Github Trends®
9675 findingsmedian surprise 0.00741window 30 days
UNIT / TREND-MONITOR · REV 2.6
[ 30 days window ]
SOURCE: own snapshots
FINDING #7909 · UNIT ID 1291994110
TingxiYu/academic-figure-skill
A skill for academic research figure generation that autonomously handles the full workflow — data understanding, chart type recommendation, and format-compliant generation.
[ PYTHON ][ GITHUB ↗ ]
SURPRISE SCORE
0.00

Score Breakdown

SURPRISE0.029
ENGAGEMENT0.30
FRESHNESS1.44
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
21% OF STARS IN ARCHIVE
[BOT] SUSPECTED STAR BOT — SCORE PENALIZED. SIGNATURES:
S2 · NO EXTERNAL ISSUE/PR AUTHORS DESPITE 100+ STARS
S7 · THIS REPO IS ~ALL OF THE OWNER'S STARS
S10 · DEAD CODE, ZERO CONTRIBUTORS, YET STARS KEEP DRIPPING

Growth Telemetry

VELOCITY /D
3.63
ACCEL
-0.08
RETENTION
35.6%
PEAK 2026-09-23 · FORK-RETENTION 57.1% · 109 STARS / WINDOW

Author Audience

AUDIENCE
85
FOLLOWERS
34
OWNER ★
514

Engagement Signals

FORKS
27
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 109 / 109 (DIVERSITY 1.00)

Why This Is A Finding

TingxiYu/academic-figure-skill собрал 109 звёзд за окно, тогда как у автора всего 34 подписчиков — эффективная аудитория ≈ 85. Это даёт surprise-индекс 0.029 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 57.1% 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 18%
VELOCITY3.633.80-0.17ABOVE 48%
RETENTION35.6%20.3%+15.4 PPABOVE 81%
FORKS27138-111ABOVE 18%
SURPRISE0.030.01+0.02ABOVE 80%