k-skill-cleaner

Use this skill when the user wants to slim down a K-skill bundle, find skills they never use, or make an evidence-backed deletion shortlist instead of deleting directories by guesswork.

Safety contract

• Do not delete skills automatically. Produce a ranked recommendation first, then make deletions only after the user explicitly approves the shortlist.
• Treat trigger counts as best-effort signals, not absolute truth. Different agents store transcripts differently and may rotate or omit logs.
• Protect any skill the user marks as "keep", even if its trigger count is zero.
• Prefer removing whole root-level skill directories only after checking README/docs/install references in the same change.

Interview first

Ask a compact interview before scanning or recommending deletion:

1. 어떤 에이전트를 주로 쓰나요? (Claude Code, Codex, OpenCode, OpenClaw/ClawHub, Hermes Agent, 기타)
2. 절대 지우면 안 되는 스킬은 무엇인가요?
3. 본인이 절대로 쓰지 않는다고 확신하는 스킬은 무엇인가요?
4. 최근 30/90/180일 중 어떤 기간의 사용 흔적을 우선 볼까요? helper 실행 시 --days 또는 --since로 반영합니다.
5. 추천만 원하나요, 아니면 승인 후 실제 삭제까지 원하나요?

Trigger count sources by agent

Local helper

From an installed standalone skill, run the deterministic helper from the k-skill-cleaner skill directory. In a full repository checkout, the compatibility wrapper at scripts/k_skill_cleaner.py accepts the same options.

python3 scripts/k_skill_cleaner.py \
  --skills-root . \
  --scan-default-logs \
  --days 90 \
  --never-use blue-ribbon-nearby,lotto-results \
  --keep k-skill-setup,k-skill-cleaner

For agent exports or hand-curated counts, pass a JSON object mapping skill name to trigger count:

python3 scripts/k_skill_cleaner.py --skills-root . --usage-json usage-counts.json --days 90

--days and --since filter scanned log records only. --usage-json values are already-aggregated counts, so prepare/export that JSON for the same time window before passing it to the helper.

The helper prints JSON with:

• skill_count: number of root-level skills discovered.
• candidates: ranked remove or review candidates with trigger_count and reasons.
• agent_usage_sources: the agent-specific paths and caveats above.
• time_window: the effective --since/--days cutoff and mtime fallback caveat.
• usage_json: whether imported counts were merged and the pre-windowing caveat.
• scanned_logs: how many readable log files were scanned and which paths contributed best-effort evidence.
• safety: reminder that no files were deleted.

Recommendation policy

• remove: user explicitly marked the skill as never used. Mention any zero/low trigger evidence as supporting context.
• review: trigger count is zero or below the selected low-usage threshold, but the user did not explicitly ask to remove it.
• keep: user-protected skills and actively triggered skills.

When reporting, group recommendations like this:

1. 삭제 후보 — interview says never used, with trigger evidence.
2. 검토 후보 — zero/low trigger count only.
3. 보존 후보 — protected or recently used.
4. 통계 한계 — which agents had no readable logs and require manual export.

If deletion is approved

1. Remove the skill directory.
2. Remove README table/list entries and docs/features/<skill>.md links.
3. Remove docs/install.md --skill <skill> entries.
4. Remove package/workspace/test references only if the skill owns those files.
5. Run npm run lint, npm run typecheck, and npm run test (or npm run ci for packaging/release changes).