repoguide-ai 0.1.2

Scan any codebase and generate onboarding docs automatically

RepoGuide

A CLI tool that scans any local codebase and generates a detailed onboarding document — the kind of walkthrough a senior dev would write for a new team member, produced automatically.

Works with Anthropic (Claude), OpenAI (GPT), or Ollama (local models, no API key needed).

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What This Demonstrates

Business problem solved: Onboarding to a new codebase is slow. Architecture lives in someone's head, entry points aren't obvious, and setup instructions are outdated or missing. RepoGuide reads the repo and produces a structured markdown guide covering architecture, tech stack, entry points, key patterns, setup instructions, and areas of complexity.

Why this matters for AI engineering: This is AI applied to a real developer workflow — not a chatbot demo. It demonstrates MCP tool orchestration, structured LLM output generation, a provider abstraction that makes the LLM swappable, and a two-pass analysis strategy that keeps token usage efficient by scanning first and reading selectively.

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How It Works

RepoGuide uses a two-pass architecture:

Pass 1 — Structural scan (no LLM, no tokens spent): An MCP server scans the directory tree, detects the tech stack from config files (package.json, requirements.txt, Cargo.toml, etc.), counts file types, and identifies likely entry points based on framework-specific knowledge.

Pass 2 — Selective deep read (targeted, token-efficient): Based on what Pass 1 found, the agent reads only the files that matter — entry points, config files, READMEs. The LLM receives the full tree structure plus contents of key files, not the entire codebase.

Generation: Everything gathered is sent to the configured LLM provider with a structured system prompt. The output is a comprehensive ONBOARDING.md written in a direct, specific tone with actual file names, function references, and code patterns.

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Quick Start

1. Install

From PyPI:

pip install repoguide-ai

From source:

git clone https://github.com/M33p5t3r/codescout-py.git
cd codescout-py
pip install .

2. Run RepoGuide

With Anthropic (default):

set ANTHROPIC_API_KEY=sk-ant-...          # Windows
export ANTHROPIC_API_KEY=sk-ant-...       # Mac/Linux
repoguide

With OpenAI:

set OPENAI_API_KEY=sk-...                 # Windows
export OPENAI_API_KEY=sk-...              # Mac/Linux
repoguide --provider openai

With Ollama (local, no API key):

ollama pull llama3.1                      # Download a model first
repoguide --provider ollama
repoguide --provider ollama --model mistral   # Use a different model

3. Follow the Prompts

You'll be asked for:

• Repository path — absolute path to any local repo
• Ignore patterns — optional comma-separated folder names to skip

RepoGuide scans the repo, shows what it detected, asks you to confirm, then generates and saves ONBOARDING.md in the target repo's root.

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Architecture

┌─────────────────────────────────────────────────────┐
│                  CLI (cli.py)                        │
│                                                     │
│  1. Get repo path from user                         │
│  2. Connect to MCP server                           │
│  3. Call scan_repo → display detected stack          │
│  4. Confirm with user                               │
│  5. Call detect_entry_points → get reading list      │
│  6. Call read_file on each entry point               │
│  7. Send everything to LLM → generate markdown       │
│  8. Save ONBOARDING.md to the target repo            │
└────────────┬──────────────────────┬─────────────────┘
             │ MCP Protocol         │ LLM Call
             ▼                      ▼
┌────────────────────────┐  ┌──────────────────────────┐
│  MCP Server            │  │  Provider (providers.py)  │
│  (repo_server.py)      │  │                          │
│                        │  │  AnthropicProvider       │
│  scan_repo             │  │  OpenAIProvider          │
│  read_file             │  │  OllamaProvider          │
│  detect_entry_points   │  │                          │
└────────────────────────┘  └──────────────────────────┘

MCP Server Tools

Tool	Purpose	Token Cost
scan_repo	Walk directory tree, detect stack from config files, count file types	Zero (pure Python)
read_file	Read a specific file with max line guard	Proportional to file size
detect_entry_points	Suggest key files based on detected frameworks	Zero (pure Python)

LLM Providers

Provider	Command	API Key Required	Best For
Anthropic	--provider anthropic	Yes (ANTHROPIC_API_KEY)	Best output quality (default)
OpenAI	--provider openai	Yes (OPENAI_API_KEY)	Alternative cloud provider
Ollama	--provider ollama	No	Offline use, privacy, free

Override the default model with --model:

repoguide --provider anthropic --model claude-opus-4-6
repoguide --provider openai --model gpt-4o-mini
repoguide --provider ollama --model codellama

Supported Frameworks

The stack detection recognizes: Next.js, React, Vue, Nuxt, SvelteKit, Express, NestJS, FastAPI, Flask, Django, Streamlit, Astro, Remix, Gatsby, and generic Node/Python projects. Adding a new framework means adding entries to the detection maps — no logic changes needed.

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Example Output

Run RepoGuide against any local repo to generate a full ONBOARDING.md. The output covers architecture, tech stack, entry points, key patterns, setup instructions, and areas of complexity — written in a direct, specific tone with actual file names and function references.

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Adding a New Provider

1. Create a class in providers.py that inherits from LLMProvider
2. Implement generate(system_prompt, user_message, max_tokens) -> str
3. Implement validate_config() -> str | None
4. Add it to the PROVIDERS dict

The MCP server, scanning logic, and output format are all provider-agnostic.

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Design Principles

RepoGuide uses the MCP client/server pattern (FastMCP framework) applied to a developer tooling problem. The provider abstraction demonstrates clean separation between orchestration logic and model calls — the LLM is a swappable component, not the product.