Skill Review

skill-review

AI reviewer for Agent Skills. Runs a Standard Review of an isolated skill folder (SKILL.md + optional references/, scripts/, assets/) and produces a structured report with a PASS/WARNING/FAIL/N/A breakdown, an Antipattern Bingo section, and a summary from Exhausted Vitaly.

Review Scopes

The skill asks for the usage context at the start and adapts the scope accordingly:

Context	Stage	Scope
Personal	2	Structure + Workflow (basic) + References RF04–RF12 + Links LK01, LK05
Team	3	+ portability, negative triggers, navigation, stable cross-refs
Repository	4	+ routing, Lifecycle
I don't know	full	everything across all stages without declared target

Check Groups

1. Structure — form and validity

Answers the question: is this a correctly structured Agent Skill, using Anthropic best practices for skill layout and markdown files?

• ST01–ST04 — folder structure, exact SKILL.md name, folder hygiene, correct use of references/, scripts/, assets/
• ST05–ST08 — YAML frontmatter: name, description, trigger phrases, frontmatter safety
• ST09 — SKILL.md size and monolith risk
• ST10–ST13 — gross form antipatterns: skill-prompt, self-generated artifact, vibe-coded core, monolith
• ST14–ST16 — justification of the skill as an artifact

Almost all of Structure lives at stage 2 — covers basic personal usability of the skill.

2. Workflow — can you actually act on this skill

The main practical layer. Checks whether the text becomes an executable procedure.

• WF01–WF04 — imperative instructions, numbered steps, examples, troubleshooting
• WF05–WF10 — six mandatory workflow elements: trigger, inputs, steps, checks, stop, recovery
• WF11–WF14 — safeguards: checkpoints, planning discipline, critical rules in an explicit place
• WF15–WF18 — preconditions, postconditions, boundary conditions, no hardcoded paths
• WF19–WF22 — correct description of MCP steps
• WF23–WF25 — correct handling of sub-agents
• WF26–WF29 — handoff, external planning artifact, context management, extracting independent phases as sub-agents

WF12 and WF13 are conditional: significant only for long skills with an explicit planning artifact.

3. References — context correctness, progressive disclosure, routing hygiene

• RF04–RF12 (stage 2) — is the knowledge layer in references/, is there a file map, explicit triggers, healthy topology, has the reference layer not become a dump
• RF01, RF03, RF13–RF15 (stage 3) — negative triggers, protection from overtriggering, freshness risks, gotchas, extraction of deterministic operations into scripts/
• RF02 (stage 4) — routing to neighboring skills in the ecosystem

4. Links — mechanical link integrity

• LK01, LK05 (stage 2) — do file refs exist, no orphan files
• LK02–LK04 (stage 2) — are anchors and TOC alive
• LK06 (stage 2) — are external URLs alive, if any
• LK07 (stage 3) — are stable cross-refs used via headings, not line numbers

5. Lifecycle — repository maturity

Launched only in a full review (stage 4 or "I don't know").

• LC01–LC03 — mandatory stage-4 markers: owner, version, changelog/version header
• LC04–LC05 — hygiene checks: secrets hygiene and README boundary (appear in the report but are not hard gate markers)

Installation

/plugin marketplace add https://github.com/mindbox-cloud/agent-skills
/plugin install skill-review@mindbox-cloud-plugins

Usage

review this skill
quick skill review
check my skill

Skill: skill-review:skill-review

References (source materials)

1. Agent Skills Specification Core spec for the skill package: folder structure, SKILL.md, progressive disclosure, frontmatter, directory hygiene.

2. Anthropic - Agent Skills Overview Official description of how skills are loaded and used as reusable filesystem-based resources.

3. Anthropic - Effective Context Engineering for AI Agents Foundation for ideas around checkpoints, TODO/planning artifacts, long-context failure modes, and structured note-taking.

4. SkillsBench Research showing that curated skills and human review genuinely improve outcomes.

5. Promptware Engineering Theoretical framework: prompts and skills as first-class software artifacts.

6. Anthropic - Building Effective Agents Practical principles of agentic workflows: simplicity, explicit contracts, transparency.

7. Manus - Context Engineering for AI Agents Practical text on managing long tasks and maintaining the thread of work.

8. Bob Renze - AI Agent Subagent Orchestration External explanation of when to extract a phase as a sub-agent and why it reduces context overload.