| name | skill-creator |
| description | Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy. |
Skill Creator
A skill for creating new skills and iteratively improving them.
Core Loop
- Decide what you want the skill to do and roughly how it should do it
- Write a draft of the skill
- Create test prompts and run them
- Evaluate results qualitatively and quantitatively
- Rewrite the skill based on feedback
- Repeat until satisfied
- Expand the test set and try again at larger scale
Creating a Skill
Capture Intent
- What should this skill enable Claude to do?
- When should this skill trigger? (what user phrases/contexts)
- What's the expected output format?
- Should we set up test cases to verify the skill works?
Write the SKILL.md
- name: Skill identifier (lowercase, hyphens for spaces)
- description: When to trigger + what it does. Make descriptions "pushy" -- Claude tends to undertrigger skills, so be explicit about when to use them
- the rest of the skill: Instructions Claude follows when the skill activates
Skill Structure
skill-name/
├── SKILL.md (required)
│ ├── YAML frontmatter (name, description required)
│ └── Markdown instructions (<500 lines ideal)
└── Bundled Resources (optional)
├── scripts/ - Executable code for deterministic/repetitive tasks
├── references/ - Docs loaded into context as needed
└── assets/ - Files used in output (templates, icons, fonts)
Progressive Disclosure
Skills use a three-level loading system:
- Metadata (name + description) - Always in context (~100 words)
- SKILL.md body - In context whenever skill triggers (<500 lines ideal)
- Bundled resources - As needed (unlimited, scripts can execute without loading)
Keep SKILL.md under 500 lines; if approaching this limit, add reference files with clear pointers.
Writing Patterns
Defining output formats:
## Report structure
ALWAYS use this exact template:
# [Title]
## Executive summary
## Key findings
## Recommendations
Examples pattern:
## Commit message format
**Example 1:**
Input: Added user authentication with JWT tokens
Output: feat(auth): implement JWT-based authentication
Writing Style
- Explain WHY things are important rather than heavy-handed MUSTs
- Use theory of mind -- make the skill general, not narrow to specific examples
- Write a draft, then look at it with fresh eyes and improve it
- Prefer imperative form in instructions
Evaluating Skills
Test Cases
Create 2-3 realistic test prompts -- the kind of thing a real user would actually say. Save to evals/evals.json:
{
"skill_name": "example-skill",
"evals": [
{
"id": 1,
"prompt": "User's task prompt",
"expected_output": "Description of expected result",
"files": []
}
]
}
Improvement Guidelines
- Generalize from feedback: Don't overfit to test examples. Skills will be used across many prompts.
- Keep the prompt lean: Remove things that aren't pulling their weight.
- Explain the why: LLMs are smart -- explaining reasoning is more effective than rigid rules.
- Look for repeated work: If all test runs independently wrote similar helper scripts, bundle that script.
- Simplicity criterion: A 0.001 improvement that adds 20 lines of complexity? Probably not worth it.
Description Optimization
The description field is the primary triggering mechanism. To optimize:
Generate Trigger Eval Queries
Create 20 eval queries -- mix of should-trigger and should-not-trigger. Make them realistic with file paths, personal context, casual speech.
Good queries: Specific, realistic, with context
Bad queries: Abstract, generic, obvious
Test and Iterate
Run queries against the skill description, measure trigger accuracy, iterate on the description to improve precision and recall.
Skill Quality Checklist
