AI agents: to install this, read and follow INSTALL_FOR_AGENTS.md — it installs the CLI and registers the /skill-issue skill, then verifies.

A coding agent decides which skill to run from each skill's always-on name + description. So a skill can be perfectly implemented and still never fire — because its description is too vague to match how people actually phrase requests, or because a more specific sibling silently wins every time. Nobody gets an error that says "this skill never triggers". It just sits there.

skill-issue audits exactly the metadata the picker reads. It grades each skill on whether it will fire, predicts which skill fires for a prompt you give it, and finds collisions where one skill shadows another.

$ skill-issue ~/.claude/skills

F  deploy-helper
     ✗ no description — the agent has nothing to match on, so it can never fire
       → add a description: "<what it does>. Use when <triggers>."
C  shipit
     ! no "use when …" trigger clause — the agent has to guess when this applies
       → append: Use when <the phrases a user would actually type>
A  rollback-prod  ✓ will fire on its triggers

12 skills · 1 error · 3 warning
2 collision cluster(s) found — run --collisions to see which skills shadow each other.

It's the activation half of skill hygiene. The drift half is skillrot (skills that call CLIs the installed version no longer accepts).

Why

You write a skill, install it, and the agent keeps doing the task some other way. You tweak the description, reload, retry, guess. There was no tool that answered "why didn't it fire?" — so this is that tool. It also catches the quieter bug: two skills that compete for the same intent, where installing both means one silently never runs and its author never finds out.

New to this? Read Why won't my skill fire? — the failure modes and how to diagnose each in ten seconds.

Install — clone + setup (the gstack / gbrain way)

skill-issue installs like gstack and gbrain: clone the repo, run ./setup. One line, any agent:

git clone https://github.com/mishanefedov/skill-issue ~/.skill-issue \
  && cd ~/.skill-issue && ./setup

./setup runs bun install, puts skill-issue on PATH (bun link), and symlinks the skill into every coding agent on the machine (~/.claude/skills, ~/.codex/skills, opencode, Factory, Cursor, Kiro, ~/.agents/skills). Requires Bun + Git. Re-run after git pull to update.

Self-install: tell any agent "use skill-issue to audit my skills" and point it at INSTALL_FOR_AGENTS.md — it runs the clone + setup itself, then the audit. No manual steps.

Other ways

# one line, no clone shown (binary fast-path → Bun source fallback):
curl -fsSL https://raw.githubusercontent.com/mishanefedov/skill-issue/main/install.sh | bash

# Claude Code plugin (no Bun, no clone):
#   /plugin marketplace add mishanefedov/skill-issue
#   /plugin install skill-issue@skill-issue

# from npm (published):
npx @misha_misha/skill-issue ~/.claude/skills

# Homebrew (macOS/Linux):
brew install mishanefedov/skill-issue/skill-issue

Works with

Any agent that loads SKILL.md-format skills: Claude Code, Codex, Cursor, opencode, Factory. The CLI is agent-agnostic — run it from anything, including CI.

Usage

skill-issue <skills-dir>                       # grade every skill A–F; exit 1 on error
skill-issue <skills-dir> --why "deploy to prod"  # simulate which skill fires, and why
skill-issue <skills-dir> --collisions          # clusters of skills that shadow each other
skill-issue <skills-dir> --fix                 # add a "Use when …" clause to weak descriptions
skill-issue <skills-dir> --json                # machine-readable
skill-issue .                                  # audit the current repo's skills/ + agents/

--why — simulate activation

$ skill-issue ~/.claude/skills --why "deploy the app to prod"
   1. shipit          0.74  [deploy prod app]  ← would fire
   2. land-deploy     0.69  [deploy prod]      (margin 0.05 — ambiguous, likely collision)
   3. canary          0.38  [prod]

prompt [heuristic]: deploy the app to prod

Each skill is scored by how much of the prompt's salient, rarity-weighted vocabulary it covers (rare, specific terms count more than generic ones). A small margin between the top two means they collide on this intent. If even the top score is weak, no skill reliably fires — which is the diagnosis when one should have. Add --skill <name> to see exactly which of the prompt's words a given skill's description is missing. Add --llm to judge with a local claude/codex CLI instead of the offline heuristic.

What it catches (v1)

Issue	Level
Empty / missing description (can never be matched)	error
Two skills with identical descriptions (picker can't tell them apart)	error
No "use when …" trigger clause	warning
Description too vague / too few specific terms	warning
Description too thin to match varied phrasing, or so heavy it taxes context	warning
Description only restates the name	warning
A collision cluster where one skill shadows another	reported by --collisions

Conservative on purpose: only genuinely broken metadata is an error. A linter that cries wolf on a big skill set gets uninstalled.

How it works

1. Discover — find every SKILL.md (and agents/*.md) under the root and read each one's name + description (the always-on activation surface).
2. Triggers — tokenize name, description, and any "use when …" clause into weighted terms; name and trigger terms weigh more than body prose; generic filler ("helps with code") is down-weighted.
3. Score / grade — --why ranks skills against a prompt by rarity-weighted term coverage; lint grades each skill A–F on its activation defects; --collisions unions skills with heavy salient-term overlap into clusters.
4. Report — grouped, with concrete fixes; exit 1 on any error.

Scope

v1: heuristic, offline, conservative — vague/thin/generic descriptions, missing "use when" clauses, duplicate metadata, and trigger collisions, with --fix to append a grounded "Use when …" clause and --llm to judge with a real model.

Roadmap (v2):

• Semantic collisions — embedding similarity instead of lexical overlap ("review code" vs "audit my diff").
• Agent-loop replay — actually run the agent harness on sample prompts and observe which skill it selects. Ground truth instead of prediction.
• Trigger coverage — generate the ways users phrase a request and check each one fires the intended skill; report the blind spots.

Prior art

skillrot is the sibling: it checks your skills call CLIs correctly for the version installed. skill-issue checks your skills fire at all. Correctness vs. activation.

License

MIT