Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add transilienceai/communitytools --skill skill-prunegit clone --depth 1 https://github.com/transilienceai/communitytoolsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/transilienceai/communitytools/skill-prune)<a href="https://agentmods.dev/skills/transilienceai/communitytools/skill-prune"><img src="https://agentmods.dev/badge/skills/transilienceai/communitytools/skill-prune.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.00640 |
| Opus 5 | $0.00024 | $0.00320 |
| Sonnet 5 | $0.00010 | $0.00128 |
| Haiku 4.5 | $0.00005 | $0.00064 |
Grade A, and why
skill-prune scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 57 lines · 49 tokens per session scan A 12dec5b1fe85
skill-prune is a skill published in the GitHub repository transilienceai/communitytools (511 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 640 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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