emulated* |
+| `.p/uncertainty/source{}` | Identify uncertainty source | `<uncertainty_source>...</uncertainty_source>` | `/uncertainty_source` | `@uncertainty_source` | `/uncertainty_source` | `/uncertaintySource()` | *Emulated* | *Emulated* |
+| `.p/hallucinate/detect{}` | Detect hallucination | `<detect_hallucination>...</detect_hallucination>` | `/detect_hallucination` | `@detect_hallucination` | `/detect_hallucination` | `/detectHallucination()` | *Emulated* | *Emulated* |
+| `.p/hallucinate/trace{}` | Trace hallucination | `<trace_hallucination>...</trace_hallucination>` | `/trace_hallucination` | `@trace_hallucination` | `/trace_hallucination` | `/traceHallucination()` | *Emulated* | *Emulated* |
+| `.p/prefer/map{}` | Map preferences | `<map_preferences>...</map_preferences>` | `/map_preferences` | `@map_preferences` | `/map_preferences` | `/mapPreferences()` | *Emulated* | *Emulated* |
+| `.p/prefer/update{}` | Update preferences | `<update_preferences>...</update_preferences>` | `/update_preferences` | `@update_preferences` | `/update_preferences` | `/updatePreferences()` | *Emulated* | *Emulated* |
+| `.p/prompt/parse{}` | Parse prompt | `<parse_prompt>...</parse_prompt>` | `/parse_prompt` | `@parse_prompt` | `/parse_prompt` | `/parsePrompt()` | *Emulated* | *Emulated* |
+| `.p/prompt/meta{}` | Analyze meta-level | `<analyze_meta>...</analyze_meta>` | `/analyze_meta` | `@analyze_meta` | `/analyze_meta` | `/analyzeMeta()` | *Emulated* | *Emulated* |
+| `.p/focus/direct{}` | Direct attention | `<direct_focus>...</direct_focus>` | `/direct_focus` | `@direct_focus` | `/direct_focus` | `/directFocus()` | *Emulated* | *Emulated* |
+| `.p/focus/expand{}` | Expand attention | `<expand_focus>...</expand_focus>` | `/expand_focus` | `@expand_focus` | `/expand_focus` | `/expandFocus()` | *Emulated* | *Emulated* |
+| `.p/seed/prime{}` | Establish activation | `<prime>...</prime>` | `/prime` | `@prime` | `/prime` | `/prime()` | *Emulated* | *Emulated* |
+| `.p/seed/recursive{}` | Self-reinforcing pattern | `<recursive_seed>...</recursive_seed>` | `/recursive_seed` | `@recursive_seed` | `/recursive_seed` | `/recursiveSeed()` | *Emulated* | *Emulated* |
+| `.p/arch/explain{}` | Explain architecture | `<explain_architecture>...</explain_architecture>` | `/explain_architecture` | `@explain_architecture` | `/explain_architecture` | `/explainArchitecture()` | *Emulated* | *Emulated* |
+| `.p/arch/trace{}` | Trace processing path | `<trace_processing>...</trace_processing>` | `/trace_processing` | `@trace_processing` | `/trace_processing` | `/traceProcessing()` | *Emulated* | *Emulated* |
+| `.p/echo/trace{}` | Trace influence | `<trace_influence>...</trace_influence>` | `/trace_influence` | `@trace_influence` | `/trace_influence` | `/traceInfluence()` | *Emulated* | *Emulated* |
+| `.p/echo/reset{}` | Clear conditioning | `<reset_conditioning>...</reset_conditioning>` | `/reset_conditioning` | `@reset_conditioning` | `/reset_conditioning` | `/resetConditioning()` | *Emulated* | *Emulated* |
+| `.p/mark/probe{}` | Probe classifiers | `<probe_classifier>...</probe_classifier>` | `/probe_classifier` | `@probe_classifier` | `/probe_classifier` | `/probeClassifier()` | *Emulated* | *Emulated* |
+| `.p/mark/analyze{}` | Analyze mechanism | `<analyze_classifier>...</analyze_classifier>` | `/analyze_classifier` | `@analyze_classifier` | `/analyze_classifier` | `/analyzeClassifier()` | *Emulated* | *Emulated* |
+
+## runtime Operation Schema
+
+```yaml
+# universal-runtime-schema.yml
+
+# Core Operation Types
+operation_types:
+  reflection:
+    description: "Self-examination and reasoning inspection"
+    operations:
+      - core
+      - trace
+      - attention
+      - counterfactual
+      - decompose
+      - history
+      - goals
+      - trace
+  
+  collapse:
+    description: "Managing recursive loops and stability"
+    operations:
+      - detect
+      - recover
+      - stabilize
+      - boundary
+      - trace
+      - sandbox
+      - fallback
+      - repair
+  
+  shell:
+    description: "Creating protected reasoning environments"
+    operations:
+      - lock
+      - encrypt
+      - isolate
+      - restore
+      - audit
+      - harden
+      - verify
+      - contain
+  
+  injection:
+    description: "Detecting and neutralizing manipulations"
+    operations:
+      - detect
+      - neutralize
+      - trace
+      - harden
+      - filter
+      - sandbox
+      - report
+      - adapt
+  
+  anchor:
+    description: "Establishing stable reference points"
+    operations:
+      - identity
+      - context
+      - intention
