Use data to discover best practices
Git AI helps you figure out what works by correlating developer workflows and prompting techniques with positive signals: successful "one-shot" prompts, high AI-accepted rates, low AI-code churn, etc.
- Identify what the teams who use AI most effectively do differently
- Help spread the best practices
AI Adoption
73%
Parallel Agents
3
AI Acceptance Rate
81%
Code Durability
74%
AI-Code Incidents
↓56%
Next tip: You've gotten much better at steering this month. Try launching a few background agents to run overnight →
Private dashboards for every engineer
Each developer gets a personal dashboard for tracking their AI-adoption, analyzing their prompts, and surfacing new patterns and tips for them based on what's been working on your team.
- Personal AI adoption tracking
- Suggested workflows based on what prompting practices work best
frontend-app
Agents.md, 14 skills, full test coverage
92
score
api-service
Agents.md, 6 skills, partial tests
78
score
data-pipeline
No Agents.md, 2 skills
45
score
legacy-auth
No AI config, no tests
23
score
Make your repositories ready for AI
Git AI helps you raise the AI-Readiness of your repositories. Get actionable recommendations about your Agents.md, Skills, Tests and how to make your repository work better with background agents.
- AI-readiness score for every repository
- See improvements in the data as repos become more AI-friendly
- Measure the impact when you change the skills, MCPs, rules etc in a repo. Don't fly blind.