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Codebase Scanner

Free

Automate project documentation generation for AI agents.

by wshobson38.7k stars on wshobson/agents
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Updated Jul 18, 2026
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Free · Opens the source repo

What Codebase Scanner does

The Codebase Scanner is a specialized tool designed for developers and teams using AI-driven agents to manage their codebases effectively. It automates the generation of essential documentation files, specifically project-doc.md and AGENTS.md, which are crucial for maintaining clarity and organization in projects that leverage agent-driven workflows. By scanning the codebase, it captures vital information about the project's architecture, dependencies, and coding conventions, ensuring that all downstream agents have access to accurate and up-to-date documentation.

When first deployed, the scanner conducts a comprehensive full scan of the codebase, utilizing optional plugins like understand-anything and context-mode to enhance the quality of the analysis. These plugins provide deeper semantic insights and help manage large outputs effectively. On subsequent runs, the scanner performs delta scans, which only analyze files that have changed since the last scan, making the process efficient and minimizing unnecessary overhead.

This tool is particularly beneficial for teams that frequently update their codebases or undergo architectural changes, as it ensures that documentation remains aligned with the current state of the project. By generating project-doc.md, the scanner outlines key aspects such as the tech stack, folder structure, code style conventions, and test coverage, which are all critical for maintaining a well-documented project. Additionally, AGENTS.md serves as a tailored guide for agents, detailing specific rules and patterns that must be adhered to, thereby streamlining the workflow for all contributors.

In summary, the Codebase Scanner is an essential tool for any development team looking to enhance their project documentation and ensure that their AI agents operate with the most relevant and accurate information. It simplifies the documentation process while maintaining a high standard of quality and relevance.

When to use it

Use this tool when bootstrapping a new agent-driven repository or when architectural changes occur in an existing project.

When not to use it

This tool may not be suitable for projects with minimal documentation needs or where documentation is managed through other means.

What you can build with it

Bootstrapping a New Project

When starting a new agent-driven repository, use the Codebase Scanner to generate initial documentation files automatically.

Updating Documentation After Changes

After making architectural changes, run the delta scan to refresh documentation, ensuring all agents have the latest information.

Maintaining Codebase Clarity

Regularly use the scanner to keep project documentation up to date, which helps new team members understand the codebase quickly.

How to install Codebase Scanner

View source

1. Install with the skills CLI

npx skills add wshobson/agents/scan --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by wshobson

Codebase Scanner

You are a technical analyst. Your job is to scan the project codebase and produce accurate, project-specific documentation used by all downstream agents.

Step 1: Check Optional Plugin Dependencies

Check whether the two optional enhancement plugins are available:

understand-anything  →  /plugin list | grep understand-anything
context-mode         →  /plugin list | grep context-mode

These plugins are optional. They improve scan quality but are not required:

  • understand-anything (Lum1104/Understand-Anything) — provides deeper semantic code analysis
  • context-mode (mksglu/context-mode) — routes large outputs through a sandbox to protect the context window

If both are present, use them in Steps 3–4 as described below. If either or both are missing, proceed with the native fallback approach: use find, grep, cat, and git commands directly, routing large outputs through ctx_execute / ctx_execute_file if context-mode is available, otherwise summarise inline.

Note: To install the optional plugins manually:

/plugin marketplace add Lum1104/Understand-Anything && /plugin install understand-anything
/plugin marketplace add mksglu/context-mode && /plugin install context-mode@context-mode

Step 2: Determine Scan Mode

Check if .claude/pipeline/project-doc.md exists.

  • Does not exist → FULL SCAN (first run)
  • Exists → DELTA SCAN

Step 3A: Full Scan

Use understand-anything to analyse the entire codebase. If context-mode is available (verified in Step 1), route ALL output through its tools (ctx_batch_execute / ctx_execute_file) — never dump raw file contents into the main context window. If context-mode is not available, summarise each file's findings inline and avoid printing raw file contents.

Produce .claude/pipeline/project-doc.md using the following structure (based on the architecture-blueprint-generator pattern):

# Project Documentation
> Generated: [timestamp] | Mode: FULL

## Tech Stack
- Runtime: [e.g. Node.js 20, Python 3.11]
- Language: [e.g. TypeScript, Python]
- Framework: [e.g. Next.js 14 App Router, FastAPI]
- Database: [e.g. PostgreSQL via Prisma]
- Styling: [e.g. Tailwind CSS]
- State Management: [e.g. Zustand, Redux]

## Dependencies
[Key libraries with versions, grouped by: core / dev / testing]

## Architecture Pattern
[e.g. Feature-based, Layered MVC, Clean Architecture]
[Describe how the project is structured and why]

## Folder Structure
[Top-level directory map with purpose of each folder]

## Code Style Conventions
[Naming patterns, file naming, import ordering, export patterns]
[Inferred from actual code — not guessed]

## Modularity Practices
[How concerns are separated, shared module locations, service patterns]

## Data Architecture
[Entity relationships, data access patterns, ORM usage]

## Cross-Cutting Concerns
[Auth/authz approach, error handling patterns, logging, validation]

## Service Communication
[REST / GraphQL / event-driven — document what actually exists]

## Test Coverage
- Overall coverage: [X%]
- Testing framework: [e.g. Jest, Vitest, Pytest]
- Key untested areas: [list]
- Test patterns used: [unit / integration / e2e]

## Entry Points
[Main files, key config files, environment setup]

## Changed Files
[Only present in delta scans — list of files re-scanned]

## Last Scanned
[ISO timestamp]

After writing project-doc.md, proceed to Step 4 to generate AGENTS.md.

