Skill Judge

Evaluate Agent Skills against official specifications and patterns derived from 17+ official examples.

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Core Philosophy

What is a Skill?

A Skill is NOT a tutorial. A Skill is a knowledge externalization mechanism.

Traditional AI knowledge is locked in model parameters. To teach new capabilities:

Traditional: Collect data → GPU cluster → Train → Deploy new version
Cost: $10,000 - $1,000,000+
Timeline: Weeks to months

Skills change this:

Skill: Edit SKILL.md → Save → Takes effect on next invocation
Cost: $0
Timeline: Instant

This is the paradigm shift from "training AI" to "educating AI" — like a hot-swappable LoRA adapter that requires no training. You edit a Markdown file in natural language, and the model's behavior changes.

The Core Formula

Good Skill = Expert-only Knowledge − What Claude Already Knows

A Skill's value is measured by its knowledge delta — the gap between what it provides and what the model already knows.

• Expert-only knowledge: Decision trees, trade-offs, edge cases, anti-patterns, domain-specific thinking frameworks — things that take years of experience to accumulate
• What Claude already knows: Basic concepts, standard library usage, common programming patterns, general best practices

When a Skill explains "what is PDF" or "how to write a for-loop", it's compressing knowledge Claude already has. This is token waste — context window is a public resource shared with system prompts, conversation history, other Skills, and user requests.

Tool vs Skill

Concept	Essence	Function	Example
Tool	What model CAN do	Execute actions	bash, read_file, write_file, WebSearch
Skill	What model KNOWS how to do	Guide decisions	PDF processing, MCP building, frontend design

Tools define capability boundaries — without bash tool, model can't execute commands. Skills inject knowledge — without frontend-design Skill, model produces generic UI.

The equation:

General Agent + Excellent Skill = Domain Expert Agent

Same Claude model, different Skills loaded, becomes different experts.

Three Types of Knowledge in Skills

When evaluating, categorize each section:

Type	Definition	Treatment
Expert	Claude genuinely doesn't know this	Must keep — this is the Skill's value
Activation	Claude knows but may not think of	Keep if brief — serves as reminder
Redundant	Claude definitely knows this	Should delete — wastes tokens

The art of Skill design is maximizing Expert content, using Activation sparingly, and eliminating Redundant ruthlessly.

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Evaluation Dimensions (120 points total)

D1: Knowledge Delta (20 points) — THE CORE DIMENSION

The most important dimension. Does the Skill add genuine expert knowledge?

Score	Criteria
0-5	Explains basics Claude knows (what is X, how to write code, standard library tutorials)
6-10	Mixed: some expert knowledge diluted by obvious content
11-15	Mostly expert knowledge with minimal redundancy
16-20	Pure knowledge delta — every paragraph earns its tokens

Red flags (instant score ≤5):

• "What is [basic concept]" sections
• Step-by-step tutorials for standard operations
• Explaining how to use common libraries
• Generic best practices ("write clean code", "handle errors")
• Definitions of industry-standard terms

Green flags (indicators of high knowledge delta):

• Decision trees for non-obvious choices ("when X fails, try Y because Z")
• Trade-offs only an expert would know ("A is faster but B handles edge case C")
• Edge cases from real-world experience
• "NEVER do X because [non-obvious reason]"
• Domain-specific thinking frameworks

Evaluation questions:

1. For each section, ask: "Does Claude already know this?"
2. If explaining something, ask: "Is this explaining TO Claude or FOR Claude?"
3. Count paragraphs that are Expert vs Activation vs Redundant

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D2: Mindset + Appropriate Procedures (15 points)

Does the Skill transfer expert thinking patterns along with necessary domain-specific procedures?

The difference between experts and novices isn't "knowing how to operate" — it's "how to think about the problem." But thinking patterns alone aren't enough when Claude lacks domain-specific procedural knowledge.

