Testing, Debugging, and Distributing Your Skills

Part 5 of a 5-part series on building production-grade skills for Claude

Previous: Part 4: Skill Patterns That Work

A skill that works in your head but breaks in production remains a draft until you validate it. This final part covers how to validate, debug, and ship your skills to users.

Testing Approaches by Rigor

Choose based on your audience:

A skill used internally by a small team has different testing needs than one deployed to thousands of enterprise users. Match your rigor to your risk.

The Three Testing Layers

Layer 1: Triggering Tests

Does your skill load when it should, and only when it should?

Should trigger:
- "Help me set up a new ProjectHub workspace"
- "I need to create a project in ProjectHub"
- "Initialize a ProjectHub project for Q4 planning"

Should NOT trigger:
- "What's the weather in San Francisco?"
- "Help me write Python code"
- "Create a spreadsheet"

Run 10 to 20 test queries, track the auto-trigger rate, and target approximately 90%.

Quick debug trick: Ask Claude directly (“When would you use the [skill-name] skill?”) and it will quote your description back, allowing you to adjust based on what’s missing.

Layer 2: Functional Tests

Does the skill produce correct outputs?

Test: Create project with 5 tasks
Given: Project name "Q4 Planning", 5 task descriptions
When: Skill executes workflow
Then:
  - Project created in ProjectHub
  - 5 tasks created with correct properties
  - All tasks linked to project
  - No API errors

Run the same request 3 to 5 times and compare outputs for structural consistency.

Layer 3: Performance Comparison

Does the skill actually improve results vs. no skill?

Without skill:
- 15 back-and-forth messages
- 3 failed API calls requiring retry
- 12,000 tokens consumed

With skill:
- 2 clarifying questions only
- 0 failed API calls
- 6,000 tokens consumed

This is the metric that matters for adoption. If your skill doesn’t measurably reduce friction, reconsider whether it’s needed.

Common Problems and Fixes

Skill Won’t Upload

“Could not find SKILL.md in uploaded folder” → File not named exactly SKILL.md. Case-sensitive. Verify with ls -la.

“Invalid frontmatter” → YAML formatting issue. Most common mistakes:

# ❌ Missing delimiters
name: my-skill
description: Does things

# ❌ Unclosed quotes
name: my-skill
description: "Does things

# ✅ Correct
---
name: my-skill
description: Does things
---

“Invalid skill name” → Name has spaces or capitals. Use my-cool-skill, not My Cool Skill.

Skill Doesn’t Trigger

Your description is too vague or missing trigger phrases. Quick checklist:

• Is it too generic? (“Helps with projects” won’t match anything specific)
• Does it include phrases users would actually say?
• Does it mention relevant file types?

Skill Triggers Too Often

Three solutions in order of effectiveness:

1. Add negative triggers:

description: >
  Advanced data analysis for CSV files. Use for statistical modeling,
  regression, clustering. Do NOT use for simple data exploration
  (use data-viz skill instead).

2. Narrow the scope:

# Too broad
description: Processes documents

# Specific
description: Processes PDF legal documents for contract review

3. Clarify boundaries:

description: >
  PayFlow payment processing for e-commerce. Use specifically for
  online payment workflows, not for general financial queries.

Instructions Not Followed

Instructions too verbose — Claude loses signal in noise. Keep instructions concise. Move detailed references to separate files.

Critical instructions buried — Put the most important rules at the top. Use ## CRITICAL or ## Important headers. Repeat key constraints if needed.

Ambiguous language:

# ❌ Bad
Make sure to validate things properly

# ✅ Good
CRITICAL: Before calling create_project, verify:
- Project name is non-empty
- At least one team member assigned
- Start date is not in the past

Model shortcuts — If Claude is skipping steps, add explicit encouragement. Note: this is more effective in user prompts than in SKILL.md:

- Take your time to do this thoroughly
- Quality is more important than speed
- Do not skip validation steps

Large Context Issues

If responses seem slow or degraded:

• Keep SKILL.md under 5,000 words
• Move detailed docs to references/
• Evaluate if you have 20 to 50 or more skills enabled simultaneously
• Consider skill “packs” for related capabilities

Iteration Signals

Skills are living documents. Watch for these signals:

Distribution

For Individual Users

1. Download/clone the skill folder
2. Zip it
3. Upload via Claude.ai → Settings → Capabilities → Skills
4. Or place in Claude Code’s skills directory

For Organizations

Admins can deploy skills workspace-wide (shipped December 2025) with automatic updates and centralized management.

Via API

Use the /v1/skills endpoint for programmatic management. Add skills to Messages API requests via container.skills. This requires the Code Execution Tool beta.

Publishing on GitHub

The recommended approach:

1. Host publicly — Clear README (at repo level, not inside the skill folder), installation instructions, example usage with screenshots
2. Link from MCP docs — If your skill enhances an MCP integration, cross-reference them
3. Focus on outcomes, not features:

# ✅ Good positioning
"The ProjectHub skill enables teams to set up complete project
workspaces in seconds (including pages, databases, and templates)
instead of spending 30 minutes on manual setup."

# ❌ Bad positioning
"The ProjectHub skill is a folder containing YAML frontmatter
and Markdown instructions that calls our MCP server tools."

The Open Standard

Anthropic has published Agent Skills as an open standard. Like MCP (Model Context Protocol), skills are designed to be portable across tools and platforms, meaning the same skill should work whether you’re using Claude or other AI platforms. Authors can note platform-specific capabilities in the compatibility field.

Final Checklist

Before shipping:

• 2 to 3 concrete use cases identified and tested
• Folder named in kebab-case, SKILL.md exists (exact spelling)
• YAML frontmatter has --- delimiters, name and description present
• Description includes WHAT and WHEN with specific trigger phrases
• No XML tags (< >) anywhere in frontmatter
• Instructions are specific and actionable, not vague
• Error handling included for common failure modes
• Examples provided for primary use cases
• Triggering tested: obvious tasks, paraphrased requests, unrelated queries
• Functional tests pass across multiple runs
• Performance comparison shows measurable improvement over no-skill baseline

Series Recap

The fastest path is to use skill-creator to generate a first draft, test against your hardest use case, iterate on the description until triggering is reliable, and then ship it. You can build your first working skill in fifteen to thirty minutes.

Resources: The Complete Guide to Building Skills for Claude (PDF) · Anthropic Skills Documentation · GitHub: anthropics/skills · MCP Documentation