J-Star Code Reviewer

Local-first, context-aware AI code reviewer powered by LlamaIndex + Groq.

Works with any language — TypeScript, Python, Rust, Go, you name it.

✨ Features

- Local Vector Index — Embeddings stored locally, no external DB
- Gemini Embeddings — Free tier friendly, no OpenAI key needed
- Chunked Reviews — Handles large diffs without rate limits
- Detective Engine — Deterministic checks for common issues
- Dashboard Output — Professional review reports with fix prompts
- Global CLI — Install once, use in any project

---

🚀 Quick Install

$3

``bash

Install globally

npm install -g jstar-reviewer

In any project directory:

jstar setup # Create config files
jstar init # Index the codebase
jstar review # Review staged changes
`

$3

`bash
curl -fsSL https://raw.githubusercontent.com/JStaRFilms/jstar-code-review/v2.0.0/setup.js | node
`

$3

1. Check Config: The tool now auto-creates .env.example and .jstar/ when you run it.
2. Add Keys: Copy .env.example → .env.local and add your GEMINI_API_KEY and GROQ_API_KEY.
3. Index: Run jstar init (or pnpm run index:init) to build the brain.
4. Review: Stage changes (git add) and run jstar review (or pnpm run review).

For a detailed walkthrough, see ONBOARDING.md.


---

`
git diff --staged
│
▼
┌──────────────────┐
│ Detective │ ← Static analysis (secrets, console.log, "use client")
│ Engine │
└────────┬─────────┘
│
▼
┌──────────────────┐
│ Local Brain │ ← Gemini embeddings via LlamaIndex
│ (Retrieval) │
└────────┬─────────┘
│
▼
┌──────────────────┐
│ Chunked Review │ ← Splits diff by file, delays between calls
│ Queue │
└────────┬─────────┘
│
▼
┌──────────────────┐
│ Groq LLM │ ← moonshotai/kimi-k2-instruct-0905
│ (The Judge) │
└────────┬─────────┘
│
▼
📝 Review Report
`

🚀 Quick Start

$3

`bash
pnpm install
`

$3

Create .env.local:

`env
GEMINI_API_KEY=your_gemini_key
GROQ_API_KEY=your_groq_key
`

$3

`bash
pnpm run index:init
`

$3

`bash
git add
pnpm run review
`

📁 Project Structure

`
scripts/
├── indexer.ts # Scans codebase, builds vector index
├── reviewer.ts # Orchestrates review pipeline
├── detective.ts # Static analysis engine
├── gemini-embedding.ts # Google Gemini adapter
└── mock-llm.ts # LlamaIndex compatibility stub

.jstar/
└── storage/ # Persisted embeddings (gitignored)

docs/features/
├── architecture-v2.md # Full architecture docs
├── detective.md # Static analysis rules
├── analyst.md # LLM reviewer (The Judge)
└── ...
`

⚙️ Configuration

Edit scripts/reviewer.ts:

`typescript
const MODEL_NAME = "moonshotai/kimi-k2-instruct-0905";
const MAX_TOKENS_PER_REQUEST = 8000;
const DELAY_BETWEEN_CHUNKS_MS = 2000;
``

📚 Documentation

- Architecture v2
- Detective Engine
- Token Budget
- Chunked Reviews

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Built with ⚡ by J Star Studios