Github Trends®
751 findingsmedian surprise 0.028window 90 days
UNIT / TREND-MONITOR · REV 2.6
[ 90 days window ]
SOURCE: gharchive
FINDING #78 · UNIT ID 1129786550
teng-lin/notebooklm-py
Unofficial Python API and agentic skill for Google NotebookLM. Full programmatic access to NotebookLM's features—including capabilities the web UI doesn't expose—via Python, CLI, and AI agents like Claude Code, Codex, and OpenClaw.
[ PYTHON ][ GITHUB ↗ ]
SURPRISE SCORE
0.00

Score Breakdown

SURPRISE0.0316
ENGAGEMENT1.16
FRESHNESS1.35
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
40% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D
79.66
ACCEL
-0.89
RETENTION
17.0%
PEAK 2026-05-19 · FORK-RETENTION 0.0% · 7,169 STARS / WINDOW

Author Audience

AUDIENCE
2,478
FOLLOWERS
666
OWNER ★
18,122

Engagement Signals

FORKS
2,401
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 7,169 / 7,169 (DIVERSITY 1.00)

Why This Is A Finding

teng-lin/notebooklm-py собрал 7,169 звёзд за окно, тогда как у автора всего 666 подписчиков — эффективная аудитория ≈ 2,478. Это даёт surprise-индекс 0.0316 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 0.0% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация отрицательная — внимание остывает после пика.

METRICS IN CONTEXT

MEDIAN ACROSS ALL 751 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.050.00+0.05ABOVE 90%
VELOCITY79.666.61+73.04ABOVE 92%
RETENTION17.0%22.2%-5.2 PPABOVE 36%
FORKS2,40197+2,304ABOVE 95%
SURPRISE0.030.03+0.00ABOVE 56%