
Codebase Scanner
FreeAutomate project documentation generation for AI agents.
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 source1. Install with the skills CLI
npx skills add wshobson/agents/scan --agent claude-code2. 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 wshobsonCodebase 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
- Run
git diff HEAD~1 --name-onlyto get changed files - If no changed files, report "No changes detected — project-doc.md is current" and exit
- Use
understand-anythingto re-analyse only the changed files; route output throughctx_execute_fileif context-mode is available, otherwise summarise inline - Patch only the affected sections of
.claude/pipeline/project-doc.md - Update the
Last ScannedandChanged Filesfields - 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".
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