Skills Eval

Community

athola

skills-eval

Evaluate Claude skill quality through auditing. Use when reviewing or auditing skills.

Overview

Publisher	athola
Repository	`claude-night-market`
Skill name	`skills-eval`
Stars	343
Forks	37
Bundled files	12
License	MIT
Links	Skill source Repository Homepage

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.
12 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by athola on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/athola/claude-night-market/tree/master/plugins/abstract/skills/skills-eval
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/athola/claude-night-market/tree/master/plugins/abstract/skills/skills-eval

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/athola/claude-night-market.git /tmp/claude-night-market
mkdir -p .claude/skills
cp -r /tmp/claude-night-market/plugins/abstract/skills/skills-eval .claude/skills/skills-eval

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skills Eval 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 Skills Eval 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 Skills Eval 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.

Skills Evaluation and Improvement

When NOT To Use

Writing a new skill (use abstract:skill-authoring)
Evaluating hooks (use abstract:hooks-eval)
Evaluating rules (use abstract:rules-eval)

Overview

This framework audits Claude skills against quality standards to improve performance and reduce token consumption. Automated tools analyze skill structure, measure context usage, and identify specific technical improvements. Run verification commands after each audit to confirm fixes work correctly.

The skills-auditor provides structural analysis, while the improvement-suggester ranks fixes by impact. Compliance is verified through the compliance-checker. Runtime efficiency is monitored by tool-performance-analyzer and token-usage-tracker.

Quick Start

Basic Audit

Run a full audit of all skills or target a specific file to identify structural issues.

bash
# Audit all skills
make audit-all

# Audit specific skill
make audit-skill TARGET=path/to/skill/SKILL.md

Analysis and Optimization

Use skill_analyzer.py for complexity checks and token_estimator.py to verify the context budget.

bash
make analyze-skill TARGET=path/to/skill/SKILL.md
make estimate-tokens TARGET=path/to/skill/SKILL.md

Improvements

Generate a prioritized plan and verify standards compliance using improvement_suggester.py and compliance_checker.py.

bash
make improve-skill TARGET=path/to/skill/SKILL.md
make check-compliance TARGET=path/to/skill/SKILL.md

Evaluation Workflow

Start with make audit-all to inventory skills and identify high-priority targets. For each skill requiring attention, run analysis with analyze-skill to map complexity. Generate an improvement plan, apply fixes, and run check-compliance to verify the skill meets project standards. Finalize by checking the token budget for efficiency.

Evaluation and Optimization

Quality assessments use the skills-auditor and improvement-suggester to generate detailed reports. Performance analysis focuses on token efficiency through the token-usage-tracker and tool performance via tool-performance-analyzer. For standards compliance, the compliance-checker automates common fixes for structural issues.

Scoring and Prioritization

We evaluate skills across five dimensions: structure compliance, content quality, token efficiency, activation reliability, and tool integration. Scores above 90 represent production-ready skills, while scores below 50 indicate critical issues requiring immediate attention.

Improvements are prioritized by impact. Critical issues include security vulnerabilities or broken functionality. High-priority items cover structural flaws that hinder discoverability. Medium and low priorities focus on best practices and minor optimizations.

Structural Patterns

Deprecated: skills/shared/modules/ directories. Shared modules must be relocated into the consuming skill's own modules/ directory. The evaluator flags any remaining skills/shared/ as a structural warning.

Current: Each skill owns its modules at skills/<skill-name>/modules/. Cross-skill references use relative paths (e.g., ../skill-authoring/modules/description-writing.md).

Resources

Shared Modules: Cross-Skill Patterns

Skill Selection: See skill-selection-judgment.md
Trigger Patterns: See trigger-patterns.md

Skill-Specific Modules

Trigger Isolation Analysis: See modules/trigger-isolation-analysis.md
Authoring Checklist: See modules/authoring-checklist.md
Evaluation Workflows: See modules/evaluation-workflows.md
Advanced Tool Use Analysis: See modules/advanced-tool-use-analysis.md
Evaluation Framework: See modules/evaluation-framework.md
Integration Patterns: See modules/integration.md
Troubleshooting: See modules/troubleshooting.md
Pressure Testing: See modules/pressure-testing.md
Integration Testing: See modules/integration-testing.md
Performance Benchmarking: See modules/performance-benchmarking.md

Tools and Automation

Tools: Executable analysis utilities in scripts/ directory.
Automation: Setup and validation scripts in scripts/automation/.

Exit Criteria

Every audited skill receives a score across all five dimensions (structure compliance, content quality, token efficiency, activation reliability, tool integration) summing to 100.
Any skill with a deprecated skills/shared/ module reference is listed as a structural warning in the audit output.
make check-compliance TARGET=<skill> exits 0 for skills reported as passing, confirming the compliance-checker agrees with the audit score.
An improvement plan is produced for any skill scoring below 75, with findings ordered by priority: critical > high > medium > low.

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 Skills Eval AI skill do?

Evaluate Claude skill quality through auditing. Use when reviewing or auditing skills.

Why use Skills Eval on TypingMind?

Because you install it once and use it with any model. Skills Eval 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 Skills Eval in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/athola/claude-night-market/tree/master/plugins/abstract/skills/skills-eval. 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 Skills Eval?

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 Skills Eval?

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

Is the Skills Eval AI skill free?

Yes. It is published on GitHub by athola under the MIT license. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Skills Evaluation and Improvement

When NOT To Use

Overview

Quick Start

Basic Audit

Analysis and Optimization

Improvements

Evaluation Workflow

Evaluation and Optimization

Scoring and Prioritization

Structural Patterns

Resources

Shared Modules: Cross-Skill Patterns

Skill-Specific Modules

Tools and Automation

Exit Criteria

Bundled files

Frequently asked questions

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