# Skill Review

> Review skills for quality, consistency, triggering accuracy, and adherence to the 5000-word / 500-line body guideline. Two modes: 'all' (bulk ecosystem-wide scan for ownership conflicts, length outliers, weak triggers, dead xrefs) or a single skill name (deep dive on description quality, body structure, anti-pattern naming, cross-references). Outputs a structured markdown report plus JSON sidecar consumable by skill-update. Trigger on "audit skills", "review this skill", "health check skills", "bulk review", "deep review", "what needs fixing".

- Skill: `ivy00johns/skill-review` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add ivy00johns/skill-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ivy00johns/skill-review/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security
- Author: ivy00johns (https://skillmd.com/u/ivy00johns)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/ivy00johns/skill-review

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# Skill Review

One skill, two modes. Use `--scope=all` for an ecosystem-wide bulk scan; use `--scope=<skill-name>` for a thorough single-skill deep dive. Both modes produce a structured markdown report plus a JSON sidecar that `skill-update` can consume to generate fix plans.

## When to Use

- Periodic health check across the skill ecosystem (`--scope=all`)
- After bulk edits or a new batch of skills lands (`--scope=all`)
- Reviewing a single skill before publishing (`--scope=<name>`)
- Investigating why one skill won't trigger or produces poor output (`--scope=<name>`)
- Quality-gating a skill before it enters the ecosystem (`--scope=<name>`)

## Inputs

The skill takes a single positional argument:

- `all` — bulk scan across `skills/**/SKILL.md`
- `<skill-name>` — deep dive on `skills/**/<skill-name>/SKILL.md`

Optional follow-on context:

- **Focus** — narrow the run to specific checks ("just ownership", "just descriptions")
- **Why-now** — what prompted the review (e.g., "it never triggers", "outputs are wrong")

## Process

### Phase 0: Parse the Argument

1. Read the argument. If it equals `all` (case-insensitive), enter Mode A.
2. Otherwise treat the argument as a skill name. Glob for `skills/**/<arg>/SKILL.md`. If exactly one match, enter Mode B. If zero matches, return a list of fuzzy candidates and stop. If multiple matches, ask the user to disambiguate.

### Mode A — Bulk Scan (`--scope=all`)

Optimize for speed. Score quickly, flag issues, move on. Use subagents to parallelize check categories where available.

#### A1. Discovery

1. Glob `skills/**/SKILL.md`
2. Parse frontmatter from each
3. Build a skill inventory table with columns: Skill, Category, Version, Description Length, Body Lines, Refs Count

#### A2. Bulk Checks

Run these check categories. Full checklist lives in `references/audit-checklist.md`.

- **Frontmatter consistency** — required fields present, name matches directory, version is valid semver, description starts with action verb and is ≤200 chars
- **Spec compliance (FAIL)** — no `<` or `>` in frontmatter field values (security rule); description ≤1024 chars (hard ceiling)
- **Spec compliance (WARN)** — reserved-prefix name (`claude-*` / `anthropic-*`). Acceptable as a documented exception when the skill targets the corresponding Anthropic product. Verify the documented exception exists in the skill body.
- **Tool field naming** — `allowed-tools` (hyphen) is canonical; `allowed_tools` (underscore) accepted as deprecated alias (warn but don't fail)
- **Ownership conflict detection** — collect every `owns.directories` and `owns.patterns` across agent roles. Flag overlaps. Validate against the v1.1 resolved conflicts table in `frontmatter-spec.md`
- **Description quality scoring** — has action verb, ≥3 trigger contexts, keyword variants, states exclusions if ambiguous, estimated "pushiness" (low/medium/high)
- **Progressive disclosure** — SKILL.md body line + word count, references linked from body, body >5,000 words OR >500 lines flagged (soft warning), references >300 lines without TOC flagged
- **Cross-skill consistency** — `composes_with` and `spawned_by` point to real skills, no circular `composes_with` chains, no orphan reference files
- **Coverage gaps** — compare inventory against `docs/architecture.md`, `CLAUDE.md`, and the orchestrator's File Ownership Map; flag roles or workflows referenced but not implemented

#### A3. Triage

Highlight the top 3–5 most impactful issues. Suggest which skills warrant a single-skill follow-up (re-run with `--scope=<name>`). Note which issues `skill-update` can address directly.

### Mode B — Deep Dive (`--scope=<skill-name>`)

Take time. Read every file in the skill's directory tree. Score each rubric dimension 1–5 with specific evidence.

