What problem does it solve? Teams need objective, evidence-based assessments of code health before refactors or releases, but manual audits are slow and findings are often unverifiable. This Skill automates multi-dimension static analysis and produces a structured report where every issue is traceable to a specific file and line. ## Core Features & Use Cases - Seven-dimension audit: Analyzes complexity, duplication, dead code, security risks, dependencies, test coverage, and code standards using automated Python scans plus manual reading of hot spots. - Structured reporting: Generates a markdown report with A-F grades per dimension, severity-rated findings (P0-P3), and verified per-directory statistics. - Independent verification: Dispatches three parallel sub-agents to cross-check the report for fabricated findings, missed security issues, and statistical inconsistencies. - Use Case: Before a major refactor, run a baseline audit of your src directory to identify high-complexity methods, duplicated blocks, untested critical files, and hardcoded secrets, then get a prioritized remediation plan. ## Quick Start Ask the agent to run a code quality analysis on your src directory covering complexity, duplication, security, and test coverage, and output the report to docs/reports/code-quality-YYYY-MM-DD.md.
