modelshow

Blind multi-model comparison with architecturally guaranteed de-anonymization. Trigger with "mdls" or "modelshow" for double-blind evaluation of AI model responses.

ModelShow — Professional Multi-Model Evaluation

ModelShow provides a sophisticated framework for comparing AI model responses through double-blind evaluation. The system queries multiple models in parallel, anonymizes their outputs, and uses an independent judge model to rank responses purely on merit.

Key Features

• Architecturally Guaranteed De-anonymization: The judge sub-agent automatically de-anonymizes results before returning them—orchestrators never see placeholder labels
• Cryptographic Randomization: Responses are presented to the judge in cryptographically secure random order using secrets.SystemRandom()
• Holistic Judge Analysis: Judges provide both per-model rankings and comprehensive "Overall Assessment" analyzing cross-model patterns
• Intelligent Polling: Automatic progress monitoring with content-free status updates and immediate completion detection
• Professional Output: Formatted results with scores, judge commentary, and actionable insights

Detection

Trigger: Message starts with mdls or modelshow (case-insensitive). Extract the prompt by removing the trigger keyword.

Example: mdls explain quantum entanglement → prompt = explain quantum entanglement

Workflow

text

Step 1  → Acknowledge & Load Configuration
Step 2  → Spawn Parallel Model Agents
Step 3  → Collect Responses with Intelligent Polling
Step 4  → Anonymize with Cryptographic Randomization
Step 5  → Spawn Judge+Deanon Sub-Agent
Step 6  → Parse De-anonymized Results
Step 7  → Build Formatted Output
Step 8  → Save Results (optionally update web index via update_modelshow_index.py)

Step 1: Acknowledge & Load Configuration

Immediate Response:

text

🔄 ModelShow starting — querying models in parallel.
Results will appear automatically when judging is complete.

Load Configuration: Read {baseDir}/config.json for model list, judge model, timeouts, and other settings.

Step 2: Spawn Parallel Model Agents

For each model in config.models:

• Model: The model alias (e.g., pro, grok, kimi)
• Label: mdls-{model}-{timestamp} (unique identifier)
• Timeout: config.timeoutSeconds (default: 360 seconds)
• Task:

  text

  {config.systemPrompt}
  
  {extracted user prompt}
  

Parallel Execution: If config.parallel is true, spawn all agents simultaneously.

Context Handling: If the prompt references external content (URLs, files, preferences), fetch and prepend this context to the task.

Step 3: Collect Responses with Intelligent Polling

Polling Strategy:

• Poll every 20 seconds
• Exit immediately when all agents complete
• Minimum 3 polls before considering timeout
• Maximum runtime: config.timeoutSeconds

Status Updates (content-free):

• ⏳ Models responding... {done}/{total} complete. ({elapsed}s elapsed)
• ✅ All {N} models responded. Sending to judge...

Response Collection:

python

collected_responses = {
  "model_name": {
    "status": "completed" | "failed" | "timeout",
    "text": "response text or empty string",
    "duration_seconds": duration
  }
}

Minimum Success Check: If successful responses < config.minSuccessful, abort with informative message.

Step 4: Anonymize with Cryptographic Randomization

Execute the anonymization pipeline:

bash

echo '{
  "action": "anonymize",
  "responses": {model: response_dict},
  "label_style": "alphabetic",
  "shuffle": true
}' | python3 {baseDir}/judge_pipeline.py

Key Features:

• shuffle: true ensures cryptographically random response order
• Labels are assigned as "Response A", "Response B", etc.
• anonymization_map tracks label-to-model mapping for later de-anonymization

Step 5: Spawn Judge+Deanon Sub-Agent

The judge sub-agent performs both evaluation and de-anonymization in a single atomic operation:

Judge Task Structure:

text

You are an impartial judge AND a data processor.

Your task has TWO parts. Complete BOTH before returning anything.

═══════════════════════════════════════════════════════════
PART 1: JUDGE THE RESPONSES
═══════════════════════════════════════════════════════════

[Blind responses with placeholder labels]

═══════════════════════════════════════════════════════════
PART 2: PROCESS YOUR JUDGMENT
═══════════════════════════════════════════════════════════

1. Write your judgment evaluating Response A, Response B, etc.
2. Include scores (1-10) for each response
3. Provide an "Overall Assessment" section analyzing cross-model patterns

After writing your judgment, run this command:

echo '{
  "action": "finalize",
  "judge_output": "[YOUR JUDGMENT TEXT HERE]",
  "anonymization_map": {anonymization_map}
}' | python3 {baseDir}/judge_pipeline.py

Return ONLY the JSON output from that command.

Judge Model: Uses config.judgeModel (e.g. sonnet, gemini31or)

Step 6: Parse De-anonymized Results

The judge sub-agent returns:

• deanonymized_judge_output: Full judgment with real model names
• ranked_models_deanonymized: Structured ranking data
• deanonymization_complete: Boolean verification

Architectural Guarantee: The orchestrator never receives placeholder labels—only de-anonymized results.

