name: file-analysis description: 'Map file structure and organization. Use when preparing review or refactor workflows that need a file map.' version: 1.9.3 alwaysApply: false category: workspace-ops tags:
- files
- structure
- analysis
- codebase
- exploration tools: [] complexity: medium model_hint: standard estimated_tokens: 800 dependencies:
- sanctum:shared
- imbue:proof-of-work
File Analysis
When To Use
- Before architecture reviews to understand module boundaries and file organization.
- When exploring unfamiliar codebases to map structure before making changes.
- As input to scope estimation for refactoring or migration work.
When NOT To Use
- General code exploration - use the Explore agent
- Searching for specific patterns - use Grep directly
Required TodoWrite Items
file-analysis:root-identifiedfile-analysis:structure-mappedfile-analysis:patterns-detectedfile-analysis:hotspots-noted
Mark each item as complete as you finish the corresponding step.
Step 1: Identify Root (file-analysis:root-identified)
- Confirm the analysis root directory with
pwd. - Note any monorepo boundaries, workspace roots, or subproject paths.
- Capture the project type (language, framework) from manifest files (
package.json,Cargo.toml,pyproject.toml, etc.).
Step 2: Map Structure (file-analysis:structure-mapped)
- Run
tree -L 2 -dorfind . -type d -maxdepth 2to capture the top-level directory layout. - Identify standard directories:
src/,lib/,tests/,docs/,scripts/,configs/. - Note any non-standard organization patterns that may affect downstream analysis.
Step 3: Detect Patterns (file-analysis:patterns-detected)
- Use
find . -name "*.ext" -not -path "*/.venv/*" -not -path "*/__pycache__/*" -not -path "*/node_modules/*" -not -path "*/.git/*" | wc -lto count files by extension. - Identify dominant languages and their file distributions.
- Note configuration files, generated files, and vendored dependencies.
- Run
wc -l $(find . -not -path "*/.venv/*" -not -path "*/__pycache__/*" -not -path "*/node_modules/*" -not -path "*/.git/*" -name "*.py" -o -name "*.rs" | head -20)to sample file sizes.
Step 4: Note Hotspots (file-analysis:hotspots-noted)
- Identify large files (potential "god objects"):
find . -type f -exec wc -l {} + | sort -rn | head -10. - Flag deeply nested directories that may indicate complexity.
- Note files with unusual naming conventions or placement.
Exit Criteria
TodoWriteitems are completed with concrete observations.- Downstream workflows (architecture review, refactoring) have structural context.
- File counts, directory layout, and hotspots are documented for reference.