skill-reviewer
Skill1 starsChanged 12 days agoMITView on GitHub
This skill should be used when the user wants to review, audit, or sanity-check an existing agent skill in this repo against authoring conventions. Common triggers include "review the X skill", "audit this skill", "check skill X against repo conventions", "is this skill any good", "second opinion on skill X", "sanity check skill X", and "review skill X before publishing". Produces a severity-tiered report (error / warn / info) with a Clean section even on pass, and classifies the skill type. Skip when authoring a brand-new skill (use skill-creator) or running general code review (use code-reviewer).
24 other projects use a version of this.
Adopted by
Most recently taken up by
- HyperVon/agent-guidance-kittheir own version, first seen Nov 1, 2026
- jqaisystems/jqai-ai-skillstheir own version, first seen Nov 1, 2026
- harvard-lil/lawskills-hubtheir own version, first seen Nov 1, 2026
- microsoft/GitHub-Copilot-for-Azuretheir own version, first seen Oct 27, 2026
- qxryz/workflowgeneratortheir own version, first seen Oct 25, 2026
- flightctl/ai-workflowstheir own version, first seen Oct 22, 2026
Used with
Projects with it also use skill-builder, security-review and 1 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
BlockSkillskill-builder
Create, edit, or inspect Berd skills stored as skill folders with SKILL.md files. Use when the user wants to build a new skill, update an existing skill, convert a workflow into a reusable skill, or decide whether instructions, scripts, references, or assets belong in a skill.
Also in 3 projects that use this
SentrySkillsecurity-review
Finds exploitable application security vulnerabilities in code changes. Use for Warden security scans, appsec review, OWASP-style checks, authentication or authorization bugs, injection, XSS, SSRF, path traversal, secrets, unsafe crypto, webhook verification, open redirects, or sensitive data exposure.
Also in 3 projects that use this
GoogleSkillcode-review
Adversarial systems, concurrency, memory-model, and architecture code review protocol. Use this skill whenever asked to review code, PRs, diffs, or branches in Filament.
Also in 4 projects that use this
Other versions24 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 24
- bytedance/deer-flow★ 85ksame name
- microsoft/GitHub-Copilot-for-Azure★ 262same name
- Terryc21/skill-reviewer★ 57same name
- qxryz/workflowgenerator★ 48same name
- harvard-lil/lawskills-hub★ 46same name
- kumaran-is/claude-code-onboarding★ 35same name
- flightctl/ai-workflows★ 10same name
- wrsmith108/skill-builder-claude-skill★ 8same name
- alienzhou/doraemon-skills★ 4same name
- jqaisystems/jqai-ai-skills★ 3same name
14 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository17 other files it gives its agents.
- AGENTS.mdAGENTS.md
- code-reviewerSkill
- npm-namerSkill
- skill-creatorSkill
- skill-portabilitySkill
- ts-best-practices-functionalSkill
- ts-best-practicesSkill
- code-reviewerSkill
- live-debugSkill
- npm-namerSkill
- orca-doctorSkill
- personaSkill
- skill-creatorSkill
- skill-portabilitySkill
- svg-creatorSkill
- ts-best-practices-functionalSkill
- ts-best-practicesSkill
The file
We split this skill into the rules it gives an agent
6 rules. 0 are common, 3 are distinctive, and 3 are very specific to this project.
0
Common
Most of the 19 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 46
Do not install the toolkit or assume a sibling skill exists.
- Very specific: line 70
If you skip these and only cite lint-checklist.md, you're guessing at depth.
- Distinctive: line 94
Use this exact format. The Clean section is mandatory — it forces grounding the verdict in specific rules rather than vibes.
- Distinctive: line 125
No grounding. The pass verdict carries no information the user can act on or revisit later.
- Distinctive: line 137
If a skill genuinely has 0 findings, say so in the Clean section.
- Very specific: line 146
Read deep references: description.md, frontmatter.md, xml-usage.md.
“Common” is measured: kept by at least 6 in 10 of the 19 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
skill-reviewer
Reviews an existing skill in this repo against authoring conventions. Produces a severity-tiered report (error / warn / info) plus a Clean section, and classifies the skill type.
Inputs
$ARGUMENTS — one of:
- A skill name (
ts-best-practices) — looks underskills/<name>/then.agents/skills/<name>/ - A path (
skills/foo/SKILL.mdorskills/foo) - Empty — ask: "Which skill should I review?"
