
codemapper
by zenobi-us
my workstation setup for linux, windows and mac
SKILL.md
name: codemapper description: Use when analyzing codebases for structure, finding symbols, tracing call paths, checking test coverage, or analyzing dependencies - provides instant AST-based code analysis using tree-sitter for Python, JavaScript, TypeScript, Rust, Java, Go, and C
CodeMapper (cm) - Fast Code Analysis
Overview
CodeMapper (cm) uses tree-sitter AST parsing to provide instant code analysis without databases. Get project structure, find symbols, trace call graphs, and analyze dependencies in milliseconds.
Supported Languages: Python, JavaScript, TypeScript, Rust, Java, Go, C, Markdown
When to Use
Use CodeMapper when you need to:
- ✅ Explore unfamiliar codebases (get overview, find structure)
- ✅ Find symbol definitions and usages (functions, classes, methods)
- ✅ Understand call graphs (who calls what, call paths)
- ✅ Check test coverage (find untested code)
- ✅ Analyze git changes at symbol level (breaking changes)
- ✅ Pre-refactoring impact analysis (understand dependencies)
Don't use for:
- ❌ Full-text search (use ripgrep/grep instead)
- ❌ Runtime analysis (use profilers)
- ❌ Code execution (use interpreters/compilers)
Quick Start
# Step 1: Get overview
cm stats .
# Step 2: See file structure (ALWAYS use --format ai for LLMs)
cm map . --level 2 --format ai
# Step 3: Find specific code
cm query <symbol> --format ai
# Step 4: Deep dive into a file
cm inspect ./path/to/file --format ai
🔥 CRITICAL: Always use --format ai when analyzing code for LLM context. This is the most token-efficient format (60-80% reduction).
Essential Commands
| Task | Command |
|---|---|
| Project overview | cm stats . |
| File structure | cm map . --level 2 --format ai |
| Find symbol | cm query <name> --format ai |
| Show implementation | cm query <name> --show-body --format ai |
| Who calls it? | cm callers <symbol> --format ai |
| What does it call? | cm callees <symbol> --format ai |
| Call path A→B | cm trace <from> <to> --format ai |
| Find tests | cm tests <symbol> --format ai |
| Untested code | cm untested . --format ai |
| Breaking changes | cm since <commit> --breaking --format ai |
For complete command reference: Read references/command-reference.md
Key Workflows
Exploring Unknown Code
cm stats .
cm map . --level 2 --format ai
cm query <symbol> --format ai
Before Refactoring
cm callers <function> --format ai # Who depends on this?
cm tests <function> --format ai # Is it tested?
cm callees <function> --format ai # What does it depend on?
Code Health Check
cm untested . --format ai # What's not tested?
cm since <last_release> --breaking --format ai # Breaking changes?
For detailed workflows: Read references/workflows.md
Common Mistakes
❌ Forgetting --format ai
# Bad (verbose, token-heavy)
cm map . --level 2
# Good (compact, LLM-optimized)
cm map . --level 2 --format ai
❌ Using grep for call graphs
# Bad (misses indirect calls, false positives)
grep -r "process_payment"
# Good (accurate AST-based call graph)
cm callers process_payment --format ai
❌ Skipping stats/map
# Bad (jumping to query without context)
cm query something --format ai
# Good (understand structure first)
cm stats .
cm map . --level 2 --format ai
cm query something --format ai
For more examples: Read references/common-mistakes.md
Best Practices
- Always start with overview:
cm stats .thencm map . --level 2 --format ai - Always use
--format aifor LLMs: Token efficiency matters - Fuzzy search first: Default fuzzy matching is more forgiving
- Check before refactoring: Run
cm callersandcm testsbefore changes - Use correct tool: CodeMapper for structure/calls, ripgrep for text search
Troubleshooting
No Symbols Found?
- Check file extensions:
cm stats .shows what's indexed - Try fuzzy search (default) vs
--exact
Slow Queries?
- First run builds cache (~10s)
- Subsequent runs use cache (~0.5s)
Git Commands Fail?
- Must be in a git repository for:
diff,since,blame,history
For detailed troubleshooting: Read references/troubleshooting.md
Reference Documentation
references/command-reference.md- Complete command and flag referencereferences/workflows.md- Detailed workflow patterns for common tasksreferences/common-mistakes.md- Extended examples of what to avoidreferences/troubleshooting.md- Comprehensive troubleshooting guidereferences/integration-examples.md- CI/CD, documentation, code review patterns
Performance
- Small repos (< 100 files): < 20ms instant
- Medium repos (100-1000): ~0.5s with cache
- Large repos (1000+): Fast mode auto-enabled
Cache location: .codemapper/ in project root (auto-managed)
スコア
総合スコア
リポジトリの品質指標に基づく評価
SKILL.mdファイルが含まれている
ライセンスが設定されている
100文字以上の説明がある
GitHub Stars 100以上
1ヶ月以内に更新
10回以上フォークされている
オープンIssueが50未満
プログラミング言語が設定されている
1つ以上のタグが設定されている
レビュー
レビュー機能は近日公開予定です
