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file-analysis

file-analysis

github.com/atholaDevelopment343121 installsUpdated 1d ago
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About file-analysis

file-analysis is a codebase-structure analysis skill for developers preparing architecture reviews, unfamiliar-codebase work, or scope estimates. It maps directories and file patterns, then records potential complexity hotspots.

Key features

  1. 01Identify project and workspace roots
  2. 02Map directory structure
  3. 03Count files by extension
  4. 04Detect configuration and generated files
  5. 05Note large files and nested directories

Use cases

  1. 01Preparing structural context for architecture reviews
  2. 02Mapping an unfamiliar repository before changes
  3. 03Estimating refactoring or migration scope

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/athola/claude-night-market --skill file-analysis

After installing, type/file-analysisin Claude Code to confirm it loaded.

Details

From the project README

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

  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" -not -path "*/.venv/*" -not -path "*/__pycache__/*" -not -path "*/node_modules/*" -not -path "*/.git/*" | 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 . -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

  • 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.

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