AI-Powered Codebase Audits & Documentation
Open-source prompt toolkit · github.com/singlas/ai-dev-prompts
The Idea
Inheriting an unfamiliar, multi-repo codebase is one of the slowest parts of real engineering work — understanding what exists, where the risks are, and how it all fits together. ai-dev-prompts is an open-source collection of structured prompts that put an AI model to work on exactly that: reading across repositories and producing consistent, reviewable technical audits and documentation instead of ad-hoc, one-off answers.
What It Produces
The prompts are designed to generate two kinds of artifact from a codebase — a technical audit that surfaces architecture, dependencies, risks, and findings, and a documentation set that explains how the platform actually works. Both are deterministic in shape, so output from different repos stays comparable and easy to review.
The Prompts
Three prompts, run in order from a folder containing all your repos — each builds on the previous. Paste them into Claude Code, Cursor, Gemini CLI, or any agentic AI assistant.
Scans every repo and writes a structured platform documentation set — architecture & API reference, database schema, operations runbook, background jobs — into a dedicated docs repo.
Reads the generated docs plus the source code and produces scored audit reports — security, code quality, performance, testing, infrastructure — with an executive summary and action plan.
Propagates the docs and audit findings back into each repo's AI context
files (.cursorrules, CLAUDE.md) so every future
AI session starts informed.
See It In Action
Two example outputs generated with the toolkit:
Architecture overview, dependency map, prioritised findings, and risk ratings for an example platform.
Structured, navigable documentation explaining how an example platform is built and operated.
Get It
The full prompt toolkit is open source on GitHub: singlas/ai-dev-prompts. The codebase-audit-docs guide walks through running it end-to-end — read the guide.