What problem does it solve?
Agent Skills often suffer from inconsistent quality, missing critical best practices like progressive disclosure, confirmation nodes for irreversible actions, and proper trigger keyword density in descriptions. These gaps lead to context bloat, missed auto-triggers, unexpected behavior during execution, and poor user experience for both developers and end users of the Skill.
Core Features & Use Cases
- Rubric-based quality review: Audits existing Agent Skills against 10 evidence-based quality rules, outputs P0-P3 graded reports with concrete evidence and fix suggestions for each gap.
- Compliant scaffold generation: Creates pre-validated new Skill directory structures that already pass the quality rubric, eliminating manual setup of required checklists, frontmatter fields, and workflow patterns.
- CI/CD integration: Includes a zero-dependency lint script that can be run in GitHub Actions, pre-commit hooks, or local command lines to automate Skill quality checks as part of development workflows.
- Use case example: A developer building a custom Cursor Skill for design system lookups can use skill-reviewer to audit their initial build, identify missing confirmation nodes and too-long inline code blocks, then generate a new scaffold that already includes all required quality guardrails.
Quick Start
Ask the AI to use the skill-reviewer skill to audit the Agent Skill located at .cursor/skills/my-design-skill and receive a graded quality report with actionable fix recommendations.