File Analysis

name: file-analysis description: |

Triggers: codebase, file, analysis, structure, exploration Structured file enumeration and content analysis for understanding codebase structure before reviews or refactoring.

Triggers: file analysis, codebase structure, directory mapping, hotspot detection, code exploration, file enumeration, structure mapping, module boundaries

Use when: before architecture reviews to understand file organization, exploring unfamiliar codebases to map structure, estimating scope for refactoring or migration

DO NOT use when: general code exploration - use the Explore agent. DO NOT use when: searching for specific patterns - use Grep directly.

Provides structural context for downstream review and refactoring workflows. category: workspace-ops tags: [files, structure, analysis, codebase, exploration] tools: [Bash, Glob, Grep, TodoWrite] complexity: medium estimated_tokens: 800 dependencies: - sanctum:shared - imbue:evidence-logging version: 1.3.7

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.

Required TodoWrite Items

1. file-analysis:root-identified
2. file-analysis:structure-mapped
3. file-analysis:patterns-detected
4. file-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 -d or find . -type d -maxdepth 2 to 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" | wc -l to count files by extension.
• Identify dominant languages and their file distributions.
• Note configuration files, generated files, and vendored dependencies.
• Run wc -l $(find . -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

• TodoWrite items are completed with concrete observations.
• Downstream workflows (architecture review, refactoring) have structural context.
• File counts, directory layout, and hotspots are documented for reference.

Troubleshooting

Common Issues

Command not found Ensure all dependencies are installed and in PATH

Permission errors Check file permissions and run with appropriate privileges

Unexpected behavior Enable verbose logging with --verbose flag