File Analysis Athola Claude Night Market B425baec

---
name: file-analysis
description: '- Before architecture reviews to understand module boundaries and file
  organization.'
---

---
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
---

# 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