MaintainerPulse

temporal prediction of dependency maintenance risk

pysocks Anorov/PySocks ▲ high risk

92%
no release in the next 12 months
75%
new issues will go unanswered (30 days)
100%
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
00.512023-092024-062025-052026-02

What drives this score

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

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
websocket-client WebSocket client for Python with low level API options 0.57 1 9%
authlib The ultimate Python library in building OAuth and OpenID Connect servers and clients. 0.55 0 4%
fastly A Python Fastly API client library 0.55 0 21%
absl-py Abseil Python Common Libraries, see https://github.com/abseil/abseil-py. 0.54 0 36%
blockkit A fast way to build Block Kit interfaces in Python 0.53 0 17%

Signals at the latest snapshot

days since last release 2538 releases, last 12mo 0
people pushing, last 12mo 0 bus factor (top pusher share) -
issues opened, last 12mo 1 30-day response rate 0%
stars accumulated 1046 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).