MaintainerPulse

temporal prediction of dependency maintenance risk

mypy-extensions python/mypy_extensions ▲ high risk

82%
no release in the next 12 months
83%
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
05102023-092024-112025-072026-03

What drives this score

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

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
typeguard Run-time type checker for Python 0.68 0 36%
typing-extensions Backported and Experimental Type Hints for Python 3.9+ 0.64 10 16%
ty An extremely fast Python type checker, written in Rust. 0.64 0 0%
mypy Optional static typing for Python 0.61 6 2%
pyflakes passive checker of Python programs 0.58 1 36%

Signals at the latest snapshot

days since last release 497 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 168 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).