file-analysis | sanctum | ClaudePluginHub

Skill

Community

file-analysis

11

Description

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.

Install

1

Add the repository(one-time)

$

/plugin marketplace add athola/claude-night-market

2

Install the plugin

$

/plugin install pensive@claude-night-market

Tool Access

This skill inherits all available tools. When active, it can use any tool Claude has access to.

Skill Content

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

file-analysis:root-identified
file-analysis:structure-mapped
file-analysis:patterns-detected
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.

Links

GitHub Stats

2 forks

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