universal-translator.md

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 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 top_p: 0.8	"When responding to this query, please use the following approach: 1. Take a deep breath and think step-by-step 2. Break down complex aspects into simpler components 3. Consider multiple perspectives and approaches 4. Identify potential misconceptions or errors 5. Synthesize your analysis into a comprehensive response 6. Structure your thinking visibly"
Gemini	System prompt + parameters	temperature: -0.2 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 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 temperature: +0.1 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 max_tokens: 1024 presence_penalty: 1.0 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 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 max_tokens: 1024	"Be brief and direct. Provide only the essential information without elaboration."
vLLM	System prompt + parameters	temperature: +0.1 max_tokens: 1024	"Give a concise, direct answer focusing only on the key information requested."
LMStudio	System prompt + parameters	temperature: +0.1 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 Custom: iterations=n	"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."
OpenAI	System prompt with iteration instructions	Default iterations: 3 Custom: iterations=n	"Please approach this task using an iterative refinement process with {iterations} cycles: 1. Initial Version: Create your first response 2. Critical Review: Analyze the strengths and weaknesses 3. Improved Version: Create an enhanced version 4. Repeat steps 2-3 for each iteration 5. Final Version: Provide your most refined response Clearly label each iteration."
Gemini	System prompt with iteration instructions	Default iterations: 3 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 Custom: iterations=n	"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"
Ollama	System prompt with iteration instructions	Default iterations: 3 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 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 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 temperature: -0.1	"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."
OpenAI	System prompt	temperature: -0.1	"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."
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: 1. Answer the query directly 2. Then reflect on your answer by analyzing: - Assumptions made - Alternative perspectives - Limitations in your approach - 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 Custom: count=n 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 Custom: count=n 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 Custom: count=n 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 Custom: count=n 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 Custom: count=n 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 Custom: count=n temperature: +0.2	"Provide {count} different perspectives or approaches to this prompt. Label each alternative clearly."
LMStudio	System prompt for alternatives	Default count: 2 Custom: count=n 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 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 Options: json, html, csv, text	JSON format: Set response_format: { "type": "json_object" } + system prompt guidance
Gemini	System prompt	Default style: markdown 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 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 Options: json, html, csv, text	JSON format: "Format your entire response as valid JSON without any additional text."
vLLM	System prompt	Default style: markdown 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 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 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 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 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 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 level=1-5	"You are a level {level}/5 expert in {domain}. Respond using appropriate terminology and expert knowledge."
vLLM	System prompt	domain=string 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 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:

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:

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:

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

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

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:

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:

interface CommandUsageEvent {
  command: string;
  parameters: Record<string, any>;