Test Skills: Triggering & Performance

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Lesson

Without systematic testing, you're guessing. Here's a structured approach that catches the three most common failure modes before your users do.

Three testing areas

Testing area	What it verifies	Common failure when skipped
Triggering	Skill loads at the right times	Skill never activates or activates for everything
Functional correctness	Skill produces correct output	Instructions are ambiguous, output is wrong
Performance comparison	Skill actually helps vs. no skill	Skill adds overhead without improving results

1. Triggering tests

Does your skill load at the right times? Create a test suite with two lists:

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"
- "Set up project tracking for the new feature"
- "Can you create my ProjectHub tasks?"

Should NOT trigger:
- "What's the weather in San Francisco?"
- "Help me write Python code"
- "Create a spreadsheet" (unless your skill handles sheets)
- "What is ProjectHub?" (asking about it, not using it)
- "Write me a poem about project management"

Run each query in a fresh conversation. Targets: 90%+ automatic loading for the should-trigger list, 0% false activations for the should-not list.

Good to know

Test with paraphrased versions of your trigger phrases. Real users won't say "plan sprint" verbatim, they'll say "help me organize next week's work." Your description needs to catch natural variations.

2. Functional tests

Verify the skill produces correct, complete output. Write test cases in given/when/then format:

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 the project
  - No API errors during execution
  - Output summary includes all task links

Run the same test 3-5 times. If output varies significantly, your instructions are ambiguous. Check for:

Check	What to look for	Red flag
Completeness	All workflow steps executed	Steps skipped without explanation
Correctness	Output matches expected format	Wrong field names, missing data
Consistency	Same input produces similar output	Wildly different results each run
Error recovery	Graceful handling of issues	Claude gets stuck or crashes
Edge cases	Unusual inputs handled	Fails on empty input or special characters

3. Performance comparison

Measure before and after:

Without skill:
  - User must explain workflow each time
  - 15 back-and-forth messages to complete task
  - 3 failed API calls requiring manual retry
  - 12,000 tokens consumed
  - Task takes 20 minutes of user attention

With skill:
  - Workflow executes automatically
  - 2 clarifying questions only
  - 0 failed API calls
  - 6,000 tokens consumed
  - Task takes 3 minutes of user attention

Even rough measurements are valuable. If the skill adds complexity without improving the experience, reconsider whether you need it.

AI pitfall

When you test your own skill, you unconsciously prompt in ways that work with it. Get someone else to test, they'll phrase things differently and expose gaps you'd never find yourself.

The iterate-on-one-task method

Pick the single most challenging workflow, the hard case, not an easy warm-up
Work with Claude conversationally until you get the perfect result
Note exactly what instructions and phrasing led to success
Write those winning instructions into your skill
Test the skill on the same hard task, does it work without back-and-forth?
Only then expand to additional scenarios

Reading failure signals

Undertriggering (skill doesn't load when it should)

Signs: Users manually enable the skill, or it loads for explicit invocations ("use the sprint-planner skill") but not paraphrased requests ("help me plan next week's work").

Fix: Add more keywords and trigger phrases to your description. Different users phrase the same request differently, a developer says "plan the sprint," a PM says "organize the backlog."

yaml

# Before (undertriggers):
description: Plans sprints in Linear.

# After (catches more variations):
description: Plans sprints and organizes backlogs in Linear. Use when user
  says "plan sprint", "organize backlog", "what should we work on",
  "create sprint tasks", "prioritize tasks", or "Linear planning".

Overtriggering (skill loads for unrelated queries)

Signs: Users disable the skill because it keeps activating unnecessarily, or it loads for topics outside its domain.

Fix: Narrow your trigger phrases. If your skill triggers on "project" but you only handle Linear projects, specify "Linear project" in your description.

Edge case

A skill with both problems usually has a vague description. The fix is to replace vague terms with specific ones, not to add or remove keywords.

Execution issues (skill loads but doesn't work well)

Signs: Inconsistent results across sessions, users frequently correct Claude's output, or Claude asks questions the skill should already answer.

Fix: Add specificityWhat is specificity?A scoring system that determines which CSS rule wins when multiple rules target the same element. IDs score higher than classes, which score higher than elements. to instructions. Include error handling with concrete solutions. Add examples of good output. If Claude keeps asking for information that's always the same, embed it in the instructions.

Using the skill-creator skill

The skill-creator skill (available on Claude.ai and Claude Code) can accelerate your iteration:

Creating: Generates properly formatted SKILL.md from natural language descriptions
Reviewing: Flags vague descriptions, missing triggers, and instruction quality issues
Iterating: Bring failed examples back and say "update the skill to handle this case better"

A testing checklist

10+ trigger queries tested with 90%+ automatic loading
5+ non-trigger queries tested with 0% false activations
Core workflow tested 3-5 times with consistent results
At least one edge case tested (empty input, missing data, wrong format)
Error handling tested by deliberately causing a common failure
Performance compared with and without the skill (even informally)
Someone other than you has tested the skill

If you can check all of these, your skill is ready to share.

Done

Complete & Next