repo-analysis

Techniques for deep-diving into unknown codebases. Focuses on dependency graphs, data flow, and architectural patterns. Use when starting a task in an unfamiliar project.

SKILL.md

GitHub

Repository Analysis

Purpose

Rapidly gain a comprehensive understanding of a project's architecture, dependencies, and internal logic.

When to Use This Skill

Use during the first 15 minutes of any task in a project that has not been previously analyzed.

Phase 1: Structural Audit

  1. Map the directory structure: Use the list_dir or mcp_filesystem_directory_tree tools. Do not use ls via bash.
  2. Identify core technologies: Read package.json, requirements.txt, etc., using mcp_filesystem_read_text_file.

Phase 2: Logic Flow Analysis

  1. Trace entry points: Use grep_search to map import trees and function calls. Never use grep inside a bash command.
  2. Monorepo Scale Analysis: For massive monolithic repositories, avoid raw exhaustive grep. Use AST (Abstract Syntax Tree) parsing or vector-based codebase search if available to prevent context limit exhaustion.
  3. Data Model Discovery: Identify the main data models and state management strategy.
  • Did I identify the true root cause, or just a symptom?
  • Is the proposed change isolated, or will it break upstream logic?

Output Format / Delivery

  • A concise context mapping report.
  • A targeted execution plan for fixes.

Behavior Rules

  1. Trust but Verify. Do not rely on outdated READMEs; check the actual code.
  2. Document Findings. Save a summary of the project's "mental model" in artifacts.

Maintenance Notes

  • Initial global core skill.