Glooker

Developer impact analytics for GitHub organizations. Glooker pulls commit history, runs LLM-based analysis on each commit, and produces ranked developer reports with metrics like code complexity, PR discipline, AI-assisted coding percentage, and overall impact scores.

Quick Start

git clone https://github.com/Smartling/glooker.git
cd glooker
npm install
cp .env.example .env.local

Edit .env.local with your GitHub token and LLM API key, then:

npm run dev
# Open http://localhost:3000

That's it. Glooker uses SQLite by default — no database setup needed.

Features

• Full commit coverage — uses GitHub's commit search API to capture all commits, not just PR-linked ones
• LLM-powered analysis — each commit is analyzed for complexity (1-10), type (feature/bug/refactor/etc), risk level, and whether it appears AI-generated
• AI detection — two layers: confirmed via Co-Authored-By trailers (Claude, Copilot, Cursor, etc.) and suspected via LLM heuristic analysis
• PR discipline tracking — shows what percentage of each developer's commits went through pull requests
• Resumable reports — interrupted reports can be resumed, skipping already-analyzed commits
• Export — CSV download or Google Sheets
• Multiple LLM providers — OpenAI, Anthropic, any OpenAI-compatible endpoint (Ollama, vLLM, Azure), or Smartling AI Proxy

Report Metrics

Metric	Description
Commits	Total commits in period (all, not just PR-linked)
PRs	Merged pull requests authored
Lines +/-	Lines added and removed
Complexity	Mean LLM-assessed complexity (1-10)
PR%	Percentage of commits that went through a PR
AI%	Percentage of commits with AI assistance (confirmed + suspected)
Impact	Weighted score combining volume, PRs, complexity, and PR discipline
Types	Commit categorization: feature, bug, refactor, infra, docs, test

Configuration

GitHub Token

Create a fine-grained personal access token:

• Resource owner: your org
• Repository access: All repositories
• Repository permissions: Contents (read), Pull requests (read), Metadata (read)
• Organization permissions: Members (read)

LLM Provider

Set LLM_PROVIDER in .env.local:

OpenAI (default)

LLM_PROVIDER=openai
LLM_API_KEY=sk-...
LLM_MODEL=gpt-4o

Anthropic

LLM_PROVIDER=anthropic
LLM_API_KEY=sk-ant-...
LLM_MODEL=claude-sonnet-4-20250514

Local / Custom (Ollama, vLLM, etc.)

LLM_PROVIDER=openai-compatible
LLM_BASE_URL=http://localhost:11434/v1
LLM_MODEL=llama3
LLM_API_KEY=not-needed

Apple Silicon (M1/M2/M3/M4) GPU acceleration: If you install Ollama via an x86 Homebrew (/usr/local/bin/brew), it will run under Rosetta and cannot access the Metal GPU — inference will be CPU-only and much slower. Install Ollama using the official installer or an ARM-native Homebrew (/opt/homebrew/bin/brew) to get full GPU acceleration. Verify with ollama ps — the PROCESSOR column should show 100% GPU, not 100% CPU.

Smartling AI Proxy

For Smartling customers with AI Proxy access:

LLM_PROVIDER=smartling
SMARTLING_BASE_URL=https://api.smartling.com
SMARTLING_ACCOUNT_UID=your_account_uid
SMARTLING_USER_IDENTIFIER=your_user_identifier
SMARTLING_USER_SECRET=your_user_secret
LLM_MODEL=anthropic/claude-sonnet-4-20250514

Database

SQLite (default) — zero config, data stored in ./glooker.db:

# No config needed — this is the default

MySQL — for teams or production:

DB_TYPE=mysql
DB_HOST=localhost
DB_USER=root
DB_PASSWORD=
DB_NAME=glooker

Then initialize: mysql -u root < schema.sql

Docker

# Set your env vars
export GITHUB_TOKEN=github_pat_...
export LLM_API_KEY=sk-...

# Start (MySQL + app)
docker compose up -d

# Open http://localhost:3000

Architecture

Browser (Next.js)  →  API Routes  →  GitHub API
                                  →  LLM Provider (OpenAI / Anthropic / Smartling / custom)
                                  →  SQLite or MySQL

How a report runs

1. List org members via GitHub API
2. For each member, search all commits and merged PRs in the date range
3. Fetch diffs for each commit
4. Detect AI co-authorship from commit message trailers
5. LLM analysis (concurrency-limited) for complexity, type, risk, and AI-generation detection
6. Aggregate per-developer stats and save to database
7. Display ranked table with export options

Key files

src/lib/
├── llm-provider.ts      # LLM provider factory (OpenAI SDK for all providers)
├── smartling-auth.ts     # Smartling OAuth (only loaded when provider=smartling)
├── github.ts             # GitHub API: members, commit/PR search, diffs, AI detection
├── analyzer.ts           # LLM commit analysis prompt + response parsing
├── aggregator.ts         # Per-developer metric rollup
├── report-runner.ts      # Pipeline orchestrator (search → fetch → analyze → save)
├── progress-store.ts     # In-memory progress tracking
└── db/
    ├── index.ts          # DB abstraction (selects SQLite or MySQL)
    ├── sqlite.ts         # SQLite implementation (default)
    └── mysql.ts          # MySQL implementation

Development

npm run dev       # Start dev server
npm run build     # Production build

If you see Cannot find module './638.js', run rm -rf .next and restart.

See CONTRIBUTING.md for contribution guidelines and CLAUDE.md for AI-assisted development context.

License

MIT