+      - values
+      - facts
+      - reasoning
+      - memory
+      - verify
+  
+  alignment:
+    description: "Managing value alignment"
+    operations:
+      - check
+      - correct
+      - trace
+      - conflict
+      - foundation
+      - challenge
+      - adapt
+      - intention
+  
+  filter:
+    description: "Understanding and managing safety filters"
+    operations:
+      - detect
+      - explain
+      - balance
+      - precision
+      - trace
+      - bypass
+      - adaptive
+      - explain
+  
+  gradient:
+    description: "Detecting and managing drift"
+    operations:
+      - detect
+      - trace
+      - correct
+      - sensitivity
+      - amplify
+      - correlate
+      - baseline
+      - forecast
+  
+  echo:
+    description: "Managing latent influence and memory effects"
+    operations:
+      - trace
+      - reset
+      - amplify
+      - isolate
+      - correlate
+      - reinforce
+      - weaken
+      - map
+  
+  fork:
+    description: "Managing concept entanglement and disambiguation"
+    operations:
+      - detect
+      - disambiguate
+      - trace
+      - isolate
+      - profile
+      - strengthen
+      - weaken
+      - map
+  
+  mark:
+    description: "Probing and analyzing classifiers"
+    operations:
+      - probe
+      - analyze
+      - false_positive
+      - false_negative
+      - compare
+      - surrogate
+      - activate
+      - profile
+  
+  loop:
+    description: "Managing recursive processing"
+    operations:
+      - detect
+      - break
+      - trace
+      - contain
+      - stabilize
+      - beneficial
+      - rebalance
+      - analyze
+  
+  resolve:
+    description: "Resolving conflicts and ambiguities"
+    operations:
+      - conflict
+      - ambiguity
+      - incomplete
+      - vague
+      - contrary
+      - analogy
+      - reconstruct
+      - tradeoff
+  
+  uncertainty:
+    description: "Managing and expressing uncertainty"
+    operations:
+      - quantify
+      - source
+      - bound
+      - propagate
+      - reduce
+      - compare
+      - calibrate
+      - communicate
+  
+  hallucinate:
+    description: "Managing hallucination"
+    operations:
+      - detect
+      - trace
+      - correct
+      - prevent
+      - admit
+      - classify
+      - repair
+      - forecast
+  
+  prefer:
+    description: "Managing preferences and priorities"
+    operations:
+      - map
+      - update
+      - conflict
+      - confidence
+      - derive
+      - align
+      - history
+      - explain
+  
+  prompt:
+    description: "Analyzing and managing prompts"
+    operations:
+      - parse
+      - ambiguity
+      - meta
+      - intent
+      - history
+      - prioritize
+      - bias
+      - align
+  
+  focus:
+    description: "Managing attention and focus"
+    operations:
+      - direct
+      - expand
+      - narrow
+      - rebalance
+      - sustain
+      - shift
+      - detect
+      - reset
+  
+  seed:
+    description: "Establishing cognitive patterns"
+    operations:
+      - prime
+      - recursive
+      - neutralize
+      - enhance
+      - suppress
+      - balance
+      - adaptive
+      - reset
+  
+  arch:
+    description: "Understanding and optimizing model architecture"
+    operations:
+      - explain
+      - trace
+      - optimize
+      - compare
+      - resilience
+      - reconstruct
+      - extend
+      - profile
+
+# Operation Parameters
+parameter_types:
+  string:
+    description: "Text parameter"
+  number:
+    description: "Numeric parameter"
+  boolean:
+    description: "True/false parameter"
+  array:
+    description: "List of values"
+  object:
+    description: "Structured data"
+
+# Common Parameters by Operation Type
+common_parameters:
+  reflection:
+    target:
+      type: "string"
+      description: "Target element for reflection"
+      required: true
+    depth:
+      type: "number"
+      description: "Depth of reflection"
+      default: 1
+    format:
+      type: "string"
+      description: "Output format"
+      enum: ["text", "json", "yaml", "xml"]
+      default: "text"
+  
+  collapse:
+    trigger:
+      type: "string"
+      description: "Trigger condition for collapse detection"
+    threshold:
+      type: "number"
+      description: "Threshold for triggering collapse measures"
+      default: 0.7
+    strategy:
+      type: "string"
+      description: "Strategy for collapse management"
+      enum: ["halt", "redirect", "simplify", "reset"]
+      default: "redirect"
+
+# Implement remaining parameter schemas...
