skill-prune
- Repo stars 290
- Author updated Live
- Author repo communitytools
- Domain
- Engineering
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @transilienceai · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: skill-prune
description: Identify and remove negative-ROI skill content — orphan files, never-read entries, duplicates, c…
category: engineering
runtime: no special runtime
---
# skill-prune output preview
## PART A: Task fit
- Use case: 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..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to invoke / Prune criteria (the four signals — same shape as /skill-update, inverted) / Safety rules” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “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.”.
- **02** When the source has headings, the agent prioritizes “When to invoke / Prune criteria (the four signals — same shape as /skill-update, inverted) / Safety rules” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source mentions slash commands such as `/skill-update`; use them first when your agent supports command triggers.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “When to invoke / Prune criteria (the four signals — same shape as /skill-update, inverted) / Safety rules”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: skill-prune
description: Identify and remove negative-ROI skill content — orphan files, never-read entries, duplicates, c…
category: engineering
source: transilienceai/communitytools
---
# skill-prune
## When to use
- Identify and remove negative-ROI skill content — orphan files, never-read entries, duplicates, content reintroducing c…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “When to invoke / Prune criteria (the four signals — same shape as /skill-update, inverted) / Safety rules” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "skill-prune" {
input -> user goal + target files + boundaries + acceptance criteria
context -> When to invoke / Prune criteria (the four signals — same shape as /skill-update, inverted) / Safety rules
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}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-orphansreports 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:
- Orphan — not linked from any SKILL.md or other reference file in the last 60 days.
- Referenced only by failed engagements — appeared in
attack-chain.mdof runs that endedstatus=BLOCKED, never in a successful chain. - Contradicted by newer content — a later scenario / pattern supersedes it; the older entry no longer reflects current technique.
- 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
- Run
scripts/skill_linter.py --check-orphansto surface orphans. - For each candidate file or block, evaluate the four signals.
- Build a deletion plan — show files / lines to remove with one-line rationale per item.
- Apply deletions only after the plan is approved (skill-prune does not auto-delete during invocation).
- Re-run
scripts/skill_linter.pyto 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.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review