Key distinction:

Type	Example	Value
Thinking patterns	"Before designing, ask: What makes this memorable?"	High — shapes decision-making
Domain-specific procedures	"OOXML workflow: unpack → edit XML → validate → pack"	High — Claude may not know this
Generic procedures	"Step 1: Open file, Step 2: Edit, Step 3: Save"	Low — Claude already knows

Score	Criteria
0-3	Only generic procedures Claude already knows
4-7	Has domain procedures but lacks thinking frameworks
8-11	Good balance: thinking patterns + domain-specific workflows
12-15	Expert-level: shapes thinking AND provides procedures Claude wouldn't know

What counts as valuable procedures:

• Workflows Claude hasn't been trained on (new tools, proprietary systems)
• Correct ordering that's non-obvious (e.g., "validate BEFORE packing, not after")
• Critical steps that are easy to miss (e.g., "MUST recalculate formulas after editing")
• Domain-specific sequences (e.g., MCP server's 4-phase development process)

What counts as redundant procedures:

• Generic file operations (open, read, write, save)
• Standard programming patterns (loops, conditionals, error handling)
• Common library usage that's well-documented

Expert thinking patterns look like:

Before [action], ask yourself:
- **Purpose**: What problem does this solve? Who uses it?
- **Constraints**: What are the hidden requirements?
- **Differentiation**: What makes this solution memorable?

Valuable domain procedures look like:

### Redlining Workflow (Claude wouldn't know this sequence)
1. Convert to markdown: `pandoc --track-changes=all`
2. Map text to XML: grep for text in document.xml
3. Implement changes in batches of 3-10
4. Pack and verify: check ALL changes were applied

Redundant generic procedures look like:

Step 1: Open the file
Step 2: Find the section
Step 3: Make the change
Step 4: Save and test

The test:

1. Does it tell Claude WHAT to think about? (thinking patterns)
2. Does it tell Claude HOW to do things it wouldn't know? (domain procedures)

A good Skill provides both when needed.

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D3: Anti-Pattern Quality (15 points)

Does the Skill have effective NEVER lists?

Why this matters: Half of expert knowledge is knowing what NOT to do. A senior designer sees purple gradient on white background and instinctively cringes — "too AI-generated." This intuition for "what absolutely not to do" comes from stepping on countless landmines.

Claude hasn't stepped on these landmines. It doesn't know Inter font is overused, doesn't know purple gradients are the signature of AI-generated content. Good Skills must explicitly state these "absolute don'ts."

Score	Criteria
0-3	No anti-patterns mentioned
4-7	Generic warnings ("avoid errors", "be careful", "consider edge cases")
8-11	Specific NEVER list with some reasoning
12-15	Expert-grade anti-patterns with WHY — things only experience teaches

Expert anti-patterns (specific + reason):

NEVER use generic AI-generated aesthetics like:
- Overused font families (Inter, Roboto, Arial)
- Cliched color schemes (particularly purple gradients on white backgrounds)
- Predictable layouts and component patterns
- Default border-radius on everything

Weak anti-patterns (vague, no reasoning):

Avoid making mistakes.
Be careful with edge cases.
Don't write bad code.

The test: Would an expert read the anti-pattern list and say "yes, I learned this the hard way"? Or would they say "this is obvious to everyone"?

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D4: Specification Compliance — Especially Description (15 points)

Does the Skill follow official format requirements? Special focus on description quality.

Score	Criteria
0-5	Missing frontmatter or invalid format
6-10	Has frontmatter but description is vague or incomplete
11-13	Valid frontmatter, description has WHAT but weak on WHEN
14-15	Perfect: comprehensive description with WHAT, WHEN, and trigger keywords

Frontmatter requirements:

• name: lowercase, alphanumeric + hyphens only, ≤64 characters
• description: THE MOST CRITICAL FIELD — determines if skill gets used at all

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Why description is THE MOST IMPORTANT field:

┌─────────────────────────────────────────────────────────────────────┐
│  SKILL ACTIVATION FLOW                                              │
│                                                                     │
│  User Request → Agent sees ALL skill descriptions → Decides which  │
│                 (only descriptions, not bodies!)     to activate    │
│                                                                     │
│  If description doesn't match → Skill NEVER gets loaded            │
│  If description is vague → Skill might not trigger when it should  │
│  If description lacks keywords → Skill is invisible to the Agent   │
└─────────────────────────────────────────────────────────────────────┘

The brutal truth: A Skill with perfect content but poor description is useless — it will never be activated. The description is the only chance to tell the Agent "use me in these situations."

