Score an existing AI agent skill against a nine-part quality rubric before shipping it.
Iteratively improve a weak SKILL.md file while only keeping changes that pass verification.
Track a history of skill optimization attempts, including which changes were kept or rolled back.
| whisperre/pangu | wamikmk/typesetter | hurapanda/chees | |
|---|---|---|---|
| Stars | 22 | 22 | 21 |
| Language | HTML | HTML | HTML |
| Setup difficulty | easy | easy | easy |
| Complexity | 3/5 | 1/5 | 2/5 |
| Audience | developer | general | general |
Figures from each repo's GitHub metadata at analysis time.
The result-card screenshot script needs Playwright installed via npm install in the pangu-skill folder.
pangu-skill is a tool for evaluating and improving Agent Skills, the instruction files that give AI coding agents like Claude Code a repeatable procedure to follow. It treats a SKILL.md file as something worth continuously improving, not as a document you write once and forget. It sets up test prompts and a baseline score, makes small targeted edits to fix weak spots, checks the results through real or dry runs, and then asks the user to confirm whether to keep the change or roll it back. The project is not a simple formatting checker. It cares about two questions: is the skill written clearly, specifically, and in a way an agent can actually act on, and does the agent's real output actually get better once the skill is applied? The author points out that as teams accumulate more and more skills, manual review tends to miss problems such as documentation that looks complete but skips over failure cases, instructions that are long but lack concrete parameters and checkpoints, or a skill that scores well on format but does not improve real task output compared to a baseline. The evaluation itself uses a nine part scoring system covering things like the clarity of the frontmatter, how clear and executable the workflow steps are, whether failure modes and recovery paths are written down, whether there are explicit checkpoints before key decisions, and whether references and scripts are reachable. The heaviest weighted category is actual measured performance improvement, since a nicely written skill that does not change real output is treated as not truly effective. The whole process runs in five phases, from setting the scope and writing test prompts, through baseline scoring, to a validation gated editing loop where only improvements that pass verification are kept, using git as a safety net to revert regressions, and ending in a summary report of what changed. The repository includes the skill definition itself along with test prompts, reference material, and result card templates, plus a screenshot script built on Playwright. It is installed with a single command using the skills package manager, and it is released under the MIT license.
pangu-skill is a tool that scores and iteratively improves AI agent skill files, keeping only changes that measurably improve the agent's real output.
Mainly HTML. The stack also includes Node.js, Playwright, Git.
MIT license: use, modify, and distribute freely, including for commercial purposes, as long as the copyright notice is kept.
Setup difficulty is rated easy, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
double-check against the repo, no cap.