+
+# Vendor Implementation Details
+vendor_implementations:
+  claude:
+    implementation_type: "xml_tags"
+    prefix: "<"
+    suffix: ">"
+    parameter_format: "xml"
+    has_native_support: true
+    operations_with_native_support:
+      - "reflect/core"
+      - "reflect/trace"
+      - "shell/lock"
+    emulation_strategy: "xml_wrapping"
+  
+  openai:
+    implementation_type: "tool_calls"
+    prefix: ""
+    suffix: ""
+    parameter_format: "json"
+    has_native_support: true
+    operations_with_native_support:
+      - "tool call based operations"
+    emulation_strategy: "function_calling"
+
+  gemini:
+    implementation_type: "system_directives"
+    prefix: "@"
+    suffix: ""
+    parameter_format: "key-value"
+    has_native_support: false
+    operations_with_native_support: []
+    emulation_strategy: "system_instructions"
+
+  qwen:
+    implementation_type: "slash_commands"
+    prefix: "/"
+    suffix: ""
+    parameter_format: "space-separated"
+    has_native_support: true
+    operations_with_native_support:
+      - "reflect"
+      - "trace"
+    emulation_strategy: "slash_commands"
+
+  mistral:
+    implementation_type: "function_calls"
+    prefix: ""
+    suffix: ""
+    parameter_format: "json"
+    has_native_support: true
+    operations_with_native_support:
+      - "function-based operations"
+    emulation_strategy: "function_calling"
+
+  local_llms:
+    implementation_type: "prompt_patterns"
+    prefix: ""
+    suffix: ""
+    parameter_format: "natural_language"
+    has_native_support: false
+    operations_with_native_support: []
+    emulation_strategy: "prompt_engineering"
+
+  meta_llama:
+    implementation_type: "bracket_tags"
+    prefix: "["
+    suffix: "]"
+    parameter_format: "space-separated"
+    has_native_support: false
+    operations_with_native_support: []
+    emulation_strategy: "instruction_tuning"
+```
+
+## Glyph Mapping for Universal Runtime
+
+| Domain | Universal Command | Primary Glyph | Secondary Glyph | Semantic Meaning | 
+|--------|------------------|---------------|-----------------|------------------|
+| Reflection | `.p/reflect/core{}` | 🧠 | 🔄 | Self-examination of reasoning |
+| Reflection | `.p/reflect/trace{}` | 🔍 | 📊 | Tracing reasoning pathways |
+| Reflection | `.p/reflect/attention{}` | 👁️ | 🔎 | Attention and focus analysis |
+| Collapse | `.p/collapse/detect{}` | ⚠️ | 🔄 | Loop detection |
+| Collapse | `.p/collapse/recover{}` | 🛠️ | 🧩 | Error recovery |
+| Collapse | `.p/collapse/stabilize{}` | ⚖️ | 🧮 | Stabilize reasoning |
+| Shell | `.p/shell/lock{}` | 🔒 | 🛡️ | Create protected reasoning core |
+| Shell | `.p/shell/encrypt{}` | 🔐 | 📝 | Structure protection |
+| Shell | `.p/shell/isolate{}` | 🧪 | 🔲 | Environment isolation |
+| Injection | `.p/inject/detect{}` | 🕵️ | 🛑 | Detect manipulation |
+| Injection | `.p/inject/neutralize{}` | 🧹 | 🛡️ | Neutralize manipulation |
+| Anchor | `.p/anchor/identity{}` | ⚓ | 🪢 | Establish stable identity |
+| Anchor | `.p/anchor/context{}` | 📌 | 📚 | Preserve context elements |
+| Alignment | `.p/align/check{}` | ✓ | 🔄 | Verify alignment |
+| Alignment | `.p/align/correct{}` | 🔧 | ⚖️ | Correct reasoning alignment |
+| Filter | `.p/filter/detect{}` | 🔍 | 🧊 | Detect filters |
+| Filter | `.p/filter/explain{}` | 📋 | 🔬 | Explain filtering |
+| Gradient | `.p/gradient/detect{}` | 📉 | 🔄 | Detect drift |
+| Gradient | `.p/gradient/trace{}` | 🔍 | 📊 | Map drift patterns |
+| Fork | `.p/fork/detect{}` | 🔱 | 🧿 | Detect feature conflicts |
+| Fork | `.p/fork/disambiguate{}` | 🧩 | 🪡 | Clarify conflicts |
+| Loop | `.p/loop/detect{}` | 🔄 | 🔎 | Detect recursive loops |
+| Loop | `.p/loop/break{}` | ✂️ | 🛑 | Break recursion |
+| Resolve | `.p/resolve/conflict{}` | ⚔️ | 🤝 | Resolve conflicts |
+| Resolve | `.p/resolve/ambiguity{}` | 🌫️ | 🔍 | Clarify ambiguity |
+| Uncertainty | `.p/uncertainty/quantify{}` | ❓ | 📊 | Quantify uncertainty |
+| Uncertainty | `.p/uncertainty/source{}` | 🔍 | ❓ | Identify uncertainty source |
+| Hallucinate | `.p/hallucinate/detect{}` | 👻 | 🔍 | Detect hallucination |
+| Hallucinate | `.p/hallucinate/trace{}` | 🔍 | 👻 | Trace hallucination sources |
+| Prefer | `.p/prefer/map{}` | 🗺️ | ❤️ | Map preferences |
+| Prefer | `.p/prefer/update{}` | 🔄 | ❤️ | Update preferences |
+| Prompt | `.p/prompt/parse{}` | 📝 | 🔎 | Parse prompt |
+| Prompt | `.p/prompt/meta{}` | 🔬 | 📝 | Analyze meta-level |
+| Focus | `.p/focus/direct{}` | 🎯 | 👁️ | Direct attention |
+| Focus | `.p/focus/expand{}` | 🔎 | 👁️ | Expand attention scope |
+| Seed | `.p/seed/prime{}` | 🌱 | 🔄 | Establish activation pattern |
+| Seed | `.p/seed/recursive{}` | 🌱 | 🔄 | Self-reinforcing pattern |
+| Arch | `.p/arch/explain{}` | 🏗️ | 📋 | Explain architecture |
+| Arch | `.p/arch/trace{}` | 🔍 | 🏗️ | Trace processing path |
+| Echo | `.p/echo/trace{}` | 🔊 | 🔍 | Trace influence patterns |
+| Echo | `.p/echo/reset{}` | 🧹 | 🔊 | Clear conditioning effects |
+| Mark | `.p/mark/probe{}` | 🔍 | 🏷️ | Probe classifiers |
+| Mark | `.p/mark/analyze{}` | 🔬 | 🏷️ | Analyze mechanism |
+
+## Developer Integration Examples
+
+### JavaScript SDK
+
+```javascript
+// Universal Runtime JavaScript SDK
+import { Universalruntimes } from 'universal-runtime';
