What problem does it solve? Authoring a new Agent Skill by hand means juggling a dozen mechanical rules — frontmatter fields, kebab-case names, dangling-reference checks, Prompting Playbook structure, and the outcome-eval contract — before any real content gets written. This Skill inverts that by generating a skeleton that is already green against the repo's gate scripts, so every failing check afterward points at your own edit. ## Core Features & Use Cases - Deterministic scaffolding: assets/scaffold_skill.py generates a complete skill directory (SKILL.md, README.md, references stub, smoke-test suite, eval stub) that passes validate-skill.sh, prompting-playbook.sh, and frontmatter-standard.sh on the first run. It refuses non-kebab-case or over-long names and never overwrites an existing directory. - Guided authoring: references/authoring-guide.md walks file-by-file from skeleton to shippable, replacing every TODO(repo2skill) marker with real content. - Semantic review: references/semantic-review.md provides the reviewer checklist for substance the mechanical gates cannot judge — orthogonal sections, real deliverable contracts, justified absolutes, and honest evals. - Use Case: You want to add a new skill to an agent-skills repository. Run the scaffolder, confirm make gate-skill SKILL=my-new-skill is green, author the content under gate cover, run the semantic review, and iterate until both the gate and the review pass. ## Quick Start Ask the agent to scaffold a new skill named my-new-skill at the repo root and iterate the quality gates until they pass.