Workflow
1. Resolve target + run lint baseline
Locate the skill directory. Confirm SKILL.md exists. In the authoring repository,
when its package script and built toolkit are available, run:
pnpm skill-toolkit lint <skill-name>Capture the lint output verbatim — it's the floor, not the ceiling. Lint passing means mechanical rules pass; it does not mean the skill is well-authored.
For an installed skill in another project, apply the bundled
lint-checklist.md manually instead.
Do not install the toolkit or assume a sibling skill exists. Report
toolkit unavailable; manual checklist used, never a fabricated lint pass.
2. Classify the skill type
Pick exactly one — this dictates which audit lens to apply:
| Type | Examples | Audit focus |
| Discipline | "always run the test", "never use any", "always use Result" | Rationalization table present? Body covers the realistic rationalizations? |
| Technique | "use ts-pattern for branching", "use zod for parsing" | Triggers cover phrasings? Workflow is concrete (not abstract)? |
| Pattern | "use *Params for ≥2-arg fns", "kebab-case files" | Description's Skip when covers counter-examples + recognition (when NOT to fire)? |
| Reference | "API X works like…", "convention Y says…" | Declines questions outside its scope? Reference depth matches the surface it claims? |
State the classification explicitly. If you can't classify it cleanly, that's itself a finding (the skill's purpose is fuzzy).
3. Read the deep references — not just the lint summary
Lint enforces frontmatter shape, naming, anti-shortcut words. Deep references cover what lint can't:
description.md— description quality beyond char countfrontmatter.md— frontmatter schemaxml-usage.md— when to use<example>/<good>/<bad>
If you skip these and only cite lint-checklist.md, you're guessing at depth.
4. Audit frontmatter + description
Beyond the lint pass:
- Description has all 3+ verbatim triggers in double quotes
- Description has an explicit
Skip when …clause naming what the skill does NOT do - Triggers in description are realistic (a real user would say them) — not abstract teacher-ese
- Description states what's distinctive ("Bakes in …") — not just what the skill does
- Body does NOT contain
## When to use/## When NOT to usesections — routing is the description's job, and dispatchers never read the body anyway; duplicating triggers wastes tokens and creates drift risk (warn if present) - Claude Code extension fields present where applicable (
argument-hint,user-invocable, optionallydisable-model-invocationandallowed-tools) and fenced behind the# --- Claude Code extensionscomment. Check supported fields against the bundled frontmatter reference, distinguishing provider extensions from the portable core.
5. Audit body
- ≥ 3
##sections - At least one
<example>block - No
TODO/FIXME/XXX - Workflow steps are numbered actions (not prose)
- Discipline skills SHOULD have a
## Rationalization tablesection — its absence on a discipline skill is awarn - Body ≤ 500 lines
6. Emit severity-tiered output (with Clean section, even on pass)
Use this exact format. The Clean section is mandatory — it forces grounding the verdict in specific rules rather than vibes.
SUMMARY: <N> findings (<E> error / <W> warn / <I> info) — <skill-type> skill
## ERRORS
✗ <file>:<line> <one-line-finding>
<body — what's wrong, why it matters, suggested edit>
## WARNS
⚠ <file>:<line> <one-line-finding>
<body>
## INFOS
ℹ <file>:<line> <one-line-finding>
<body>
## Clean
- <specific rule that passes — e.g., "Description has 5 verbatim triggers in double quotes (description.md:38)">
- <another specific rule>
- <`pnpm skill-toolkit lint <name>` — <pass/fail summary>>Empty severity sections render as ## ERRORS\n(none) — keeps the shape comparable across runs. A pass verdict is 0 error / 0 warn / 0 info plus a populated Clean section.
Anti-patterns (don't do these)
<bad>
"Looks solid, ship it."
</bad>No grounding. The pass verdict carries no information the user can act on or revisit later. Use a Clean section listing specific rules.
<bad>
"Lint passes, looks fine."
</bad>Lint is the floor. This is the lazy-reviewer failure mode — see step 3.
<bad>
Manufactured nits because the skill seemed *too* clean and the reviewer wanted to look thorough.
</bad>If a skill genuinely has 0 findings, say so in the Clean section. Inventing critique to look thorough is worse than missing real issues.
