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]"