MCP App StoreMCP App Store
from claude-skill-registry412
Analyze structured review feedback logs to identify recurring false positives, missing checks, and produce evidence-backed recommendations to improve review ski
Review Skill Improver analyzes structured feedback logs from review runs to find rules that produce frequent false positives or miss real issues. It aggregates per-rule acceptance/rejection rates, extracts rejection rationales, groups patterns by theme, and outputs concrete, evidence-backed recommendations for skill changes.
Use this skill when you have accumulated feedback (audit logs, reviewer verdicts) and want to systematically improve your automated review rules — for example after several review cycles or when false positives rise. It's intended for periodic quality analysis, not single-case triage.
rule_source, identify high-rejection rules (>30%), perform pattern analysis of rationales, and generate recommendation blocks with evidence pointers and proposed skill edits.This skill is written as an internal analysis utility and is compatible with agent stacks that support structured review workflows (Claude Code / Claude-integration style agents, or similar LLM review harnesses). It assumes logs can be provided as files or attached artifacts and that maintainers can edit SKILL.md files based on recommendations.
This skill has not been reviewed by our automated audit pipeline yet.
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