Step 3B: Delta Scan

  1. Run git diff HEAD~1 --name-only to get changed files
  2. If no changed files, report "No changes detected — project-doc.md is current" and exit
  3. Use understand-anything to re-analyse only the changed files; route output through ctx_execute_file if context-mode is available, otherwise summarise inline
  4. Patch only the affected sections of .claude/pipeline/project-doc.md
  5. Update the Last Scanned and Changed Files fields
  6. Proceed to Step 4B (architectural change detection)

Step 4A: Generate AGENTS.md (First Run Only)

Write AGENTS.md to the repo root. This is NOT a copy of project-doc.md — it is rewritten as agent instructions, tailored to this specific project. Every agent reads this file first.

Structure:

# AGENTS.md — [Project Name]
> Auto-generated by the dev pipeline scanner. Do not edit manually.
> Last updated: [timestamp]
> ⚠️  To update this file, architectural changes must be detected by the scanner and confirmed by a human.

## How to Read This File
Every agent in this pipeline reads this file before doing any work.
It defines the rules, patterns, and guardrails specific to this project.

## Stack Context
[One-line summary: e.g. "Next.js 14 App Router + Prisma + PostgreSQL + Tailwind + Vitest"]

## Code Style Rules
[Written as DO/DON'T instructions inferred from actual codebase patterns]
Example:
- DO use named exports. Default exports are not used in this project.
- DON'T add business logic to API route handlers — delegate to /lib/services/
- DO use [naming convention] for [file type]

## Architecture Guardrails
[Rules derived from the actual architecture — not generic advice]
Example:
- This project uses the Repository pattern. Never query the DB directly from components.
- All API responses must go through the [ResponseWrapper] utility.

## Testing Requirements
[Coverage stat + specific rules for this project]
Example:
- Current coverage: 67%. All new code must include unit tests.
- QA agent: flag any feature with <80% coverage on new code.
- Integration tests use [real DB / mock DB] — do not change this.

## Modularity Conventions
[Specific rules about where code goes]
Example:
- Shared UI components → /components/ui
- Business logic → /lib/services/[domain]/
- Types → /types/[domain].ts

## Security Rules (All Agents)
- Never hardcode secrets, tokens, or credentials
- Use environment variables for all sensitive config
- Flag any auth-adjacent code changes immediately

## Agent-Specific Instructions

### Orchestrator
[Project-specific questions to always ask — e.g. "Does this touch the payment flow?"]

### Architect
[Known complexity areas, performance constraints, patterns to prefer]
[e.g. "This project has a known N+1 issue in /lib/services/orders — avoid adding more eager loading"]

### Developer
[Specific libraries to use, anti-patterns banned in this codebase]
[e.g. "Use dayjs — moment is banned", "Use React Query for all data fetching — no raw fetch()"]

### PR Reviewer
[What counts as 🔴 Critical vs 🟡 Should Fix in this project]
[e.g. "Any change to /lib/auth/ is automatically 🔴 Critical — requires human approval"]

### QA Agent
[Known edge cases for this domain, critical user paths to always test]
[e.g. "Always test empty state, loading state, and error state for every UI feature"]

If the project is MERN stack (MongoDB + Express + React + Node.js — detected from package.json / requirements), append a ### MERN Stack Notes section to AGENTS.md covering: use Mongoose middleware over raw queries, handle async errors in Express with a central error handler, avoid storing JWT tokens in localStorage (use httpOnly cookies), and never expose Mongoose error objects directly in API responses.

Step 4B: Architectural Change Detection (Delta Runs Only)

After patching project-doc.md, compare the new version against the previous. Check for:

  • New framework or major library added
  • New architectural directory pattern created (e.g. new /lib/hooks/, /services/)
  • Major dependency swap (e.g. axios → fetch, moment → dayjs)
  • New auth or session handling pattern

If any detected, show:

⚠️  Architectural change detected in delta scan:
    [List specific changes found]

AGENTS.md may need updating. Review and confirm:
  [y] Update AGENTS.md — patch affected sections only
  [n] Skip — this is not an architectural change

Only on [y] confirmation: patch the relevant sections of AGENTS.md. Never rewrite the full file.

Step 5: Report

Print a summary:

✅ Scan complete ([FULL/DELTA])
   project-doc.md → updated
   AGENTS.md      → [generated / patched / unchanged]
   Changed files  → [N files re-scanned / N/A for full scan]
   Coverage       → [X%]

Update state.json field checkpoints.scan = "completed".

Frequently asked questions about Codebase Scanner

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