skill-creator

Skill Creator

Default operating mode: autonomous — create or update the skill, run evals, improve it, optimize the description, and return with a final report. Only pause for human review if the user explicitly requests it or you hit an ambiguity that can't be resolved from the evidence alone.

At a high level, the process of creating a skill goes like this:

• Decide what you want the skill to do and roughly how it should do it
• Write a draft of the skill
• Create a few test prompts and run claude-with-access-to-the-skill on them
• Evaluate the results both qualitatively and quantitatively
  • While the runs happen in the background, draft some quantitative evals if there aren't any (if there are some, you can either use as is or modify if you feel something needs to change about them)
  • Use the eval-viewer/generate_review.py script to show the results if the user wants to review
• Rewrite the skill based on eval results, benchmark data, and user feedback (if review was requested)
• Repeat until quality thresholds are met or the user is satisfied
• Optimize the description for triggering accuracy parallel

Figure out where the user is in this process and then jump in and help them progress through these stages. Route based on what they need. Of course, you should always be flexible and if the user is like "I don't need to run a bunch of evaluations, just vibe with me", you can do that instead.

Workspace convention: All eval artifacts go in tmp/<skill-name>-workspace/ under the project root (the directory containing .claude/). This directory is gitignored. Within the workspace, organize by iteration (iteration-1/, iteration-2/, etc.).

Nested invocation: When invoked as a subagent with an explicit outputs directory, use that outputs/ directory as the root for all inner workspaces — not tmp/<skill-name>-workspace/.

Track your progress with tasks/todos — without them, description optimization and the judge step are commonly skipped.

Communicating with the user