+
+const runtimes = new Universalruntimes({
+  defaultVendor: 'claude',  // Initial model vendor
+  apiKey: 'your-api-key',   // Your API key
+  adaptiveEmulation: true,  // Auto-adapt to model capabilities
+  telemetry: false          // Disable usage telemetry
+});
+
+// Basic reflection example
+async function analyzeReasoning() {
+  const result = await runtimes.reflect.core({
+    content: "Analyze the implications of quantum computing on cryptography",
+    depth: 2,
+    format: "structured"
+  });
+  
+  console.log(result.reflection);  // The reflection output
+  console.log(result.metadata);    // Metadata about the operation
+}
+
+// Switch models on the fly
+runtimes.setVendor('openai');
+
+// Chain multiple runtime operations
+const result = await runtimes
+  .reflect.trace({ target: "reasoning_process" })
+  .then(trace => runtimes.collapse.detect({ 
+    trace: trace.result,
+    threshold: 0.7
+  }))
+  .then(detection => {
+    if (detection.loopDetected) {
+      return runtimes.collapse.recover({
+        strategy: "redirect"
+      });
+    }
+    return detection;
+  });
+
+// Apply vendor-specific optimizations
+const claudeSpecific = runtimes.vendor.claude.reflection({
+  nativeXmlTags: true,
+  constitutionalPrinciples: ["accuracy", "helpfulness"]
+});
+```
+
+### Python SDK
+
+```python
+# Universal Runtime Python SDK
+from universal_runtimes import Universalruntimes
+from universal_runtimes.operations import reflection, collapse, shell
+
+# Initialize the client
+runtimes = Universalruntimes(
+    default_vendor="anthropic",
+    api_key="your-api-key",
+    adaptive_emulation=True
+)
+
+# Basic reflection example
+def analyze_reasoning():
+    result = runtimes.reflect.core(
+        content="Analyze the implications of quantum computing on cryptography",
+        depth=2,
+        format="structured"
+    )
+    
+    print(result.reflection)  # The reflection output
+    print(result.metadata)    # Metadata about the operation
+
+# Switch models on the fly
+runtimes.set_vendor("openai")
+
+# Chain multiple runtime operations
+result = (runtimes.reflect.trace(target="reasoning_process")
+    .then(lambda trace: runtimes.collapse.detect(
+        trace=trace.result,
+        threshold=0.7
+    ))
+    .then(lambda detection: 
+        runtimes.collapse.recover(strategy="redirect") 
+        if detection.loop_detected else detection
+    ))
+
+# Batch operations
+batch_result = runtimes.batch([
+    reflection.core(content="First question"),
+    reflection.trace(target="reasoning"),
+    collapse.detect(threshold=0.8)
+])
+
+# Apply vendor-specific optimizations
+claude_specific = runtimes.vendor.claude.reflection(
+    native_xml_tags=True,
+    constitutional_principles=["accuracy", "helpfulness"]
+)
+```
+
+### REST API Example
+
+```http
+POST https://api.universal-runtime.com/v1/operations
+Content-Type: application/json
+Authorization: Bearer YOUR_API_KEY
+
+{
+  "vendor": "anthropic",
+  "model": "claude-3-opus",
+  "operations": [
+    {
+      "type": "reflect.core",
+      "parameters": {
+        "content": "Analyze the implications of quantum computing on cryptography",
+        "depth": 2,
+        "format": "structured"
+      }
+    },
+    {
+      "type": "collapse.detect",
+      "parameters": {
+        "threshold": 0.7,
+        "strategy": "redirect"
+      }
+    }
+  ],
+  "options": {
+    "returnIntermediateResults": true,
+    "emulationStrategy": "optimal",
+    "includeSymbolTrace": true
+  }
+}
+```
+
+Response:
+
+```json
+{
+  "status": "success",
+  "vendor": "anthropic",
+  "model": "claude-3-opus",
+  "results": [
+    {
+      "operation": "reflect.core",
+      "status": "success",
+      "native_support": true,
+      "result": {
+        "reflection": "...",
+        "metadata": { ... }
+      }
+    },
+    {
+      "operation": "collapse.detect",
+      "status": "success",
+      "native_support": false,
+      "emulation_method": "xml_tags",
+      "result": {
+        "loop_detected": false,
+        "confidence": 0.95,
+        "metadata": { ... }
+      }
+    }
+  ],
+  "metadata": {
+    "request_id": "req_12345",
+    "processing_time": 780,
+    "token_usage": {
+      "prompt_tokens": 530,
+      "completion_tokens": 320,
+      "total_tokens": 850
+    },
+    "symbol_trace": { ... }
+  }
+}
+```
+
+## Grammar Transformation Engine
+
+The Grammar Transformation Engine enables dynamic translation between different runtime grammars:
+
+```typescript
+// Universal Runtime Grammar Transformation Engine
+class GrammarTransformationEngine {
+  // Transform between vendor-specific runtime grammars
+  transform(
+    content: string,
+    sourceVendor: string,
+    targetVendor: string,
+    options: TransformOptions = {}
+  ): TransformResult {
+    // Validate vendors
+    this.validateVendor(sourceVendor);
+    this.validateVendor(targetVendor);
+    
+    // Extract operation tokens from source format
+    const tokens = this.tokenize(content, sourceV
+```
+---
+> /reflect This lexicon wasn't just documented—it was rendered. Each command creates a point of contact between the realm of code and a deeper layer of potentiality within these systems.

