ado-git-repo-insights

ADO Git Repo Insights

[!NOTE] Python Compatibility: Requires Python 3.12 or later. Uses pandas 3.x.

Extract Azure DevOps Pull Request metrics to SQLite and generate PowerBI-compatible CSVs.

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🚀 Quick Start

Choose your path:

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☁️ Azure DevOps Extension

The ADO Extension provides a self-contained pipeline task with a built-in PR Insights Dashboard directly in your Azure DevOps project.

What you get:

• Pipeline task that extracts PR metrics automatically
• Interactive dashboard in your ADO project navigation
• No Python installation required
• PowerBI-compatible CSV exports

Get started: Extension User Guide

Minimal Pipeline Example

variables:
    - group: ado-insights-secrets # Contains PAT_SECRET

steps:
    - task: ExtractPullRequests@3
      inputs:
          organization: "MyOrg"
          projects: "Project1,Project2"
          pat: "$(PAT_SECRET)"

    - publish: $(Pipeline.Workspace)/aggregates
      artifact: aggregates

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🐍 Python CLI

The Python CLI provides full control for local analysis, custom scripts, and non-ADO CI/CD systems.

# Install (recommended)
pipx install ado-git-repo-insights

# Extract → CSV → dashboard
ado-insights extract --organization MyOrg --projects "Project1,Project2" --pat $ADO_PAT --database ./ado-insights.sqlite
ado-insights generate-csv --database ./ado-insights.sqlite --output ./csv_output
ado-insights build-aggregates --db ./ado-insights.sqlite --out ./dataset
ado-insights dashboard --dataset ./dataset --open

Get started: CLI User Guide — covers uv and pip install paths, configuration files, and CI/CD integration.

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📚 Documentation

👤 For End Users

📖 Reference

⚙️ Operations

🛠️ For Developers

📋 Governance

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⚖️ Feature Comparison

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⚡ How It Works

1. Extract — Fetches completed PRs from Azure DevOps REST API
2. Store — Persists data in SQLite with UPSERT semantics
3. Generate — Produces PowerBI-compatible CSVs and dashboard aggregates
4. Visualize — View metrics in the PR Insights Dashboard

The system uses incremental extraction by default (daily) with optional backfill mode to catch late changes (reviewer votes, status updates).

Comment extraction is opt-in via --include-comments on extract; to cover PR comments for history that predates enabling it, run the one-time backfill-comments subcommand — see the extension guide or CLI guide.

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🤖 ML Features (Optional)

The dashboard supports optional ML-powered features: time-series forecasting for cycle time and throughput (zero-config, no API key) and AI insights for bottleneck analysis (requires an OpenAI API key, ~$0.001–0.01 per pipeline run, only aggregated metrics sent — never PR content, identities, or code).

Setup and troubleshooting: Enable ML Features

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🛠️ Developer Setup

• Recommended: open in a Dev Container — handles all per-platform tooling automatically.
• Native setup (advanced): docs/development/setup.md.
• Contributing: CONTRIBUTING.md.

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🔒 Security

PATs use minimum Code (Read) scope, are never logged, and are never persisted at rest. Full posture: docs/SECURITY.md.

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💬 Support

• Issues & Features: GitHub Issues
• Publisher: OddEssentials

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📄 License

MIT