llm-judgeUse when comparing two or more code implementations against a spec or requirements doc. Triggers on "which repo is better", "compare these implementations",...
Install via ClawdBot CLI:
clawdbot install anderskev/llm-judgeGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated Apr 22, 2026
Compare multiple GitHub repositories implementing the same API specification to select the best candidate for integration or forking. The LLM Judge assesses functionality, security, and code quality to rank implementations objectively.
Evaluate internal codebases across different teams for adherence to financial regulations and security standards. The skill identifies vulnerabilities and dead code, ensuring compliance and reducing audit risks.
Analyze code from multiple target companies during mergers to assess technical debt, test quality, and security posture. This helps in valuation and integration planning by scoring each codebase systematically.
Use in coding bootcamps or universities to compare student submissions against a reference implementation. It provides automated, rubric-based feedback on functionality and overengineering to aid learning.
Integrate into CI/CD pipelines to evaluate pull requests from multiple repositories against a shared spec. It scores changes on security and test quality before merging, ensuring consistent standards.
Offer a cloud-based service where teams upload repositories for automated LLM Judge assessments. Charge per evaluation or via subscription tiers based on repo size and frequency, targeting enterprises needing compliance checks.
Provide custom integration of the LLM Judge skill into client workflows, such as M&A due diligence or internal audits. Revenue comes from project-based fees and ongoing support contracts for tailored solutions.
Release the core skill as open source to build community adoption, then monetize through premium features like advanced reporting, historical analysis, or priority support. Target developers and small teams scaling up.
💬 Integration Tip
Ensure all referenced files (e.g., fact-schema.md) are accessible in the environment, and pre-load the llm-artifacts-detection dependency to avoid runtime errors during Phase 1.
Scored Jun 29, 2026
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