#### B1. Structural Analysis

Read `SKILL.md` and every file in `references/`. Score these dimensions against `references/deep-review-rubric.md`:

1. **Frontmatter compliance** — required fields, types, semver, optional fields used appropriately
2. **Description quality** — action verb, trigger contexts, keyword variants, length, "pushiness"
3. **Progressive disclosure** — body ≤5,000 words (soft warning past 500 lines), references used appropriately, clear pointers
4. **Instruction clarity** — imperative voice, logical flow, no ambiguity, explains "why" not just "what"
5. **Coordination** — ownership declarations, `composes_with` accuracy, no overlaps
6. **Completeness** — referenced files exist, no dead links, validation checklists where needed
7. **Anti-patterns** — see the anti-pattern checklist in `references/audit-checklist.md`, including the model-adaptation checks (reasoning-extraction refusal risk, prior-model over-prescription) that flag a skill written for a superseded model, and the writing-craft checks (leading-word anchor, no-op-sentence deletion, positive-prompting lint). When a skill scores poorly there or "worked before and got worse," hand off to the `model-adaptation` skill's migration audit.

#### B2. Live Trigger Testing

First, check the skill carries a **triggering test** — at least one example phrasing that must activate it (ideally one near-miss that must not). Flag its absence as an issue. Then verify it actually holds.

If `/skill-creator` is available, use its eval infrastructure to test whether the skill actually triggers:

1. Generate 3–5 realistic prompts that **should** trigger this skill
2. Generate 2–3 near-miss prompts that should **not** trigger it
3. Run trigger evaluation via skill-creator's description optimization tooling
4. Report should-trigger hit rate and false-positive rate. List any problem triggers.

If `/skill-creator` is unavailable, skip this phase and note it in the report.

#### B3. Output Quality Sampling

For skills that produce structured output (reports, files, configs):

1. Pick 2 representative test prompts
2. Run them through the skill (or skill-creator's test harness)
3. Compare actual output against the skill's stated format
4. Note gaps between promised and actual output

Optionally (recommended for non-trivial skills), run one prompt with and without the skill and compare — if the skill doesn't measurably improve the output, flag that it may not earn its context cost.

### Phase 4: Report

Both modes write the report following `references/report-format.md`. Output two artifacts:

- `skill-review-report.md` — structured markdown for humans
- `skill-review-report.json` — sidecar consumable by `skill-update`

In Mode A, save to the repo root or a user-specified path. In Mode B, save to `{skill-path}/skill-review-report.{md,json}`.

## Output Handoff

The report is designed to feed directly into **skill-update**, which consumes the JSON sidecar and produces a prioritized edit plan. After writing the report, tell the user:

> "Review complete. [X] issues found. Feed the JSON sidecar into `/skill-update` to generate a fix plan, or re-run me with `--scope=<skill-name>` for a deeper look at [specific skills]."

## Guidelines

- Be constructive — every issue gets a concrete suggestion
- Score honestly but explain reasoning, especially for low scores. Don't nitpick style if the skill is functionally sound
- Weight trigger testing heavily — a skill that doesn't trigger is useless regardless of how well-written it is
- When recommending fixes, expect them applied one focused change at a time with re-validation between — don't bundle many unrelated edits into one pass
- In Mode A, don't read reference file contents unless checking for orphans or broken links. Frontmatter + body line count is enough for most checks
- In Mode B, if the user supplied context (e.g., "it never triggers"), lead with that complaint
- If scope is filtered in Mode A, still validate cross-skill references against the full inventory
- Use subagents to parallelize independent check categories when available

## Data source

Before recommending changes, consult real usage signal: run
`scripts/skill-health.sh report --json` (P2-C skill-health telemetry). It returns
per-skill total invocations, 7-day/30-day success rates, a `declining` flag, and
version drift (recorded vs current `SKILL.md` frontmatter `version`). The math is
deterministic in code, never a prompt. Treat the signal as coarse and
best-effort: the emitter can only attribute a skill on a Skill-tool call and
records `outcome:unknown`, so a skill reported as `no-data` means "unobserved",
not "broken". Use it to prioritize which skills to deep-review and to back up
declining-quality verdicts — never as the sole basis for a low score. See
`contracts/installer/skill-health.md`.

## Reference Files

- `references/audit-checklist.md` — checklist of every per-skill and ecosystem-level check, plus the anti-pattern list
- `references/deep-review-rubric.md` — 1–5 scoring criteria for each of the seven deep-review dimensions, with verdict thresholds (SHIP / NEEDS WORK / MAJOR REWORK)
- `references/report-format.md` — exact shape of the markdown report and JSON sidecar