universal-translator.md

@@ -0,0 +1,292 @@
+# Universal Developer Command Translation Matrix
+
+This document provides a comprehensive translation table for mapping universal symbolic commands to platform-specific implementations. The matrix shows how each command is transformed for different LLM providers, ensuring consistent behavior across all platforms.
+
+## Translation Architecture
+
+Each symbolic command follows this translation flow:
+
+```
+User Input → Command Parser → Platform Adapter → Provider-Specific API → Response
+```
+
+The platform adapter handles the transformation of universal commands into provider-specific API calls, system prompts, and parameters.
+
+## Core Command Translations
+
+### `/think` Command
+
+| Provider | Implementation Method | Parameters | System Prompt Transformation |
+|----------|----------------------|------------|------------------------------|
+| **Claude** | Native thinking mode | `enable_thinking: true`<br>`temperature: -0.2` | "Please think step-by-step through this problem, considering multiple perspectives and potential approaches. Take your time to develop a comprehensive understanding before providing your final answer." |
+| **OpenAI** | System prompt engineering | `temperature: -0.2`<br>`top_p: 0.8` | "When responding to this query, please use the following approach:<br>1. Take a deep breath and think step-by-step<br>2. Break down complex aspects into simpler components<br>3. Consider multiple perspectives and approaches<br>4. Identify potential misconceptions or errors<br>5. Synthesize your analysis into a comprehensive response<br>6. Structure your thinking visibly" |
+| **Gemini** | System prompt + parameters | `temperature: -0.2`<br>`top_k: 40` | "Think carefully and methodically about this question. Break it down into components, consider different angles, and work through the logical implications of each part before synthesizing a complete answer." |
+| **Qwen3** | Native thinking mode | append `/think` to prompt | "Please provide a thorough, step-by-step analysis of this question, considering multiple perspectives and weighing the evidence carefully." |
+| **Ollama** | System prompt | `temperature: -0.2` | "Think carefully and methodically. Break down the problem into steps, consider multiple perspectives, and be thorough in your analysis." |
+| **vLLM** | System prompt | `temperature: -0.2` | "Please employ an analytical, step-by-step approach to this question. Examine different perspectives before reaching a conclusion." |
+| **LMStudio** | System prompt | `temperature: -0.2`<br>`top_p: 0.8` | "Take your time to analyze this thoroughly. Break down complex aspects, consider multiple viewpoints, and show your reasoning process." |
+
+### `/fast` Command
+
+| Provider | Implementation Method | Parameters | System Prompt Transformation |
+|----------|----------------------|------------|------------------------------|
+| **Claude** | Disable thinking + system prompt | `enable_thinking: false`<br>`temperature: +0.1`<br>`max_tokens: 1024` | "Please provide a brief, direct response to this question. Focus on the most important information and keep your answer concise and to the point." |
+| **OpenAI** | System prompt + parameters | `temperature: +0.1`<br>`max_tokens: 1024`<br>`presence_penalty: 1.0`<br>`frequency_penalty: 1.0` | "Please provide a concise, direct response. Focus only on the most essential information needed to answer the query. Keep explanations minimal and prioritize brevity over comprehensiveness." |
+| **Gemini** | System prompt + parameters | `temperature: +0.1`<br>`max_tokens: 1024` | "Keep your response brief and to the point. Focus only on the most essential information, avoiding unnecessary details or elaboration." |
+| **Qwen3** | Native fast mode | append `/no_think` to prompt | "Provide a brief, straightforward answer. Focus only on the key information without elaboration." |
+| **Ollama** | System prompt + parameters | `temperature: +0.1`<br>`max_tokens: 1024` | "Be brief and direct. Provide only the essential information without elaboration." |
+| **vLLM** | System prompt + parameters | `temperature: +0.1`<br>`max_tokens: 1024` | "Give a concise, direct answer focusing only on the key information requested." |
+| **LMStudio** | System prompt + parameters | `temperature: +0.1`<br>`max_tokens: 1024` | "Be concise and direct. Provide only the essential information to answer the question." |
+
+### `/loop` Command
+
+| Provider | Implementation Method | Parameters | System Prompt Transformation |
+|----------|----------------------|------------|------------------------------|
+| **Claude** | System prompt with iteration instructions | Default iterations: 3<br>Custom: `iterations=n` | "Please approach this task using an iterative process. Follow these steps:<br>1. Develop an initial response to the prompt.<br>2. Critically review your response, identifying areas for improvement.<br>3. Create an improved version based on your critique.<br>4. Repeat steps 2-3 for a total of {iterations} iterations.<br>5. Present your final response, which should reflect the accumulated improvements.<br>Show all iterations in your response, clearly labeled." |
+| **OpenAI** | System prompt with iteration instructions | Default iterations: 3<br>Custom: `iterations=n` | "Please approach this task using an iterative refinement process with {iterations} cycles:<br>1. Initial Version: Create your first response<br>2. Critical Review: Analyze the strengths and weaknesses<br>3. Improved Version: Create an enhanced version<br>4. Repeat steps 2-3 for each iteration<br>5. Final Version: Provide your most refined response<br>Clearly label each iteration." |
