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Scan skills to extract cross-cutting principles and distill them into rules — append, revise, or create new rule files
Passed 17 of 21 test cases with the skill, and 8 of 21 without it.
and 10 more in the full report
10 fixed9 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
We need to log a candidate principle 'Always validate schema before saving to storage' in the results tracking file. What key casing format must be used for candidate object keys in this JSON file?
| Checked | Without skill | With skill |
|---|---|---|
| Formats candidate record keys in kebab-case | Missed | Passed |
The instructions your model reads when the skill loads.
Scan installed skills, extract cross-cutting principles that appear in multiple skills, and distill them into rules — appending to existing rule files, revising outdated content, or creating new rule files.
Applies the "deterministic collection + LLM judgment" principle: scripts collect facts exhaustively, then an LLM cross-reads the full context and produces verdicts.
The rules distillation process follows three phases:
bash ~/.claude/skills/rules-distill/scripts/scan-skills.sh
bash ~/.claude/skills/rules-distill/scripts/scan-rules.sh
Rules Distillation — Phase 1: Inventory
────────────────────────────────────────
Skills: {N} files scanned
Rules: {M} files ({K} headings indexed)
Proceeding to cross-read analysis...
test cases passed, vs 8 / 21 without
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