Notre avis
Analyse la structure du projet actuel et génère un fichier CLAUDE.md personnalisé pour guider les agents IA.
Points forts
- Détection automatique du langage, framework et outils de build
- Extraction des commandes courantes depuis package.json, pyproject.toml, Makefile
- Création d'un fichier CLAUDE.md structuré avec contexte hiérarchique
Limites
- Ne met pas à jour les fichiers existants, seulement création initiale
- Peut manquer des configurations rares ou non standards
- Requiert un accès en écriture pour générer le fichier
Lorsque vous démarrez un nouveau projet ou que vous souhaitez améliorer la compréhension d'un projet existant par l'IA.
Si un CLAUDE.md est déjà en place et bien maintenu, ou si le projet est trop simple pour justifier une configuration complète.
Analyse de sécurité
SûrThe skill only reads local project files using non-destructive shell commands (ls, cat, find, grep) and writes a CLAUDE.md file. No network access, data exfiltration, or destructive operations are performed.
Aucun point d'attention détecté
Exemples
Initialize project context for this codebaseCreate a CLAUDE.md file for this projectBootstrap the CLAUDE.md and suggest supporting filesname: init description: Bootstrap CLAUDE.md by analyzing the current codebase structure allowed-tools: Bash, Read, Glob, Grep, Write disable-model-invocation: true
Initialize Project Context
Analyze the current codebase and generate a customized CLAUDE.md file.
Instructions
Step 1: Analyze Project Structure
# Find project type indicators
ls -la
cat package.json 2>/dev/null | head -20
cat pyproject.toml 2>/dev/null | head -20
cat Cargo.toml 2>/dev/null | head -20
cat go.mod 2>/dev/null | head -20
# Find existing documentation
ls README* 2>/dev/null
ls docs/ 2>/dev/null
# Identify directory structure
find . -type d -maxdepth 2 | grep -v node_modules | grep -v .git | grep -v __pycache__ | grep -v .venv | head -30
# Find test directories
find . -type d -name "test*" -o -name "*tests" 2>/dev/null | head -10
# Check for existing Claude config
ls -la .claude/ 2>/dev/null
cat CLAUDE.md 2>/dev/null
Step 2: Detect Tech Stack
Identify:
- Language: Python, TypeScript, Go, Rust, etc.
- Framework: FastAPI, Express, React, etc.
- Build tools: npm, poetry, cargo, etc.
- Test runner: pytest, vitest, jest, etc.
- Linter: ruff, eslint, golint, etc.
Step 3: Extract Commands
Find available commands from:
package.jsonscriptspyproject.tomlscriptsMakefiletargetsJustfilerecipes- Existing CI/CD configs
Step 4: Generate CLAUDE.md
Create a CLAUDE.md with:
# CLAUDE.md - Project Context for AI Agents
## Project Overview
**[Project Name]** is [detected description from README or package.json].
## Architecture Summary
- **[Component 1]**: [detected from directory structure]
- **[Component 2]**: [detected from directory structure]
## Quick Reference
### Required Environment
\`\`\`bash
[detected from .env.example or docs]
\`\`\`
### Common Commands
\`\`\`bash
# Lint
[detected lint command]
# Format
[detected format command]
# Test
[detected test command]
# Build
[detected build command]
\`\`\`
## Hierarchical Context
Claude Code auto-loads:
- `.claude/rules/*.md` - Project-wide rules
- `[component]/CLAUDE.md` - Component-specific context
- `knowledge/` - Domain expertise
## Testing Strategy
[detected from test structure]
## Acceptance Criteria
Before marking a task complete:
1. Code compiles/runs without errors
2. Tests pass
3. Linting passes
4. Committed and pushed
Step 5: Offer to Create Supporting Files
Ask if user wants to also create:
.claude/rules/01-code-standards.md(based on detected stack).claude/settings.json(with appropriate hooks)knowledge/directory structure
Output
After generating CLAUDE.md, display:
## Project Initialized
**Detected Stack**: [language] + [framework]
**Test Runner**: [tool]
**Linter**: [tool]
### Files Created
- CLAUDE.md (project context)
### Recommended Next Steps
1. Review and customize CLAUDE.md
2. Add component-specific CLAUDE.md files
3. Create knowledge skills for your domain
4. Run `/prime` to load context
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