Github Trends®
9675 findingsmedian surprise 0.00741window 30 days
UNIT / TREND-MONITOR · REV 2.6
[ 30 days window ]
SOURCE: own snapshots
FINDING #9088 · UNIT ID 1307762938
phoenix-zhou/industrial-general-rag
The project integrates the RAG and FAQ dual mechanisms, introduces intent recognition, dynamic adaptive retrieval, and the RAGAS automated evaluation system, achieving a transformation from empirical optimization to data-driven optimization.
[ PYTHON ][ GITHUB ↗ ]
SURPRISE SCORE
0.00

Score Breakdown

SURPRISE0.00515
ENGAGEMENT0.22
FRESHNESS1.15
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
17% OF STARS IN ARCHIVE
[BOT] SUSPECTED STAR BOT — SCORE PENALIZED. SIGNATURES:
S2 · NO EXTERNAL ISSUE/PR AUTHORS DESPITE 100+ STARS
S10 · DEAD CODE, ZERO CONTRIBUTORS, YET STARS KEEP DRIPPING

Growth Telemetry

VELOCITY /D
3.07
ACCEL
-0.11
RETENTION
39.3%
PEAK 2026-09-23 · FORK-RETENTION 0.0% · 92 STARS / WINDOW

Author Audience

AUDIENCE
556
FOLLOWERS
177
OWNER ★
3,786

Engagement Signals

FORKS
26
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 92 / 92 (DIVERSITY 1.00)

Why This Is A Finding

phoenix-zhou/industrial-general-rag собрал 92 звёзд за окно, тогда как у автора всего 177 подписчиков — эффективная аудитория ≈ 556. Это даёт surprise-индекс 0.00515 (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 9675 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.000.00-0.00ABOVE 6%
VELOCITY3.073.80-0.73ABOVE 41%
RETENTION39.3%20.3%+19.0 PPABOVE 86%
FORKS26138-112ABOVE 18%
SURPRISE0.010.01-0.00ABOVE 42%