Github Trends®
9789 findingsmedian surprise 0.00728window 7 days
UNIT / TREND-MONITOR · REV 2.6
[ 7 days window ]
SOURCE: own snapshots
FINDING #9185 · UNIT ID 1310013986
codejunkie99/graph-engineering
Graph engineering for AI agents: the 9-stage knowledge-graph pipeline (translated from SEU's graduate course) + task-graph orchestration patterns, as a Claude skill with teaching mode and paste-ready workflows
SURPRISE SCORE
0.00

Score Breakdown

SURPRISE0.00337
ENGAGEMENT0.29
FRESHNESS1.00
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
4% 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
2.57
ACCEL
-0.07
RETENTION
50.0%
PEAK 2026-09-08 · FORK-RETENTION 0.0% · 18 STARS / WINDOW

Author Audience

AUDIENCE
724
FOLLOWERS
245
OWNER ★
4,791

Engagement Signals

FORKS
66
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 18 / 18 (DIVERSITY 1.00)

Why This Is A Finding

codejunkie99/graph-engineering собрал 18 звёзд за окно, тогда как у автора всего 245 подписчиков — эффективная аудитория ≈ 724. Это даёт surprise-индекс 0.00337 (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 9789 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.000.00-0.00ABOVE 6%
VELOCITY2.573.43-0.86ABOVE 36%
RETENTION50.0%37.5%+12.5 PPABOVE 66%
FORKS66120-54ABOVE 37%
SURPRISE0.000.01-0.00ABOVE 33%