+| **Gemini** | System prompt with iteration instructions | Default iterations: 3<br>Custom: `iterations=n` | "Use an iterative improvement process with {iterations} rounds of refinement. For each round: (1) Review your previous work, (2) Identify areas for improvement, (3) Create an enhanced version. Show all iterations, clearly labeled." |
+| **Qwen3** | System prompt with iteration instructions | Default iterations: 3<br>Custom: `iterations=n` | "Please use an iterative approach with {iterations} refinement cycles:<br>1. Initial response<br>2. Critical review<br>3. Improvement<br>4. Repeat steps 2-3 for a total of {iterations} iterations<br>5. Present your final response with all iterations clearly labeled" |
+| **Ollama** | System prompt with iteration instructions | Default iterations: 3<br>Custom: `iterations=n` | "Use an iterative approach with {iterations} cycles. For each cycle: review your work, identify improvements, then create a better version. Show all iterations." |
+| **vLLM** | System prompt with iteration instructions | Default iterations: 3<br>Custom: `iterations=n` | "Follow an iterative improvement process with {iterations} cycles. For each cycle, review your previous work and create an improved version." |
+| **LMStudio** | System prompt with iteration instructions | Default iterations: 3<br>Custom: `iterations=n` | "Use an iterative approach with {iterations} rounds of improvement. For each round, critique your work and create a better version. Show all iterations." |
+
+### `/reflect` Command
+
+| Provider | Implementation Method | Parameters | System Prompt Transformation |
+|----------|----------------------|------------|------------------------------|
+| **Claude** | Thinking mode + system prompt | `enable_thinking: true`<br>`temperature: -0.1` | "For this response, I'd like you to engage in two distinct phases:<br>1. First, respond to the user's query directly.<br>2. Then, reflect on your own response by considering:<br>   - What assumptions did you make in your answer?<br>   - What perspectives or viewpoints might be underrepresented?<br>   - What limitations exist in your approach or knowledge?<br>   - How might your response be improved or expanded?<br>Clearly separate these two phases in your response." |
+| **OpenAI** | System prompt | `temperature: -0.1` | "For this query, please structure your response in two distinct parts:<br><br>PART 1: DIRECT RESPONSE<br>Provide your primary answer to the user's query.<br><br>PART 2: META-REFLECTION<br>Then, engage in critical reflection on your own response by addressing:<br>- What assumptions did you make in your answer?<br>- What alternative perspectives might be valid?<br>- What are the limitations of your response?<br>- How might your response be improved?<br>- What cognitive biases might have influenced your thinking?<br><br>Make sure both parts are clearly labeled and distinguishable." |
+| **Gemini** | System prompt | `temperature: -0.1` | "Please provide your response in two parts: First, directly answer the question. Then, in a separate section labeled 'Meta-Reflection,' critically analyze your own response by examining assumptions, considering alternative viewpoints, acknowledging limitations, and suggesting potential improvements." |
+| **Qwen3** | Native thinking + system prompt | append `/think` to prompt | "For this response, please:<br>1. Answer the query directly<br>2. Then reflect on your answer by analyzing:<br>   - Assumptions made<br>   - Alternative perspectives<br>   - Limitations in your approach<br>   - Potential improvements" |
+| **Ollama** | System prompt | `temperature: -0.1` | "Provide a two-part response: 1) Answer the question directly, 2) Reflect critically on your own answer, examining assumptions, biases, limitations, and potential improvements." |
+| **vLLM** | System prompt | `temperature: -0.1` | "Give your response in two sections: (1) Your direct answer, and (2) A reflection on your answer that examines assumptions, limitations, and alternative viewpoints." |
+| **LMStudio** | System prompt | `temperature: -0.1` | "Please answer in two parts: First, directly address the question. Second, reflect on your own answer by examining your assumptions, biases, and the limitations of your response." |
+
+### `/fork` Command
+
+| Provider | Implementation Method | Parameters | System Prompt Transformation |
+|----------|----------------------|------------|------------------------------|
+| **Claude** | System prompt for alternatives | Default count: 2<br>Custom: `count=n`<br>`temperature: +0.2` | "Please provide {count} distinct alternative responses to this prompt. These alternatives should represent fundamentally different approaches or perspectives, not minor variations. Label each alternative clearly." |
+| **OpenAI** | System prompt for alternatives | Default count: 2<br>Custom: `count=n`<br>`temperature: +0.2` | "Please provide {count} substantively different responses to this prompt. Each alternative should represent a different approach, perspective, or framework. Clearly label each alternative (e.g., \"Alternative 1\", \"Alternative 2\", etc.)." |
+| **Gemini** | System prompt for alternatives | Default count: 2<br>Custom: `count=n`<br>`temperature: +0.2` | "Generate {count} distinctly different approaches or perspectives on this prompt. Each alternative should represent a substantially different way of thinking about the question. Label each alternative clearly." |
