u/tva_variant

repovital.com

Kept adopting dependencies that died later, so I built a tool to try to catch it early It's an ML model that scores GitHub repo health 0–100, using commit velocity, contributor concentration, PR merge rate, that kind of stuff. SHAP values explain each score so it's not a black box.

Best sanity check so far: facebook/create-react-app scored 58 (Watch). The model has no idea Meta officially deprecated it, it just saw 437 days since last commit, declining activity, thinning contributors, and flagged it.

Here's how some well-known repos score:

supabase/supabase                  94  Healthy
vllm-project/vllm                  96  Healthy
babel/babel                        91  Healthy
gruntjs/grunt                      90  Healthy  # still alive?
facebook/create-react-app          58  Watch
gulpjs/gulp                        42  At Risk
bower/bower                         8  Critical
trufflesuite/truffle               Archived

A few edge cases it handles well:

• Archived repos: determine Archived status, never even touches the ML pipeline
• Repos under 6 months old: Unscored with an explanation of not enough signal history
• Private/missing repos: simple 404

You can score any public repo, takes about 3 seconds. There's also a README badge, every repo that adds one helps surface the tool to more devs.

Would genuinely love feedback on scores that feel wrong, that's the best signal for improving the model. Happy to dig into what the model saw for any specific repo.

repovital.com

Kept adopting dependencies that died later, so I built a tool to try to catch it early It's an ML model that scores GitHub repo health 0–100, using commit velocity, contributor concentration, PR merge rate, that kind of stuff. SHAP values explain each score so it's not a black box.

Best sanity check so far: facebook/create-react-app scored 58 (Watch). The model has no idea Meta officially deprecated it, it just saw 437 days since last commit, declining activity, thinning contributors, and flagged it.

Here's how some well-known repos score:

supabase/supabase                  94  Healthy
vllm-project/vllm                  96  Healthy
babel/babel                        91  Healthy
gruntjs/grunt                      90  Healthy  # still alive?
facebook/create-react-app          58  Watch
gulpjs/gulp                        42  At Risk
bower/bower                         8  Critical
trufflesuite/truffle               Archived

A few edge cases it handles well:

• Archived repos: determine Archived status, never even touches the ML pipeline
• Repos under 6 months old: Unscored with an explanation of not enough signal history
• Private/missing repos: simple 404

You can score any public repo, takes about 3 seconds. There's also a README badge, every repo that adds one helps surface the tool to more devs.

Would genuinely love feedback on scores that feel wrong, that's the best signal for improving the model. Happy to dig into what the model saw for any specific repo.