peer-reviewMulti-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.
Install via ClawdBot CLI:
clawdbot install staybased/peer-reviewGrade Good — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated Mar 1, 2026
A hedge fund uses the peer review skill to validate AI-generated market analyses before making investment decisions. The multi-model consensus catches factual errors in economic data and logical gaps in trading strategies, reducing risk in high-stakes financial operations.
A law firm employs the skill to review AI-drafted contracts and legal briefs for logical inconsistencies and missing clauses. Local models flag overconfident assertions or hallucinated legal precedents, ensuring accuracy before client submission.
Universities integrate peer review to check AI-generated research summaries or paper drafts for factual inaccuracies and unsupported conclusions. It helps researchers avoid publishing errors in technical domains by weighting flags based on model confidence.
Medical institutions use the skill to review AI-assisted diagnostic reports for logical errors or missing context in patient data analysis. It acts as a safety layer before finalizing treatment plans, though technical domain limitations require careful flag interpretation.
Media companies apply peer review to validate AI-written articles or reports for factual mistakes and overconfidence before publication. The Discord workflow synthesizes critiques to recommend revisions, maintaining editorial standards in time-insensitive scenarios.
Offer the peer review skill as a cloud API endpoint (e.g., Reef API) with tiered subscriptions based on review volume and model selection. Revenue comes from monthly fees for businesses needing high-stakes output validation, with logging and TPR tracking as premium features.
Provide custom integration services for enterprises to embed peer review into their existing AI workflows, such as financial or legal systems. Revenue is generated through one-time setup fees and ongoing support contracts, leveraging the skill's scripts and consensus logic.
Release a free version with basic peer review capabilities for individual users or small teams, monetized through a premium tier offering batch processing, advanced error categories, and lower latency options. Revenue streams include upgrades and enterprise licenses.
💬 Integration Tip
Ensure Ollama is running locally with required models pulled, and use the provided scripts in workspace/ for initial testing before full API deployment.
Scored Apr 19, 2026
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