Audit CLAUDE.md, rules, and memory for drift and contradictions.
npx skills add https://github.com/benkruger/flow --skill flow-hygiene
Please help me install this Agent Skill. Skill: flow-hygiene Source: https://github.com/benkruger/flow/tree/main/skills/flow-hygiene Command: npx skills add https://github.com/benkruger/flow --skill flow-hygiene
Audit the health of instruction surfaces such as CLAUDE.md, .claude/rules/*, and memory to detect drift, misplacements, and contradictions that degrade project governance.
Run the hygiene audit to scan CLAUDE.md, rules, and memory and generate a findings report.
High-intent search queries and answers about installing and using this skill.
Run a hygiene audit to scan CLAUDE.md, .claude/rules/*, and memory files for stale references, orphaned content, and conflicting constraints. This generates a grouped findings report with clear remediation guidance for each discrepancy.
Instruction corpus drift occurs when CLAUDE.md, rules, and memory files accumulate stale references, orphaned content, and contradictions that degrade project governance and misalign constraints from the actual codebase.
Yes, you can audit memory files and .claude/rules/* for contradictions by running a structural validation that verifies file references exist and checks content alignment against the codebase across all instruction surfaces.
Verify structural references by scanning the instruction corpus to ensure file paths in CLAUDE.md and rules point to existing files and match the codebase. The audit classifies broken paths and misplacements as actionable discrepancies.
The best way to generate a findings report for instruction drift is to run a hygiene audit that groups discrepancies by source file, classifies the structural validation issues, and outputs actionable remediation guidance for CLAUDE.md and rules.
You need projects containing CLAUDE.md, .claude/rules/*, and memory files to run the hygiene audit. The scanner requires these instruction surfaces to perform surface inventory, structural verification, and discrepancy classification.
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