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Description must answer THREE questions:

1. WHAT: What does this Skill do? (functionality)
2. WHEN: In what situations should it be used? (trigger scenarios)
3. KEYWORDS: What terms should trigger this Skill? (searchable terms)

Excellent description (all three elements):

description: "Comprehensive document creation, editing, and analysis with support
for tracked changes, comments, formatting preservation, and text extraction.
When Claude needs to work with professional documents (.docx files) for:
(1) Creating new documents, (2) Modifying or editing content,
(3) Working with tracked changes, (4) Adding comments, or any other document tasks"

Analysis:

• WHAT: creation, editing, analysis, tracked changes, comments
• WHEN: "When Claude needs to work with... for: (1)... (2)... (3)..."
• KEYWORDS: .docx files, tracked changes, professional documents

Poor description (missing elements):

description: "处理文档相关功能"

Problems:

• WHAT: vague ("文档相关功能" — what specifically?)
• WHEN: missing (when should Agent use this?)
• KEYWORDS: missing (no ".docx", no specific scenarios)

Another poor example:

description: "A helpful skill for various tasks"

This is useless — Agent has no idea when to activate it.

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Description quality checklist:

• Lists specific capabilities (not just "helps with X")
• Includes explicit trigger scenarios ("Use when...", "When user asks for...")
• Contains searchable keywords (file extensions, domain terms, action verbs)
• Specific enough that Agent knows EXACTLY when to use it
• Includes scenarios where this skill MUST be used (not just "can be used")

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D5: Progressive Disclosure (15 points)

Does the Skill implement proper content layering?

Skill loading has three layers:

Layer 1: Metadata (always in memory)
         Only name + description
         ~100 tokens per skill

Layer 2: SKILL.md Body (loaded after triggering)
         Detailed guidelines, code examples, decision trees
         Ideal: < 500 lines

Layer 3: Resources (loaded on demand)
         scripts/, references/, assets/
         No limit

Score	Criteria
0-5	Everything dumped in SKILL.md (>500 lines, no structure)
6-10	Has references but unclear when to load them
11-13	Good layering with MANDATORY triggers present
14-15	Perfect: decision trees + explicit triggers + "Do NOT Load" guidance

For Skills WITH references directory, check Loading Trigger Quality:

Trigger Quality	Characteristics
Poor	References listed at end, no loading guidance
Mediocre	Some triggers but not embedded in workflow
Good	MANDATORY triggers in workflow steps
Excellent	Scenario detection + conditional triggers + "Do NOT Load"

The loading problem:

Loading too little ◄─────────────────────────────────► Loading too much
- References sit unused                    - Wastes context space
- Agent doesn't know when to load          - Irrelevant info dilutes key content
- Knowledge is there but never accessed    - Unnecessary token overhead

Good loading trigger (embedded in workflow):

### Creating New Document

**MANDATORY - READ ENTIRE FILE**: Before proceeding, you MUST read
[`docx-js.md`](docx-js.md) (~500 lines) completely from start to finish.
**NEVER set any range limits when reading this file.**

**Do NOT load** `ooxml.md` or `redlining.md` for this task.

Bad loading trigger (just listed):

## References
- docx-js.md - for creating documents
- ooxml.md - for editing
- redlining.md - for tracking changes

For simple Skills (no references, <100 lines): Score based on conciseness and self-containment.

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D6: Freedom Calibration (15 points)

Is the level of specificity appropriate for the task's fragility?

Different tasks need different levels of constraint. This is about matching freedom to fragility.

Score	Criteria
0-5	Severely mismatched (rigid scripts for creative tasks, vague for fragile ops)
6-10	Partially appropriate, some mismatches
11-13	Good calibration for most scenarios
14-15	Perfect freedom calibration throughout

The freedom spectrum:

