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skill-prune

Identify and remove negative-ROI skill content — orphan files, never-read entries, duplicates, content reintroducing challenge-specific lore. Inverse of /skill-update. Use during quarterly maintenance or after the linter flags issues.

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transilienceai/communitytools595 stars(GitHub 595 + cccskills 0)

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Files included (1)

  • SKILL.md2.8 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Skill Prune

Inverse of /skill-update. Removes content rather than adding it. Run during quarterly maintenance, after engagements, or when scripts/skill_linter.py reports orphans / duplicates.

When to invoke

  • Quarterly cadence.
  • After scripts/skill_linter.py --check-orphans reports orphan reference files.
  • After a SKILL.md or reference file grows past its cap and needs trimming.
  • After a de-specialization sweep, to drop content tied to retired challenges.

Prune criteria (the four signals — same shape as /skill-update, inverted)

A reference / scenario / line is a prune candidate when it satisfies any of:

  1. Orphan — not linked from any SKILL.md or other reference file in the last 60 days.
  2. Referenced only by failed engagements — appeared in attack-chain.md of runs that ended status=BLOCKED, never in a successful chain.
  3. Contradicted by newer content — a later scenario / pattern supersedes it; the older entry no longer reflects current technique.
  4. Redundant with newer content — same technique covered more clearly elsewhere.

Removing content fails any of these → keep it.

Safety rules

  • Never prune a file with <!-- KEEP: <reason> --> annotation.
  • Never prune content cited in a still-open engagement's OUTPUT_DIR/attack-chain.md.
  • Never prune the canonical-home file for a single-owner rule (brute-force, output-discipline, env-reader, skill-update).
  • Bias toward keeping technique-rich content over operational lore.

Procedure

  1. Run scripts/skill_linter.py --check-orphans to surface orphans.
  2. For each candidate file or block, evaluate the four signals.
  3. Build a deletion plan — show files / lines to remove with one-line rationale per item.
  4. Apply deletions only after the plan is approved (skill-prune does not auto-delete during invocation).
  5. Re-run scripts/skill_linter.py to confirm the change broke no other links and didn't reintroduce duplicates.

Output

Concise change report:

  • Removed. File or block + one-line rationale per item.
  • Kept (flagged for follow-up). Items that failed all four signals but are worth revisiting next quarter.
  • No prunes. State explicitly when nothing warranted removal.

Anti-Patterns

  • Pruning content because it's old — age alone is not a signal; relevance is.
  • Pruning a reference because its parent SKILL.md is bloated — fix the SKILL.md instead.
  • Removing a single-owner canonical file (brute-force / output-discipline / env-reader).
  • Pruning during an active engagement that may still cite the content.

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