retentioneering/retentioneering-tools — GitHub trend

FINDING #3369 · UNIT ID 194870085

retentioneering/retentioneering-tools

Python toolkit, MCP server, and agent skills for reproducible, auditable clickstream and event log analytics. Helps AI agents, data scientists and analysts build, validate, and cross-check product analytics, quantitative UX, customer journeys, graph-based user flows, behavioral segmentation, A/B tests, process mining models, Markov chain simulation

[ PYTHON ][ ORG ][ GITHUB ↗ ]

SURPRISE SCORE

0.00

Score Breakdown

SURPRISE0.00777

ENGAGEMENT0.20

FRESHNESS1.00

SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE

SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)

2% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D

2.29

ACCEL

+1.00

RETENTION

16.7%

PEAK 2026-07-14 · FORK-RETENTION 0.0% · 16 STARS / WINDOW

Author Audience

AUDIENCE

254

FOLLOWERS

OWNER ★

920

Engagement Signals

FORKS

135

ISSUE AUTH

PR AUTH

UNIQUE STARGAZERS 16 / 16 (DIVERSITY 1.00)

Why This Is A Finding

retentioneering/retentioneering-tools собрал 16 звёзд за окно, тогда как у автора всего 35 подписчиков — эффективная аудитория ≈ 254. Это даёт surprise-индекс 0.00777 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 0.0% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация положительная — рост ещё не выдохся.

Related Findings

RANKS ABOVE 51% OF 6892 FINDINGS

#01MDX-Tom/gpt-5.6-instruct[ PYTHON ]0.86 #02Git-creat7/grokRegister-cpa[ PYTHON ]0.67 #04William-Lu-stack/LuxyAI[ PYTHON ]0.52 #05AlephAITech/WorkBuddyGuide[ PYTHON ]0.51 #06Cjbuilds/Codex-Orchestration[ PYTHON ]0.51

METRICS IN CONTEXT

MEDIAN ACROSS ALL 6892 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW

METRICVALUEMEDIANΔ MEDPERCENTILE

SCORE0.000.00+0.00ABOVE 51%

VELOCITY2.294.14-1.86ABOVE 26%

RETENTION16.7%40.6%-24.0 PPABOVE 21%

FORKS13589+46ABOVE 61%

SURPRISE0.010.01-0.00ABOVE 41%