Github Trends®
9651 findingsmedian surprise 0.00326window 7 days
UNIT / TREND-MONITOR · REV 2.6
[ 7 days window ]
SOURCE: own snapshots
FINDING #5181 · UNIT ID 1129786550
teng-lin/notebooklm-py
Unofficial Python API and agentic skill for Google Gemini Notebook. Full programmatic access to Gemini Notebook'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

SURPRISE0.00128
ENGAGEMENT0.53
FRESHNESS1.00
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
0% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D
4.14
ACCEL
-1.39
RETENTION
11.8%
PEAK 2026-10-11 · FORK-RETENTION 66.7% · 29 STARS / WINDOW

Author Audience

AUDIENCE
3,194
FOLLOWERS
1,113
OWNER ★
20,808

Engagement Signals

FORKS
2,635
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 29 / 29 (DIVERSITY 1.00)

Why This Is A Finding

teng-lin/notebooklm-py собрал 29 звёзд за окно, тогда как у автора всего 1,113 подписчиков — эффективная аудитория ≈ 3,194. Это даёт surprise-индекс 0.00128 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 66.7% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация отрицательная — внимание остывает после пика.

METRICS IN CONTEXT

MEDIAN ACROSS ALL 9651 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.000.00-0.00ABOVE 46%
VELOCITY4.143.14+1.00ABOVE 60%
RETENTION11.8%16.7%-4.9 PPABOVE 39%
FORKS2,635277+2,358ABOVE 87%
SURPRISE0.000.00-0.00ABOVE 34%