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code-analyzer

@petbrains/mvp-builder
4
0

Comprehensive codebase analysis for building mental model of project structure, dependencies, and implementation context. Use when needing to: (1) Understand project architecture before review or documentation, (2) Find dependencies and shared modules, (3) Locate implementation markers (AICODE-*), (4) Prepare context for review, memory generation, or agent creation. Triggers on: analyze code, load code context, scan codebase, understand project structure.

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SKILL.md

name code-analyzer
description Comprehensive codebase analysis for building mental model of project structure, dependencies, and implementation context. Use when needing to: (1) Understand project architecture before review or documentation, (2) Find dependencies and shared modules, (3) Locate implementation markers (AICODE-*), (4) Prepare context for review, memory generation, or agent creation. Triggers on: analyze code, load code context, scan codebase, understand project structure.
allowed-tools Read, Bash (*)

Code Analyzer

Analyze codebase to build comprehensive mental model for downstream operations.

Workflow Overview

  1. Scan — Collect facts via bash script (deterministic)
  2. Understand — Interpret structure and stack
  3. Build — Construct dependency graph and mental model
  4. Confirm — Ready for operations

Step 1: Scan Project

Run codebase scanner to collect facts:

.claude/skills/code-analyzer/scripts/scan-codebase.sh

Scanner auto-detects project root (git root or pwd) and collects:

  • Structure: file count, extensions, configs, directories, src modules
  • Markers: AICODE-NOTE, AICODE-TODO, AICODE-FIX with locations
  • Git: branch, modified/added/deleted files

Outputs JSON. No external dependencies required.

Exclusions (automatic)

  • node_modules, .git, dist, build
  • pycache, .venv, venv
  • ai-docs, .next, .nuxt, coverage, .cache

Step 2: Understand Structure

Interpret scan results to determine:

  • Stack: Language(s) from extensions, framework from configs
  • Entry points: Main/index/app files in directories
  • Modules: Domain boundaries from src_modules or directories
  • Conventions: Naming patterns, structure style

Step 3: Build Mental Model

Extract and internalize from scan results:

From structure:

  • Stack: [language] | [framework] | [build-tool]
  • Entry points with types
  • Module list with inferred domains
  • Directory organization

From markers:

  • AICODE-NOTE → Implementation context (why decisions were made)
  • AICODE-TODO → Planned work (incomplete areas)
  • AICODE-FIX → Known issues (from previous reviews)

From git:

  • Current branch → feature context
  • Changed files → review/focus scope

From reading key files:

  • Import patterns → dependency relationships
  • Shared modules → components with 3+ incoming connections
  • Circular dependencies → architectural issues

Step 4: Confirm Readiness

Output minimal confirmation:

✅ Code context loaded: [project-name]
   Stack: [language] | [framework]
   Modules: [count] ([list])
   Markers: [N] NOTE, [N] TODO, [N] FIX
   Ready for: review | documentation | agent-generation

Error Handling

  • Empty project: Report "No source files found"
  • No git repo: Continue without git section (is_repo: false)
  • Permission denied: Report file, continue with available

Usage Notes

This skill prepares context for:

  • Code review (scope, markers, dependencies)
  • Documentation generation (structure, stack)
  • Agent creation (domains, boundaries)
  • Architecture queries

Context remains in memory for entire conversation.