skills/conventions-improver/SKILL.md
Audit and improve project conventions files (AGENTS.md, CLAUDE.md, GEMINI.md). Scans for all conventions files, evaluates quality against a scoring rubric, outputs a quality report, then makes targeted improvements with user approval. Use when asked to check, audit, update, or improve AGENTS.md or similar files.
npx skillsauth add euxx/claude-skills-for-copilot conventions-improverInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Audit, evaluate, and improve project conventions files (AGENTS.md, CLAUDE.md, GEMINI.md) to ensure AI agents have optimal project context.
This skill can write to conventions files. After presenting a quality report and getting user approval, it applies targeted improvements.
Find all conventions files in the repository using this priority order: AGENTS.md, then CLAUDE.md, then GEMINI.md.
Search for files named AGENTS.md, CLAUDE.md, or GEMINI.md at the root and in any subdirectories.
File Types & Locations:
| Type | Location | Purpose |
| ---------------- | ------------------------------------------- | ----------------------------------------- |
| Project root | ./AGENTS.md (or CLAUDE.md, GEMINI.md) | Primary project context, shared with team |
| Package-specific | ./packages/*/AGENTS.md | Module-level context in monorepos |
| Subdirectory | Any nested location | Feature/domain-specific context |
Note: AI agents auto-discover conventions files in parent directories, so monorepo setups work automatically.
For each conventions file found, evaluate it against the following scoring rubric (total: 100 points):
1. Commands/Workflows (20 points)
2. Architecture Clarity (20 points)
3. Non-Obvious Patterns (15 points)
4. Conciseness (15 points)
5. Currency (15 points)
6. Actionability (15 points)
Grade thresholds: A (90–100), B (70–89), C (50–69), D (30–49), F (0–29)
Assessment process: Read the file completely, cross-reference with actual codebase (verify commands, check if referenced files exist, test architecture descriptions), score each criterion, then list specific issues and propose concrete improvements.
Red flags to note:
Always output the quality report before making any updates.
## Conventions File Quality Report
### Summary
- Files found: X
- Average score: X/100
- Files needing update: X
### File-by-File Assessment
#### 1. ./AGENTS.md (Project Root)
**Score: XX/100 (Grade: X)**
| Criterion | Score | Notes |
|-----------|-------|-------|
| Commands/workflows | X/20 | ... |
| Architecture clarity | X/20 | ... |
| Non-obvious patterns | X/15 | ... |
| Conciseness | X/15 | ... |
| Currency | X/15 | ... |
| Actionability | X/15 | ... |
**Issues:**
- [List specific problems]
**Recommended additions:**
- [List what should be added]
After outputting the quality report, ask for user confirmation before updating.
Update principles — only add information that genuinely helps future AI sessions:
What TO add:
What NOT to add:
Diff format for each change — show file path, a Why: explanation, and a unified diff:
### Update: ./AGENTS.md
**Why:** Build command was missing, causing confusion about how to run the project.
+## Commands
+
+| Command | Description |
+|---------|-------------|
+| `npm run dev` | Start development server |
+| `npm test` | Run test suite |
After user approval, apply changes using file editing tools. Preserve existing content structure.
Use only the sections relevant to the project. Typical sections:
# purpose annotationsKey principles:
Validation checklist before finalizing any update:
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