Our review
Analyzes the current project structure and generates a customized CLAUDE.md file to guide AI agents.
Strengths
- Automatically detects language, framework, and build tools
- Extracts common commands from package.json, pyproject.toml, Makefile
- Creates a structured CLAUDE.md with hierarchical context
Limitations
- Does not update existing files, only initial creation
- May miss rare or non-standard configurations
- Requires write access to generate the file
When starting a new project or improving AI understanding of an existing codebase.
If a well-maintained CLAUDE.md already exists, or the project is too simple for full configuration.
Security analysis
SafeThe 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.
No concerns found
Examples
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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