Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Developer Tools
Peer Review
Multi-model peer review layer using local LLMs via Ollama to catch errors in cloud model output. Fan-out critiques to 2-3 local models, aggregate flags, synthesize consensus. Use when: validating trade analyses, reviewing agent output quality, testing local model accuracy, checking any high-stakes Claude output before publishing or acting on it. Don't use when: simple fact-checking (just search the web), tasks that don't benefit from multi-model consensus, time-critical decisions where 60s latency is unacceptable, reviewing trivial or low-stakes content. Negative examples: - "Check if this date is correct" → No. Just web search it. - "Review my grocery list" → No. Not worth multi-model inference. - "I need this answer in 5 seconds" → No. Peer review adds 30-60s latency. Edge cases: - Short text (<50 words) → Models may not find meaningful issues. Consider skipping. - Highly technical domain → Local models may lack domain knowledge. Weight flags lower. - Creative writing → Factual review doesn't apply well. Use only for logical consistency.
skill openclawclawhub Free
Multi-model peer review layer using local LLMs via Ollama to catch errors in cloud model output. Fan-out critiques to 2-3 local models, aggregate flags, synthesize consensus. Use when: validating trade analyses, reviewing agent output quality, testing local model accuracy, checking any high-stakes Claude output before publishing or acting on it. Don't use when: simple fact-checking (just search the web), tasks that don't benefit from multi-model consensus, time-critical decisions where 60s latency is unacceptable, reviewing trivial or low-stakes content. Negative examples: - "Check if this date is correct" → No. Just web search it. - "Review my grocery list" → No. Not worth multi-model inference. - "I need this answer in 5 seconds" → No. Peer review adds 30-60s latency. Edge cases: - Short text (<50 words) → Models may not find meaningful issues. Consider skipping. - Highly technical domain → Local models may lack domain knowledge. Weight flags lower. - Creative writing → Factual review doesn't apply well. Use only for logical consistency.
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Package facts
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- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- staybased
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Documentation
Peer Review — Local LLM Critique Layer
Hypothesis: Local LLMs can catch ≥30% of real errors in cloud output with <50% false positive rate.
Architecture
Cloud Model (Claude) produces analysis │ ▼ ┌────────────────────────┐ │ Peer Review Fan-Out │ ├────────────────────────┤ │ Drift (Mistral 7B) │──► Critique A │ Pip (TinyLlama 1.1B) │──► Critique B │ Lume (Llama 3.1 8B) │──► Critique C └────────────────────────┘ │ ▼ Aggregator (consensus logic) │ ▼ Final: original + flagged issues
Swarm Bot Roles
BotModelRoleStrengthsDrift 🌊Mistral 7BMethodical analystStructured reasoning, catches logical gapsPip 🐣TinyLlama 1.1BFast checkerQuick sanity checks, low latencyLume 💡Llama 3.1 8BDeep thinkerNuanced analysis, catches subtle issues
Scripts
ScriptPurposescripts/peer-review.shSend single input to all models, collect critiquesscripts/peer-review-batch.shRun peer review across a corpus of samplesscripts/seed-test-corpus.shGenerate seeded error corpus for testing
Usage
# Single file review bash scripts/peer-review.sh <input_file> [output_dir] # Batch review bash scripts/peer-review-batch.sh <corpus_dir> [results_dir] # Generate test corpus bash scripts/seed-test-corpus.sh [count] [output_dir] Scripts live at workspace/scripts/ — not bundled in skill to avoid duplication.
Critique Prompt Template
You are a skeptical reviewer. Analyze the following text for errors. For each issue found, output JSON: {"category": "factual|logical|missing|overconfidence|hallucinated_source", "quote": "...", "issue": "...", "confidence": 0-100} If no issues found, output: {"issues": []} TEXT: --- {cloud_output} ---
Error Categories
CategoryDescriptionExamplefactualWrong numbers, dates, names"Bitcoin launched in 2010"logicalNon-sequiturs, unsupported conclusions"X is rising, therefore Y will fall"missingImportant context omittedIgnoring a major counterargumentoverconfidenceCertainty without justification"This will definitely happen" on 55% eventhallucinated_sourceCiting nonexistent sources"According to a 2024 Reuters report..."
Discord Workflow
Post analysis to #the-deep (or #swarm-lab) Drift, Pip, and Lume respond with independent critiques Celeste synthesizes: deduplicates flags, weights by model confidence If consensus (≥2 models agree) → flag is high-confidence Final output posted with recommendation: publish | revise | flag_for_human
Success Criteria
OutcomeTPRFPRDecisionStrong pass≥50%<30%Ship as default layerPass≥30%<50%Ship as opt-in layerMarginal20–30%50–70%Iterate on prompts, retestFail<20%>70%Abandon approach
Scoring Rules
Flag = true positive if it identifies a real error (even if explanation is imperfect) Flag = false positive if flagged content is actually correct Duplicate flags across models count once for TPR but inform consensus metrics
Dependencies
Ollama running locally with models pulled: mistral:7b, tinyllama:1.1b, llama3.1:8b jq and curl installed Results stored in experiments/peer-review-results/
Integration
When peer review passes validation: Package as Reef API endpoint: POST /review Agents call before publishing any analysis Configurable: model selection, consensus threshold, categories Log all reviews to #reef-logs with TPR tracking
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md