project-skill-audit

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Audit a project and recommend the highest-value skills to add or update.

AI & Automation 39,350 stars 6386 forks Updated today MIT

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Quality Score: 99/100

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100
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100
Frontmatter 20%
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Issue Health 10%
50
License 10%
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Description 5%
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Skill Content

# Project Skill Audit ## Overview Audit the project's real recurring workflows before recommending skills. Prefer evidence from memory, rollout summaries, existing skill folders, and current repo conventions over generic brainstorming. Recommend updates before new skills when an existing project skill is already close to the needed behavior. ## When to Use - When the user asks what skills a project needs or which existing skills should be updated. - When recommendations should be grounded in project history, memory files, and local conventions. ## Workflow 1. Map the current project surface. Identify the repo root and read the most relevant project guidance first, such as `AGENTS.md`, `README.md`, roadmap/ledger files, and local docs that define workflows or validation expectations. 2. Build the memory/session path first. Resolve the memory base as `$CODEX_HOME` when set, otherwise default to `~/.codex`. Use these locations: - memory index: `$CODEX_HOME/memories/MEMORY.md` or `~/.codex/memories/MEMORY.md` - rollout summaries: `$CODEX_HOME/memories/rollout_summaries/` - raw sessions: `$CODEX_HOME/sessions/` or `~/.codex/sessions/` 3. Read project past sessions in this order. If the runtime prompt already includes a memory summary, start there. Then search `MEMORY.md` for: - repo name - repo basename - current `cwd` - important module or file names Open only the 1-3 most relevant rollout summaries first. Fall back to raw sessio...

Details

Author
sickn33
Repository
sickn33/antigravity-awesome-skills
Created
4 months ago
Last Updated
today
Language
Python
License
MIT

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