MaintainerPulse

temporal prediction of dependency maintenance risk

llm-dialog-manager xihajun/llm_dialog_manager ▲ high risk

100%
no release in the next 12 months
54%
new issues will go unanswered (30 days)
100%
maintainer activity collapse within 12 months
80%
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%2025-06high risk

Repository activity, last 36 months

pushes issues PyPI release
02.5k5k2024-112025-012025-022025-05

What drives this score

TreeSHAP contributions; raises risk / lowers risk
events, all time+releases, all time+package age (months)+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
jupyterlab-chat A chat extension based on shared documents 0.59 0 2%
kombu Messaging library for Python. 0.56 0 6%
slangtorch A package for calling Slang modules from Python and PyTorch. 0.53 0 2%
tm1py A python module for TM1. 0.50 0 9%
dearpygui DearPyGui: A simple Python GUI Toolkit 0.49 0 13%

Signals at the latest snapshot

days since last release 472 releases, last 12mo 0
people pushing, last 12mo 0 bus factor (top pusher share) -
issues opened, last 12mo 0 30-day response rate no issues
stars accumulated 0 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).