What problem does it solve? Writing an effective skill is hard to get right on the first try, and without structured testing there is no way to know whether a skill actually improves Claude's output or triggers when it should. This Skill provides a complete draft-test-review-improve loop with quantitative benchmarking and description optimization. ## Core Features & Use Cases - Guided Skill Authoring: Interviews the user to capture intent, then drafts a SKILL.md with proper frontmatter, progressive disclosure structure, and writing patterns. - Parallel Evaluation Runs: Spawns with-skill and baseline subagent runs for each test case, grades outputs against assertions, and aggregates pass rates, timing, and token usage into benchmark reports. - Interactive Review Viewer: Generates an HTML viewer (via generate_review.py) so users can inspect outputs side-by-side and leave feedback that drives the next iteration. - Description Optimization: Runs an automated train/test loop that rewrites the skill's description to maximize triggering accuracy on realistic queries. - Use Case: You want Claude to consistently generate properly formatted weekly status reports. Use this Skill to draft the skill, run three realistic test prompts with and without it, review the outputs in the browser viewer, and iterate until the results satisfy you. ## Quick Start Help me create a new skill that formats my weekly status reports, then test it and iterate until it works well.
