# Send Modelshow to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "modelshow", "name": "Modelshow", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/schbz/modelshow", "canonicalUrl": "https://clawhub.ai/schbz/modelshow", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/modelshow", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=modelshow", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "blind_judge_manager.py", "config.json", "judge_pipeline.py", "README.md", "save_results.py", "skill.json" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "slug": "modelshow", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-02T12:00:10.374Z", "expiresAt": "2026-05-09T12:00:10.374Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=modelshow", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=modelshow", "contentDisposition": "attachment; filename=\"modelshow-1.0.1.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "modelshow" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/modelshow" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/modelshow", "downloadUrl": "https://openagent3.xyz/downloads/modelshow", "agentUrl": "https://openagent3.xyz/skills/modelshow/agent", "manifestUrl": "https://openagent3.xyz/skills/modelshow/agent.json", "briefUrl": "https://openagent3.xyz/skills/modelshow/agent.md" } } ``` ## Documentation ### 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 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: 🔄 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: {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: 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: 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: 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: 🕶️ 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: { "prompt": "", "timestamp": "", "models": ["model1", "model2", "model3"], "judge_model": "", "output_dir": "", "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": "", "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: echo '' | 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) KeyDescriptionDefaultkeywordPrimary trigger"mdls"alternativeKeywordsAlso trigger on["modelshow"]modelsList of model aliases to compare["pro", "sonnet", "deepseek", "gpt4", "grok", "kimi"]judgeModelModel for double-blind evaluation"sonnet"outputDirWhere to save result files"~/.openclaw/workspace/modelshow-results"timeoutSecondsMaximum wait time per model360minSuccessfulMinimum responses to proceed2parallelRun models in paralleltrueshowTopNNumber of top results to display10includeResponseTextInclude full responses in outputtrueblindJudgingEnable anonymizationtrueblindJudgingLabelsLabel style for anonymization"alphabetic"shuffleBlindOrderRandomize response ordertrue ### File Structure 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 ### 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: mdls explain the difference between TCP and UDP Creative Task: mdls write a short poem about working late at night Technical Analysis: mdls pros and cons of event sourcing vs traditional CRUD Code Review: mdls review this Python function for potential issues: [code] ### Best Practices Prompt Clarity: Provide clear, specific prompts for meaningful comparisons Model Selection: Choose models with complementary strengths for the task type Context Inclusion: Reference relevant context when appropriate Result Interpretation: Consider both scores and the judge's holistic assessment Tailor config: Update config.json to match the models available on your instance 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. ## Trust - Source: tencent - Verification: Indexed source record - Publisher: schbz - Version: 1.0.1 ## Source health - Status: healthy - Item download looks usable. - Yavira can redirect you to the upstream package for this item. - Health scope: item - Reason: direct_download_ok - Checked at: 2026-05-02T12:00:10.374Z - Expires at: 2026-05-09T12:00:10.374Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/modelshow) - [Send to Agent page](https://openagent3.xyz/skills/modelshow/agent) - [JSON manifest](https://openagent3.xyz/skills/modelshow/agent.json) - [Markdown brief](https://openagent3.xyz/skills/modelshow/agent.md) - [Download page](https://openagent3.xyz/downloads/modelshow)