Step 7: Build Formatted Output

Create professional presentation:

text

🕶️ Double-Blind Judging Results:

🏆 Model Name (Score: X.X/10)
[Full response text]
Judge's assessment: [Commentary]

🥈 Second Place (Score: X.X/10)
[Full response text]
Judge's assessment: [Commentary]

📊 Overall Assessment:
[Judge's holistic analysis of cross-model patterns]

⚠️ WARNING: Step 8 is MANDATORY. Sending results to the user is NOT the end of the task. You MUST execute Step 8 (save_results) immediately after sending. The task is NOT complete until save_results.py returns {"success": true}. Do NOT skip, defer, or omit Step 8 under any circumstances.

Step 8: Save Results

🚨 THIS STEP IS REQUIRED. DO NOT SKIP. After every ModelShow run, you MUST save the results using save_results.py. Failure to save means the results are not persisted for later use.

Save to config.outputDir (default: ~/.openclaw/workspace/modelshow-results):

• JSON: {config.outputDir}/{slug}-{timestamp}.json
• Markdown: {config.outputDir}/{slug}-{timestamp}.md

Exact JSON payload to pipe into save_results.py:

json

{
  "prompt": "<the original user prompt>",
  "timestamp": "<ISO 8601 timestamp, e.g. 2026-03-08T01:00:00Z>",
  "models": ["model1", "model2", "model3"],
  "judge_model": "<config.judgeModel>",
  "output_dir": "<config.outputDir>",
  "ranked_results": [
    {
      "rank": 1,
      "model": "model_alias",
      "score": 9.5,
      "judge_notes": "Judge's per-model commentary here",
      "response_text": "The full model response text here"
    },
    {
      "rank": 2,
      "model": "model_alias",
      "score": 8.0,
      "judge_notes": "Judge's per-model commentary here",
      "response_text": "The full model response text here"
    }
  ],
  "deanonymized_judge_output": "<full judge output text with real model names>",
  "anonymization_map": {
    "Response A": "model_alias_1",
    "Response B": "model_alias_2"
  },
  "metadata": {
    "total_duration_ms": 45000,
    "successful_models": 4,
    "failed_models": 0,
    "timed_out_models": ["deepseek"]
  }
}

Execute the save command:

bash

echo '<JSON payload above>' | python3 {baseDir}/save_results.py

Verify success: The script MUST return {"success": true, ...}. If it returns an error, fix and retry. Do NOT proceed without a successful save.

Optional: For building a local index of result files (e.g. for a custom dashboard or static site) or for web display (e.g. rexuvia.com), see update_modelshow_index.py. This is not part of the mandatory workflow.

✅ Only after save_results.py returns success is the ModelShow task complete.

Configuration (config.json)

Key	Description	Default
keyword	Primary trigger	"mdls"
alternativeKeywords	Also trigger on	["modelshow"]
models	List of model aliases to compare	["pro", "sonnet", "deepseek", "gpt4", "grok", "kimi"]
judgeModel	Model for double-blind evaluation	"sonnet"
outputDir	Where to save result files	"~/.openclaw/workspace/modelshow-results"
timeoutSeconds	Maximum wait time per model	360
minSuccessful	Minimum responses to proceed	2
parallel	Run models in parallel	true
showTopN	Number of top results to display	10
includeResponseText	Include full responses in output	true
blindJudging	Enable anonymization	true
blindJudgingLabels	Label style for anonymization	"alphabetic"
shuffleBlindOrder	Randomize response order	true

File Structure

text

modelshow/
├── SKILL.md              # This documentation
├── config.json           # Configuration settings
├── judge_pipeline.py     # Anonymization & de-anonymization pipeline
├── save_results.py       # Result saving with holistic assessment extraction
├── update_modelshow_index.py # Optional: build local index / web index
├── blind_judge_manager.py # Anonymization utility (legacy)
├── README.md             # User documentation
└── .gitignore            # Git exclusions

Scripts

judge_pipeline.py

Core pipeline for anonymization and de-anonymization:

• action: "anonymize": Creates cryptographically randomized blind responses
• action: "finalize": De-anonymizes judge output and extracts rankings

save_results.py

Saves results in both JSON and Markdown formats with specialized extraction of the "Overall Assessment" section from judge output. Results are written to config.outputDir for local use, scripting, or your own tooling.

update_modelshow_index.py

Optional utility to build a local index of result JSON files (e.g. for a custom dashboard or static site) or to update the web index for rexuvia.com. Not required for the core workflow.

Usage Examples

Basic Comparison:

text

mdls explain the difference between TCP and UDP

Creative Task:

text

mdls write a short poem about working late at night

Technical Analysis:

text

mdls pros and cons of event sourcing vs traditional CRUD

Code Review:

text

mdls review this Python function for potential issues: [code]

Best Practices

1. Prompt Clarity: Provide clear, specific prompts for meaningful comparisons
2. Model Selection: Choose models with complementary strengths for the task type
3. Context Inclusion: Reference relevant context when appropriate
4. Result Interpretation: Consider both scores and the judge's holistic assessment
5. Tailor config: Update config.json to match the models available on your instance
6. Web Integration: Optionally use update_modelshow_index.py to publish results

Integration Points

• Local storage: Results are saved as JSON and Markdown in config.outputDir for local use, scripting, or your own tooling
• Web display: Use update_modelshow_index.py to make results available online
• Cron Automation: Can be scheduled for regular comparative analysis
• API Access: JSON results enable programmatic analysis

ModelShow represents state-of-the-art in AI model comparison, combining rigorous methodology with practical usability for both casual exploration and professional evaluation.