A brutally useful quality checker for AI Agent Skills.
Skill Roast analyzes a SKILL.md, AGENTS.md, or system prompt directly in the browser and produces a shareable 0–100 report card.
The web app is currently in private preview. The source, rubric, and v0.2 benchmark are public; the free public URL will open for the launch campaign.
Private preview · Public v0.2 benchmark · Report feedback
- Clarity — Can an agent follow the instructions without guessing?
- Activation — Does the file explain when it should and should not be used?
- Safety — Are sensitive data, destructive actions, permissions, and verification addressed?
- Efficiency — Does every token earn its place?
- Portability — Can the workflow travel across agent environments?
Every report includes dimension scores, prioritized findings, concrete fixes, evidence lines when available, a shareable result URL, and a downloadable social card.
Analysis runs entirely in the browser. Pasted instructions are not uploaded or stored.
Share URLs and prefilled feedback issues contain only generated report data. They never include the original pasted instructions. Do not paste proprietary instructions into GitHub issues.
SKILL.mdvalidates YAML frontmatter as well as the instruction body.AGENTS.md / promptevaluates the instruction body without requiring Skill metadata.
The rubric is optimized for English instructions. It also recognizes common Chinese workflow and safety signals and uses a CJK-aware token estimate.
The launch benchmark scores 20 public Skills from five repositories. Every file is pinned to a commit SHA, linked to its source and license status, and processed by the same v0.2 analyzer used in the browser.
Read the leaderboard and methodology, or reproduce it locally:
npm run benchmarkScores are heuristic diagnostics, not certifications or judgments of the authors. v0.2 cannot inspect a Skill's referenced files, scripts, or assets.
Requirements: Node.js >=22.13.0.
npm install
npm run dev
npm test
npm run lintSkill Roast v0.2 uses deterministic, testable heuristics. It parses frontmatter only from a valid opening YAML block, distinguishes ordinary product mentions from exclusive tool lock-in, and applies separate activation rules to Skills and general instruction files.
The score is a fast diagnostic. Always evaluate a Skill against real tasks before production use.
Use the issue forms for an incorrect score, a missed rule, a bug, or a focused feature request. The next rubric version will be prioritized from repeated false positives and false negatives, not raw feature votes.
- Multi-file Skill folder analysis (
SKILL.mdplus references and scripts) - Expanded multilingual rubric coverage
- Exportable remediation checklist
React, vinext, Vite, and OpenAI Sites. No application backend or database is required.
Released under the MIT License. Third-party benchmark sources remain under their respective licenses and are not redistributed in this repository.