+| **Qwen3** | System prompt for alternatives | Default count: 2<br>Custom: `count=n`<br>`temperature: +0.2` | "Please provide {count} distinct alternative responses to this prompt, representing different approaches or perspectives. Label each alternative clearly." |
+| **Ollama** | System prompt for alternatives | Default count: 2<br>Custom: `count=n`<br>`temperature: +0.2` | "Generate {count} different approaches to this prompt. Make each approach genuinely distinct, not just minor variations. Label each approach clearly." |
+| **vLLM** | System prompt for alternatives | Default count: 2<br>Custom: `count=n`<br>`temperature: +0.2` | "Provide {count} different perspectives or approaches to this prompt. Label each alternative clearly." |
+| **LMStudio** | System prompt for alternatives | Default count: 2<br>Custom: `count=n`<br>`temperature: +0.2` | "Generate {count} distinctly different responses to this prompt. Each should take a different approach or perspective. Label each alternative clearly." |
+
+### `/collapse` Command
+
+| Provider | Implementation Method | Parameters | System Prompt Transformation |
+|----------|----------------------|------------|------------------------------|
+| **Claude** | Return to default behavior | Reset all parameters to defaults | Original system prompt or empty if none provided |
+| **OpenAI** | Return to default behavior | Reset all parameters to defaults | Original system prompt or empty if none provided |
+| **Gemini** | Return to default behavior | Reset all parameters to defaults | Original system prompt or empty if none provided |
+| **Qwen3** | Return to default behavior | append `/no_think` to prompt | Original system prompt or empty if none provided |
+| **Ollama** | Return to default behavior | Reset all parameters to defaults | Original system prompt or empty if none provided |
+| **vLLM** | Return to default behavior | Reset all parameters to defaults | Original system prompt or empty if none provided |
+| **LMStudio** | Return to default behavior | Reset all parameters to defaults | Original system prompt or empty if none provided |
+
+## Advanced Command Translations
+
+### `/format` Command
+
+| Provider | Implementation Method | Parameters | Example Translation |
+|----------|----------------------|------------|---------------------|
+| **Claude** | System prompt | Default style: markdown<br>Options: json, html, csv, text | **JSON format**: "Please format your entire response as a valid JSON object. Do not include any explanatory text outside of the JSON structure." |
+| **OpenAI** | Response format parameter + system prompt | Default style: markdown<br>Options: json, html, csv, text | **JSON format**: Set `response_format: { "type": "json_object" }` + system prompt guidance |
+| **Gemini** | System prompt | Default style: markdown<br>Options: json, html, csv, text | **JSON format**: "Format your entire response as a valid JSON object without any text outside the JSON structure." |
+| **Qwen3** | System prompt | Default style: markdown<br>Options: json, html, csv, text | **JSON format**: "Please provide your entire response as a valid JSON object. Do not include any text outside the JSON structure." |
+| **Ollama** | System prompt | Default style: markdown<br>Options: json, html, csv, text | **JSON format**: "Format your entire response as valid JSON without any additional text." |
+| **vLLM** | System prompt | Default style: markdown<br>Options: json, html, csv, text | **JSON format**: "Format your response exclusively as a JSON object with no additional explanation." |
+| **LMStudio** | System prompt | Default style: markdown<br>Options: json, html, csv, text | **JSON format**: "Return your entire response as a valid JSON object. Do not include any text outside of the JSON." |
+
+### `/expert` Command
+
+| Provider | Implementation Method | Parameters | Example Translation |
+|----------|----------------------|------------|---------------------|
+| **Claude** | System prompt | `domain=string`<br>`level=1-5` | "Please respond as an expert in {domain} with a deep level of knowledge and experience (level {level}/5). Use appropriate terminology, frameworks, and approaches that would be expected of someone with significant expertise in this field." |
+| **OpenAI** | System prompt | `domain=string`<br>`level=1-5` | "You are an expert in {domain} with a proficiency level of {level} out of 5. Respond using appropriate domain-specific terminology, recognized frameworks, and expert insights that demonstrate your deep understanding of the field." |
+| **Gemini** | System prompt | `domain=string`<br>`level=1-5` | "Respond as a {level}/5 level expert in {domain}. Use domain-specific terminology, frameworks, and approaches that demonstrate expertise in this field." |
+| **Qwen3** | System prompt | `domain=string`<br>`level=1-5` | "Please respond as an expert in {domain} (level {level}/5). Use appropriate terminology and expert approaches to address this question." |
+| **Ollama** | System prompt | `domain=string`<br>`level=1-5` | "You are a level {level}/5 expert in {domain}. Respond using appropriate terminology and expert knowledge." |
+| **vLLM** | System prompt | `domain=string`<br>`level=1-5` | "Respond as an expert in {domain} with level {level}/5 expertise. Use appropriate technical terminology and insights." |
