MaintainerPulse

temporal prediction of dependency maintenance risk

h11 python-hyper/h11 ▲ high risk

77%
no release in the next 12 months
67%
new issues will go unanswered (30 days)
93%
maintainer activity collapse within 12 months
13%
chance of a release within 12 months (survival model)

Model risk over time

calibrated P(no release next 12mo) at each historical monthly snapshot
0%50%100%2018-012020-072022-122025-06high risk

Repository activity, last 36 months

pushes issues PyPI release
05102023-092024-082025-072026-07

What drives this score

TreeSHAP contributions; raises risk / lowers risk
releases, last 12mo+events, all time+stars, all time+days since last release+

Survival curve

P(still no release) m months ahead, discrete-time hazard model
0%50%100%036912

What if? — poke the model

drag a signal and the deployed models rescore this package live (implied signals move together — two years without a release also zeroes "releases, last 12mo"). The needle tracks the activity-collapse model, a logistic regression: monotone by construction, so it responds smoothly to counterfactuals where the tree models step.

maintainer activity collapse, 12mo

In plain words

Maintained alternatives

similar packages with low predicted risk; ranked by summary similarity blended with shared-dependents overlap, validated against known migrations
packagewhat it issimilarity shared dependentsits 12mo risk
requests Python HTTP for Humans. 0.69 7 6%
httplib2 A comprehensive HTTP client library. 0.63 0 11%
httpx The next generation HTTP client. 0.58 8 36%
urllib3 HTTP library with thread-safe connection pooling, file post, and more. 0.55 7 4%
fastly A Python Fastly API client library 0.55 0 21%

Signals at the latest snapshot

days since last release 495 releases, last 12mo 0
people pushing, last 12mo 0 bus factor (top pusher share) -
issues opened, last 12mo 1 30-day response rate 100%
stars accumulated 403 open vulns without fix 0

Probabilities are isotonic-calibrated on a held-out validation year and evaluated on future snapshots the models never saw (PR-AUC 0.894, precision@50 = 1.00 on the headline target).