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    Skill Trigger Tuner: Audit and Rewrite SKILL.md Descriptions

    1

    Audits SKILL.md descriptions, finds trigger collisions between skills and rewrites each description as a "Use when / Do not use when" classifier you can measure.

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    Skill Trigger Tuner: Audit and Rewrite SKILL.md Descriptions

    Skill Trigger Tuner: Audit and Rewrite SKILL.md Descriptions

    Example session with this skill installed

    Audit the three skills in this folder and tell me which ones overlap.

    • Read your context and instructions
    • Compiled the trigger tuner

    Summary

    Audited 3 skills. changelog-helper and release-notes collide (Jaccard 0.636, containment 0.875), and neither has a "Use when" or "Do not use when" clause. Output below is the real script output from the bundled demo.

    Trigger audit

    skilllengthUse whenDo not use whenphraseserrorswarnings
    changelog-helper98nono003
    commit-message-helper258yesyes401
    release-notes86nono004

    Collisions

    levelskill Askill BJaccardcontainmentshared words
    collisionchangelog-helperrelease-notes0.6360.875change, changelog, note, project, release, summary, write

    Thresholds are heuristics for spotting overlap; the agent's real choice depends on the model and the full prompt.

    Next

    I rewrite release-notes with the Scope–Signal–Fence method, then score the old and new versions on the demo test set and show the comparison table.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Use when a skill never loads, the wrong skill loads, or a folder of skills has grown and you suspect overlap.

    A standard-library Python audit checks every description against Anthropic's documented limits (1024 characters, reserved words in name), counts trigger phrases and flags colliding pairs with Jaccard and containment scores. The skill then rewrites each weak description with the Scope–Signal–Fence method and scores the old and new versions side by side on a should-fire / should-not-fire test set, using a transparent word-overlap proxy.

    Do not use when you need a brand-new skill written from scratch, a review of the instructions inside the skill body, or marketplace listing copy.

    What the scores mean

    The evaluation is a word-overlap proxy for before/after comparison. It shows whether a description's wording covers the prompts you care about. It does not predict how often a particular model will load the skill.

    Example prompts

    • "My deploy skill never triggers. Here's the SKILL.md, fix the description."
    • "Audit my .claude/skills folder and tell me which skills overlap."
    • "Rewrite this description so it fires on 'write release notes' but not on commit messages, and show me before and after."

    Independent

    Not affiliated with or endorsed by Anthropic. Product names are used only to describe compatibility.

    Scores are a word-overlap proxy for before/after comparison; they do not predict how often a particular model will load the skill. Symlinks are not followed during audits.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

    Reviews

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    Fresh listing

    Recently published to Agensi

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    Doesn't do what its page promises? You get your money back.

    Trust & safety

    Security scan

    Security scan passed 9 days ago

    Requires

    Python 3.8+ (standard library only)
    Code execution enabled (claude.ai custom skills)

    Permissions

    Terminal / Shell
    Read Files
    Write Files
    Listed10 days ago
    Updated6 days ago

    What's inside

    Frequently Asked Questions

    It identifies 'trigger collisions' where two skills have overlapping descriptions, causing the AI to get confused or pick the wrong tool for a task.

    The tuner is optimized for Anthropic's Agent Skills specification and any AI framework that utilizes `SKILL.md` files for tool definitions.

    You receive the core Python diagnostic suite, the Scope-Signal-Fence optimization logic, and the scoring scripts for measuring precision and recall.

    No, it uses a Python-based diagnostic suite to run local audits and heuristic scoring, though you can use an LLM to execute the suggested rewrites.

    The 'Fence' is a specific negation technique included in the optimization process that explicitly defines what a skill should NOT do to prevent it from stealing tasks from others.