Testing and iteration

Skills can be tested at varying levels of rigor depending on your needs:

• Manual testing in Claude.ai - Run queries directly and observe behavior. Fast iteration, no setup required.
• Scripted testing in Claude Code - Automate test cases for repeatable validation across changes.
• Programmatic testing via skills API - Build evaluation suites that run systematically against defined test sets.

Choose the approach that matches your quality requirements and the visibility of your skill. A skill used internally by a small team has different testing needs than one deployed to thousands of enterprise users.

We've found that the most effective skill creators iterate on a single challenging task until Claude succeeds, then extract the winning approach into a skill. This leverages Claude's in-context learning and provides faster signal than broad testing. Once you have a working foundation, expand to multiple test cases for coverage.

Recommended testing approach

Based on early experience, effective skills testing typically covers three areas:

1. Triggering tests

Goal: Ensure your skill loads at the right times.

Test cases

• ✅ Triggers on obvious tasks
• ✅ Triggers on paraphrased requests
• ❌ Doesn't trigger on unrelated topics

Example test suite

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" (unless ProjectHub skill handles sheets)

2. Functional tests

Goal: Verify the skill produces correct outputs.

Test cases

• Valid outputs generated
• API calls succeed
• Error handling works
• Edge cases covered

Example

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

3. Performance comparison

Goal: Prove the skill improves results vs. baseline.

Use the metrics from Define Success Criteria. Here's what a comparison might look like.

Baseline comparison

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

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

Using the skill-creator skill

The skill-creator skill - available in Claude.ai via plugin directory or download for Claude Code - can help you build and iterate on skills. If you have an MCP server and know your top 2-3 workflows, you can build and test a functional skill in a single sitting - often in 15-30 minutes.

Creating skills

• Generate skills from natural language descriptions
• Produce properly formatted SKILL.md with frontmatter
• Suggest trigger phrases and structure

Reviewing skills

• Flag common issues (vague descriptions, missing triggers, structural problems)
• Identify potential over/under-triggering risks
• Suggest test cases based on the skill's stated purpose

Iterative improvement

• After using your skill and encountering edge cases or failures, bring those examples back to skill-creator
• Example: "Use the issues & solution identified in this chat to improve how the skill handles [specific edge case]"

To use

"Use the skill-creator skill to help me build a skill for [your use case]"

Note: skill-creator helps you design and refine skills but does not execute automated test suites or produce quantitative evaluation results.

Iteration based on feedback

Skills are living documents. Plan to iterate based on:

Undertriggering signals

• Skill doesn't load when it should
• Users manually enabling it
• Support questions about when to use it

Overtriggering signals

• Skill loads for irrelevant queries
• Users disabling it
• Confusion about purpose

Execution issues

• Inconsistent results
• API call failures
• User corrections needed