Github Trends®
9519 findingsmedian surprise 0.00484window 3 days
UNIT / TREND-MONITOR · REV 2.6
[ 3 days window ]
SOURCE: own snapshots
FINDING #6445 · UNIT ID 1329272295
Leonxlnx/unlazy
Anti-laziness skill for AI agents. Core: the Depth Tree method, which splits a task N layers deep and gives every leaf the full time budget of the whole task, so effort multiplies with depth. Grounded in 2025-2026 research on model laziness, underthinking and premature completion.
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SURPRISE SCORE
0.00

Score Breakdown

SURPRISE0.00107
ENGAGEMENT0.32
FRESHNESS1.00
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
1% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D
13.33
ACCEL
-1.50
RETENTION
67.6%
PEAK 2026-10-07 · FORK-RETENTION 0.0% · 40 STARS / WINDOW

Author Audience

AUDIENCE
12,416
FOLLOWERS
2,214
OWNER ★
102,019

Engagement Signals

FORKS
288
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 40 / 40 (DIVERSITY 1.00)

Why This Is A Finding

Leonxlnx/unlazy собрал 40 звёзд за окно, тогда как у автора всего 2,214 подписчиков — эффективная аудитория ≈ 12,416. Это даёт surprise-индекс 0.00107 (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 9519 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.000.00-0.00ABOVE 32%
VELOCITY13.335.67+7.67ABOVE 77%
RETENTION67.6%40.0%+27.6 PPABOVE 74%
FORKS288317-29ABOVE 48%
SURPRISE0.000.00-0.00ABOVE 25%