Compute a probabilistic codebase rating using parallel raters and adversaries.
npx skills add https://github.com/HECStore/cj-store --skill rate
Please help me install this Agent Skill. Skill: rate Source: https://github.com/HECStore/cj-store/tree/main/.claude/skills/rate Command: npx skills add https://github.com/HECStore/cj-store --skill rate
This Skill provides a structured, probabilistic assessment of a codebase or project by orchestrating multiple raters and adversaries to produce a final rating, helping teams gauge quality quickly.
Invoke /rate in chat to obtain a probabilistic rating for your codebase.
High-intent search queries and answers about installing and using this skill.
To get an AI codebase review, invoke the /rate command in chat. The system uses parallel raters and adversaries to sample repository aspects and compute a final probabilistic quality rating.
Adversarial AI review works by spawning K subagents per rater to generate adjusted reports that challenge original assessments. Surviving ratings aggregate into a final mean rating with a concise synthesis of strengths and gaps.
Yes, you can get actionable improvements during a codebase assessment by using the optional improve flag. This mode collects suggested improvements from subagents and includes them directly in the final synthesis.
Yes, the multi-rater codebase rating supports different programming languages and tooling. It applies to project reviews and repo quality judgments across diverse languages by sampling repository aspects.
The best way to evaluate repository quality is using a probabilistic rating system with N raters and K adversaries per rater. This approach challenges initial assessments to produce a synthesized mean rating.
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