🔍 Git Daily Review

Automated daily AI code review for any Git repository — usable as a CLI, a scheduled routine, or an MCP server for AI agents.

Git Daily Review analyzes your daily commits across multiple repositories, checks them against your project's conventions and quality gates, and produces a structured Markdown report. It ships with an MCP (Model Context Protocol) server, so agents like Claude Code and Claude Desktop can run reviews, read reports, and curate the project knowledge base conversationally.

License: AGPL-3.0-only · Python 3.10+ core · TypeScript MCP server

Highlights

• Any repo, any language — the AI adapts to your stack (TypeScript, Python, Java, Go, Rust, C#, …)

• Multi-repo — monitor as many repositories as you need in a single run

• MCP server — expose reviews, reports, and knowledge-base curation as agent tools

• Multi-provider AI — Anthropic Claude, OpenAI, Google Gemini, or any local model via Ollama (fully offline)

• Self-improving knowledge base — 7-layer project context that learns from each review, with a code-enforced auto-merge policy and human approval for high-impact changes

• RAG-ready — optional integration with an external RAG service for semantic context

• Cross-platform scheduling — macOS (launchd), Linux (cron), Windows (Task Scheduler); secrets stay in .env, never in the scheduler files

• Setup wizard — web-based configuration, no manual YAML editing required

Quick start (CLI)

git clone https://github.com/YOUR_USER/git-daily-review.git
cd git-daily-review

# 1. Configure (opens the wizard in your browser)
python3 setup.py

# 2. Secrets (cloud providers only — Ollama needs none)
cp .env.example .env      # then add e.g. ANTHROPIC_API_KEY=...
chmod 600 .env

# 3. First review
python3 scripts/daily_review.py

# 4. Schedule daily runs
python3 scripts/scheduler.py --install

CLI reference:

python3 scripts/daily_review.py                    # review today
python3 scripts/daily_review.py --date 2026-05-12  # specific date
python3 scripts/daily_review.py --days 5           # last N days
python3 scripts/daily_review.py --collect-only     # git data only, no AI
python3 scripts/daily_review.py --history          # report history index

python3 scripts/kb_manager.py --review             # interactive KB curation
python3 scripts/kb_manager.py --approve <ID>       # non-interactive approve
python3 scripts/kb_manager.py --reject  <ID>       # non-interactive reject
python3 scripts/kb_manager.py --stats

MCP server (use it as an agent)

The mcp/ directory contains a TypeScript MCP server that wraps the review engine.

cd mcp
npm install
npm run build

Register it with Claude Code:

claude mcp add git-daily-review -- node /absolute/path/to/git-daily-review/mcp/dist/index.js

Or add it to Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "git-daily-review": {
      "command": "node",
      "args": ["/absolute/path/to/git-daily-review/mcp/dist/index.js"]
    }
  }
}

Exposed tools:

Then just ask your agent things like “run today's review and summarize the critical issues” or “show me pending KB suggestions and approve the ones about naming conventions”.

Environment overrides: GDR_ROOT (project root, default: repo root), GDR_PYTHON (Python executable, default: python3).

AI providers

Secrets live in a .env file at the project root (see .env.example), loaded automatically at startup. They are never written into launchd plists, crontabs, or batch files.

Knowledge base (7 layers)

config/knowledge-base.yaml gives the reviewer structured project context:

L0 Identity · L1 Architecture · L2 Conventions · L3 Quality Gates
L4 Domain · L5 Integrations · L6 Release

After each run the system extracts new knowledge surfaced by the review and stores it as suggestions in config/kb_suggestions/. The auto-merge policy is enforced by code (not by the model): only new conventions and info/warning gates with confidence ≥ 0.85 can auto-merge, capped at 2 per run. Critical gates, domain rules, and integrations always require human approval — interactively (kb_manager.py --review), via CLI flags, or through the MCP decide_kb_suggestion tool.

Disable learning entirely with kb_learning: false in the ai: section of config.yaml.

Project structure

git-daily-review/
├── setup.py                    ← setup wizard entry point
├── requirements.txt
├── .env.example                ← secrets template (never commit .env)
├── config/
│   ├── config.example.yaml
│   └── knowledge-base.example.yaml
├── scripts/                    ← Python core
│   ├── daily_review.py         ← orchestrator (loads .env at startup)
│   ├── git_collector.py        ← fetch, commits, diffs
│   ├── ai_reviewer.py          ← review pipeline
│   ├── ai_provider.py          ← Anthropic/OpenAI/Ollama/Gemini abstraction
│   ├── kb_updater.py           ← KB learning + code-enforced auto-merge policy
│   ├── kb_manager.py           ← KB curation CLI (interactive + --approve/--reject)
│   ├── rag_client.py           ← optional RAG client
│   ├── report_generator.py     ← Markdown reports + history index
│   ├── scheduler.py            ← launchd / cron / schtasks (no secrets on disk)
│   └── wizard_app.py           ← wizard HTTP backend
├── mcp/                        ← MCP server (TypeScript)
│   └── src/index.ts
├── templates/wizard.html
└── reports/                    ← generated daily reports (git-ignored)

Roadmap

• Slack/email notifications for the daily report

• --ci mode: non-zero exit on critical findings (PR gating)

• Full TypeScript port of the core

• Remote repository support (review without a local clone)

Contributions welcome — open an issue or a PR.

License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0-only). If you run a modified version as a network service, you must offer its source to the users of that service. See LICENSE.