skill-creator

Skill Creator

Create skills and iteratively improve them through measurement.

Output style directive (applies to every generated skill/agent)

Generated SKILL.md and agent bodies must be written as dense informational text focused on accuracy. Minimize prose, maximize signal, no filler.

• No motivational framing, no pep talk, no "this will help you...".
• No repeated restatement of the same constraint in different words.
• Prefer tables, numbered phases, and bullet lists over paragraphs.
• Every sentence must carry information the model will act on; cut anything that is only atmosphere.
• Explanations of "why" stay short and attached to the rule they justify -- one clause, not a paragraph.

New and edited skill files are written to the Dense-Complete Writing standard: skills/shared-patterns/dense-complete-writing.md. That file is the canonical rule; the bullets above are its application to skill scaffolding.

This is a generation constraint on the outputs of this skill, not a style note for this skill's own prose. Enforce it during the "Write the SKILL.md" phase and during any agent scaffolding.

The process:

• Decide what the skill should do and how it should work
• Write a draft of the skill
• Create test prompts and run claude-with-the-skill on them
• Evaluate the results -- both with agent reviewers and optionally human review
• Improve the skill based on what the evaluation reveals
• Repeat until the skill demonstrably helps

Figure out where the user is in this process and help them progress. If they say "I want to make a skill for X", help narrow scope, write a draft, write test cases, and run the eval loop. If they already have a draft, go straight to testing.

---

Creating a skill

Capture intent

Start by understanding what the user wants. The current conversation might already contain a workflow worth capturing ("turn this into a skill"). If so, extract:

1. What should this skill enable Claude to do?
2. When should this skill trigger? (what user phrases, what contexts)
3. What is the expected output?
4. Are the outputs objectively verifiable (code, data transforms, structured files) or subjective (writing quality, design aesthetics)? Objectively verifiable outputs benefit from test cases. Subjective outputs are better evaluated by human review.