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Skill Review

Community
richtabor
skill-review

Reviews and validates agent skills against best practices. Triggers on "review this skill", "check my skill", "validate skill", "is this skill well-written", or when creating/editing skills.

Overview

Publisherrichtabor
Repositoryagent-skills
Skill nameskill-review
Stars
70
Forks
11
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by richtabor on GitHub. Read the source before you install it.

Installation

Install the Skill Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/richtabor/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/skill-review .claude/skills/skill-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Skill Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Skill Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Skill Review

Overview

Validates agent skills against the Agent Skills standard and compiled best practices. Reviews structure, frontmatter, description quality, progressive disclosure, and common anti-patterns.

When to Use

  • User asks to review or validate a skill
  • User is creating a new skill and wants feedback
  • User asks "is this skill well-written?"
  • User mentions skill quality, best practices, or improvement

Review Process

Phase 1: Load References

Before reviewing, read:

  • references/best-practices.md — Comprehensive guidelines
  • references/checklist.md — Quick validation checklist

Phase 2: Identify Target

Determine what to review:

  • Single skill: Review skills/<name>/SKILL.md and its structure
  • All skills: Audit entire skills/ directory
  • New skill draft: Review provided content before creation

Phase 3: Structural Audit

Check the skill directory structure:

skill-name/
├── SKILL.md              # Required
├── references/           # Optional - loaded docs
├── scripts/              # Optional - executable code
└── assets/               # Optional - output files (not loaded)

Verify:

  • SKILL.md exists
  • Directory name matches name in frontmatter
  • References are one level deep (no nested chains)
  • Scripts use forward slashes (no Windows paths)
  • No extraneous files (README.md, CHANGELOG.md, etc.)
  • Script paths in SKILL.md body (scripts/foo.py) exist in directory
  • If scripts use external binaries, dependencies are documented

Phase 4: Frontmatter Validation

Check YAML frontmatter:

yaml
---
name: skill-name          # Required: lowercase, hyphens, ≤64 chars
description: >-           # Required: ≤1024 chars, third-person
  What it does. When to use it.
---

Validate:

  • name: Lowercase with hyphens only ([a-z0-9-])
  • name: ≤64 characters
  • name: No "anthropic" or "claude" in name
  • description: Non-empty, ≤1024 characters
  • description: Third-person voice (not "I can" or "You can")
  • description: Includes what it does AND when to trigger
  • description: Contains specific trigger phrases

Phase 5: Description Quality

The description is the triggering mechanism. Evaluate:

Good descriptions include:

  • Specific actions: "Extract text and tables from PDF files"
  • Trigger phrases: "Use when analyzing Excel files, spreadsheets, or .xlsx"
  • Synonyms users might say: "tabular data, CSV, workbooks"

Bad descriptions:

  • Vague: "Helps with documents"
  • Generic: "Processes data"
  • Missing triggers: "Analyzes spreadsheets" (no "when to use")

Phase 6: Body Analysis

Review SKILL.md body content:

Length:

  • Under 500 lines (check with wc -l)
  • If longer, split into reference files

Progressive Disclosure:

  • Quick start or overview near top
  • Details moved to references/
  • Long reference files (>100 lines) have TOC

Token Efficiency:

  • No obvious explanations (Claude already knows)
  • Examples over lengthy prose
  • Each line justifies its token cost

Degrees of Freedom:

  • High freedom for context-dependent tasks
  • Low freedom for fragile/error-prone tasks
  • Defaults provided when multiple options exist

Phase 7: Anti-Pattern Check

Scan for common issues:

Anti-PatternLook For
Windows pathsscripts\file.py instead of scripts/file.py
Nested referencesA.md → B.md → C.md chains
Time-sensitive info"If before August 2025..."
Magic numbersUnexplained values
Too many options"You can use X, or Y, or Z..." without default
Inconsistent termsMixing "endpoint"/"URL"/"route"
User-facing docsREADME, CHANGELOG, installation guides
First/second person descriptions"I can help" or "You can use"

Phase 8: Report Findings

Present findings using this format:

## Skill Review: [skill-name]

### Summary
[1-2 sentence overall assessment]

### Structure
[✓/✗] Directory organization
[✓/✗] File presence
[✓/✗] Reference depth

### Frontmatter
[✓/✗] name validation
[✓/✗] description validation

### Description Quality
**Score**: [Strong / Adequate / Needs Work]
**Issues**: [List specific problems]
**Suggested rewrite** (if needed):
```yaml
description: >-
  [Improved description]

Body Analysis

Line count: [X] lines Token efficiency: [Good / Could trim] Progressive disclosure: [✓/✗]

Anti-Patterns Found

  • [Issue 1] — Location: file:line
  • [Issue 2] — Location: file:line

Recommendations

  1. [Actionable fix]
  2. [Actionable fix]

## Quick Review Mode

For rapid validation, run through the checklist in `references/checklist.md` and report only failures.

## Resources

### references/best-practices.md
Comprehensive guide covering architecture, design principles, writing effective descriptions, bundled resources, workflow patterns, and advanced patterns from production skills.

### references/checklist.md
Quick-reference validation checklist for fast reviews.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Skill Review AI skill do?

Reviews and validates agent skills against best practices. Triggers on "review this skill", "check my skill", "validate skill", "is this skill well-written", or when creating/editing skills.

Why use Skill Review on TypingMind?

Because you install it once and use it with any model. Skill Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Skill Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/richtabor/agent-skills/tree/main/skills/skill-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Skill Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Skill Review?

As many as you like. As long as a model supports skills, you can use Skill Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Skill Review AI skill free?

It is published on GitHub by richtabor. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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