Examples
<example>
<input>"review skills/ts-best-practices — does it still hold up against our conventions?"</input>
<output>
1. Resolve: `skills/ts-best-practices/`. Run `pnpm skill-toolkit lint ts-best-practices` → clean.
2. Classify: **discipline skill** (rules like "use *Params for ≥2-arg fns", "JSDoc on exports").
3. Read deep references: `description.md`, `frontmatter.md`, `xml-usage.md`.
4. Frontmatter audit: trigger parity drift — `SKILL.md` has 6 triggers, `description` and `README.md` have 5. Description missing `"audit this ts file"`.
5. Body audit: rationalization table present (good — discipline skill). `@pkg/fp` in conditionals table leaks an internal-monorepo assumption.
6. Emit:SUMMARY: 3 findings (0 error / 2 warn / 1 info) — discipline skill
## ERRORS
(none)
## WARNS
⚠ description (SKILL.md:5) trigger parity drift vs. README.md (5 vs 6 triggers; missing "audit this ts file")
Add the missing trigger to description and README — the description is the dispatcher's surface.
⚠ SKILL.md:184 `@pkg/fp` reference leaks a private-monorepo assumption into a public skill
Replace with "ternary helper" or add a one-line reference link.
## INFOS
ℹ description (SKILL.md:11) Skip-when bundles two cases with an em dash
Split the framework case (React/Vue/Svelte) from the functional-refactor redirect.
## Clean
- Description has Use-when phrase + 5 verbatim triggers in double quotes (description.md:38)
- Frontmatter has the documented Claude Code extension fields (argument-hint, user-invocable)
- Body has 2 <example> blocks (xml-usage.md ≥ 1 required)
- Rationalization table present — appropriate for a discipline skill (skill-creator step 6)
- pnpm skill-toolkit lint ts-best-practices — 0 error / 0 warn / 0 info</output>
</example><example>
<input>"sanity-check skills/skill-portability — i think it's fine"</input>
<output>
Lint clean. Classified as a **technique skill** (replaces ad-hoc provider knowledge with a structured providers.mjs lookup).SUMMARY: 0 findings (0 error / 0 warn / 0 info) — technique skill
## ERRORS
(none)
## WARNS
(none)
## INFOS
(none)
## Clean
- Description: 720 chars, has Use-when, 5 verbatim triggers, explicit Skip-when (description.md ✓)
- Frontmatter: argument-hint, user-invocable set; no fabricated fields
- Body: ≥ 3 `## ` sections, ≥ 1 `<example>` block, no TODO/FIXME
- Companions: README.md, LICENSE, references/, scripts/ all present
- Single-source-of-truth in providers.mjs reflects the skill's own thesis
- pnpm skill-toolkit lint skill-portability — 0/0/0Nothing manufactured. For provider compatibility, use skill-portability if installed. Its optional URL staleness check is separate from this structural review; do not assume that sibling skill is installed.
</output>
</example>Rationalization table
Captured from baseline transcripts where reviewers without this skill skipped rules. Future reviewers: recognize your own pattern.
| Skipped rule | Verbatim excuse | Why it's wrong |
Read deep references beyond lint-checklist.md | "relied on lint-checklist summary" | Lint enforces mechanical rules; the deep references cover description quality, frontmatter shape, and XML usage rules that lint cannot check |
| Classify the skill type (discipline / technique / pattern / reference) | (omitted entirely) | Different types need different audits — discipline skills require a rationalization table; pattern skills need recognition tests; without classification you're applying the wrong lens |
Use severity-tiered output (error / warn / info) even on a pass | "used numbered findings" / "prose verdict" | Comparable output across runs; numbered lists drift in shape; prose ("looks solid") invites manufactured-nits or vague-pass failure modes |
| Include a Clean section listing what specifically passes | "said 'looks solid' / 'ship it'" | Pass verdicts without specifics rot — six months later nobody knows what was actually checked. Clean sections force grounding in specific rules |
References
description.md— description quality rulesfrontmatter.md— frontmatter schemalint-checklist.md— mechanical rules (the floor)xml-usage.md—<example>/<good>/<bad>boundaries
These references are vendored from the authoring sources via skill.json;
the reviewer does not require sibling skills to be installed.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