Task Type	Should Have	Why	Example Skill
Creative/Design	High freedom	Multiple valid approaches, differentiation is value	frontend-design
Code review	Medium freedom	Principles exist but judgment required	code-review
File format operations	Low freedom	One wrong byte corrupts file, consis how to use skill-judge CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity How to use skill-judge on Cursor AI-first code editor with Composer 1 Prerequisites Before installing skills in Cursor, ensure your development environment meets these requirements: ›Cursor installed and configured on your development machine ›Node.js version 16.0+ with npm package manager (verify with node --version) ›Active project directory or workspace where you want to add skill-judge 2 Execute installation command Execute the skills CLI command in your project's root directory to begin installation: $npx skills add https://github.com/davila7/claude-code-templates --skill skill-judgecopy The skills CLI fetches skill-judge from GitHub repository davila7/claude-code-templates and configures it for Cursor. 3 Select Cursor when prompted The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor: ◆ Which agents do you want to install to? │ │ ── Universal (.agents/skills) ── always included ──── │ • Amp │ • Antigravity │ • Cline │ • Codex │ ●Cursor(selected) │ • Cursor │ • Windsurf 4 Verify installation Confirm successful installation by checking the skill directory location: .cursor/skills/skill-judge Reload or restart Cursor to activate skill-judge. Access the skill through slash commands (e.g., /skill-judge) or your agent's skill management interface. ⚠ Security & Verification Notice We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use. Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment. Additional Resources ›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills? List & Monetize Your Skill Submit your Claude Code skill and start earning GET_STARTED → Use Cases▌ User Story & Requirements Generation Create detailed user stories, acceptance criteria, and feature specs Example Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios ✓ Reduce spec writing time by 50%, ensure comprehensive coverage Competitive Analysis Research competitors, compare features, identify gaps Example Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities ✓ Complete competitive research in 2 hours instead of 2 days Roadmap Prioritization Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs Example Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale ✓ Make data-driven prioritization decisions faster Stakeholder Communication Draft PRDs, status updates, and stakeholder presentations Example Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement ✓ Save 3-5 hours/week on communication overhead Implementation Guide▌ Prerequisites ›Claude Desktop or compatible AI client ›Access to product documentation and roadmap tools (Jira, Notion, etc.) ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.) ›Stakeholder contact information and communication channels Time Estimate 30-60 minutes to see productivity improvements Installation Steps 1.Install product management skill 2.Start with user story generation for known feature 3.Progress to competitive analysis: research 2-3 competitors 4.Use for roadmap prioritization: apply RICE/ICE scoring 5.Draft stakeholder communications and refine based on feedback 6.Build template library for recurring PM tasks 7.Share effective prompts with product team Common Pitfalls ⚠Not validating competitive research—verify facts before sharing ⚠Accepting user stories without involving engineering team ⚠Over-relying on frameworks without qualitative judgment ⚠Not customizing outputs to company culture and communication style ⚠Skipping stakeholder validation of generated requirements Best Practices▌ ✓ Do +Validate research and competitive analysis with real data +Collaborate with engineering when generating technical requirements +Customize frameworks and templates to your company context +Use skill for first drafts, refine with stakeholder input +Document successful prompt patterns for PM tasks +Combine AI efficiency with human judgment and intuition ✗ Don't −Don't publish competitive analysis without fact-checking −Don't finalize user stories without engineering review −Don't make prioritization decisions solely on AI scoring −Don't skip customer validation of generated requirements −Don't ignore company-specific context and culture 💡 Pro Tips ★Provide context: company goals, constraints, customer feedback ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap' ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec' ★Use skill for 70% generation + 30% customization to company needs When to Use This▌ ✓ Use When Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work. ✗ Avoid When Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed. Learning Path▌ 1Basic: user stories, feature specs, status updates 2Intermediate: competitive analysis, prioritization frameworks, PRDs 3Advanced: product strategy, go-to-market planning, OKR setting 4Expert: product vision, market positioning, business model innovation Discussion Product Hunt–style comments (not star reviews) post comment No comments yet — start the thread. general reviews Ratings 4.5★★★★★37 reviews ★★★★★Ganesh Mohane· Dec 24, 2024 Registry listing for skill-judge matched our evaluation — installs cleanly and behaves as described in the markdown. ★★★★★Shikha Mishra· Dec 20, 2024 We added skill-judge from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront. ★★★★★Aanya Thompson· Dec 16, 2024 skill-judge reduced setup friction for our internal harness; good balance of opinion and flexibility. ★★★★★Chinedu Ramirez· Dec 12, 2024 skill-judge has been reliable in day-to-day use. Documentation quality is above average for community skills. ★★★★★Yash Thakker· Nov 11, 2024 skill-judge fits our agent workflows well — practical, well scoped, and easy to wire into existing repos. ★★★★★Aditi Tandon· Nov 7, 2024 skill-judge has been reliable in day-to-day use. Documentation quality is above average for community skills. ★★★★★Chinedu Menon· Nov 3, 2024 skill-judge reduced setup friction for our internal harness; good balance of opinion and flexibility. ★★★★★Camila Thompson· Oct 26, 2024 Useful defaults in skill-judge — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows. ★★★★★Chinedu Perez· Oct 22, 2024 I recommend skill-judge for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area. ★★★★★Dhruvi Jain· Oct 2, 2024 skill-judge is among the better-maintained entries we tried; worth keeping pinned for repeat workflows. showing 1-10 of 37 prev1 / 4next bookmarkshare