transilienceai/communitytools/skill-prune

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.