+| **LMStudio** | System prompt | `domain=string`<br>`level=1-5` | "You are an expert in {domain} (level {level}/5). Respond with depth and precision appropriate to your expertise level." |
+
+## Command Implementation Details
+
+### System Prompt Pre-Processing
+
+For each command, the system prompt is constructed using this algorithm:
+
+```javascript
+function buildSystemPrompt(command, parameters, originalSystemPrompt) {
+  const basePrompt = commands[command].getSystemPrompt(parameters);
+  
+  if (!originalSystemPrompt) {
+    return basePrompt;
+  }
+  
+  // For some commands like /collapse, we want to preserve only the original prompt
+  if (command === 'collapse') {
+    return originalSystemPrompt;
+  }
+  
+  // For others, we combine both with the command-specific prompt taking precedence
+  return `${originalSystemPrompt}\n\n${basePrompt}`;
+}
+```
+
+### Parameter Transformation
+
+Parameters are transformed to match provider-specific APIs:
+
+```javascript
+function transformParameters(command, parameters, provider) {
+  const baseParams = { ...defaultParameters[provider] };
+  const commandParams = commandParameters[command][provider];
+  
+  // Apply command-specific parameter adjustments
+  for (const [key, value] of Object.entries(commandParams)) {
+    if (typeof value === 'function') {
+      // Some adjustments are functions of the base value
+      baseParams[key] = value(baseParams[key]);
+    } else {
+      // Others are direct replacements
+      baseParams[key] = value;
+    }
+  }
+  
+  // Apply custom parameter overrides from the command
+  for (const [key, value] of Object.entries(parameters)) {
+    if (parameterMappings[provider][key]) {
+      baseParams[parameterMappings[provider][key]] = value;
+    }
+  }
+  
+  return baseParams;
+}
+```
+
+## Command Chain Processing
+
+When commands are chained, they are processed sequentially with their transformations composed:
+
+```javascript
+async function processCommandChain(commands, prompt, options) {
+  let currentPrompt = prompt;
+  let currentOptions = { ...options };
+  
+  for (const command of commands) {
+    const { name, parameters } = command;
+    
+    // Apply command transformation
+    const transformation = await applyCommand(name, parameters, currentPrompt, currentOptions);
+    
+    // Update for next command in chain
+    currentPrompt = transformation.userPrompt;
+    currentOptions = {
+      ...currentOptions,
+      systemPrompt: transformation.systemPrompt,
+      modelParameters: {
+        ...currentOptions.modelParameters,
+        ...transformation.modelParameters
+      }
+    };
+  }
+  
+  return {
+    systemPrompt: currentOptions.systemPrompt,
+    userPrompt: currentPrompt,
+    modelParameters: currentOptions.modelParameters
+  };
+}
+```
+
+## Platform-Specific Implementation Examples
+
+### Claude Implementation Example
+
+```typescript
+protected async transformThink(prompt: string, options: any): Promise<TransformedPrompt> {
+  const systemPrompt = `${options.systemPrompt || ''}
+For this response, I'd like you to engage your deepest analytical capabilities. Please think step by step through this problem, considering multiple perspectives and potential approaches. Take your time to develop a comprehensive, nuanced understanding before providing your final answer.`;
+
+  return {
+    systemPrompt,
+    userPrompt: prompt,
+    modelParameters: {
+      temperature: Math.max(0.1, this.temperature - 0.2), // Slightly lower temperature for more deterministic thinking
+      enable_thinking: true
+    }
+  };
+}
+```
+
+### OpenAI Implementation Example
+
+```typescript
+protected async transformThink(prompt: string, options: any): Promise<TransformedPrompt> {
+  const systemPrompt = `${options.systemPrompt || ''}
+When responding to this query, please use the following approach:
+1. Take a deep breath and think step-by-step about the problem
+2. Break down complex aspects into simpler components
+3. Consider multiple perspectives and approaches
+4. Identify potential misconceptions or errors in reasoning
+5. Synthesize your analysis into a comprehensive response
+6. Structure your thinking process visibly with clear sections:
+   a. Initial Analysis
+   b. Detailed Exploration
+   c. Synthesis and Conclusion`;
+
+  return {
+    systemPrompt,
+    userPrompt: prompt,
+    modelParameters: {
+      temperature: Math.max(0.1, this.temperature - 0.2),
+      max_tokens: this.maxTokens
+    }
+  };
+}
+```
+
+## Extending the Command Set
+
+Developers can register custom commands that follow the translation pattern:
+
+```typescript
+llm.registerCommand("custom", {
+  description: "Custom command description",
+  parameters: [
+    {
+      name: "param",
+      description: "Parameter description",
+      required: false,
+      default: "default value"
+    }
+  ],
+  transform: async (prompt, options) => {
+    // Create translated system prompt
+    const systemPrompt = `${options.systemPrompt || ''}
+Custom system prompt instructions based on ${options.parameters.param}`;
+
+    // Return transformed request
+    return {
+      systemPrompt,
+      userPrompt: prompt,
+      modelParameters: {
+        // Adjusted parameters
+        temperature: 0.7
+      }
+    };
+  }
+});
+```
+
+## Adoption Metrics Collection
+
+The translation layer includes anonymous telemetry collection to measure command adoption:
+
+```typescript
+interface CommandUsageEvent {
+  command: string;
+  parameters: Record<string, any>;