Github Trends®
9588 findingsmedian surprise 0.00637window 30 days
UNIT / TREND-MONITOR · REV 2.6
[ 30 days window ]
SOURCE: own snapshots
FINDING #5675 · 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

SURPRISE0.00324
ENGAGEMENT0.32
FRESHNESS1.07
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
49% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D
33.50
ACCEL
+5.91
RETENTION
0.0%
PEAK 2026-08-23 · FORK-RETENTION 0.0% · 1,005 STARS / WINDOW

Author Audience

AUDIENCE
10,303
FOLLOWERS
1,773
OWNER ★
85,300

Engagement Signals

FORKS
114
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 1,005 / 1,005 (DIVERSITY 1.00)

Why This Is A Finding

Leonxlnx/unlazy собрал 1,005 звёзд за окно, тогда как у автора всего 1,773 подписчиков — эффективная аудитория ≈ 10,303. Это даёт surprise-индекс 0.00324 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 0.0% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация положительная — рост ещё не выдохся.

METRICS IN CONTEXT

MEDIAN ACROSS ALL 9588 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.000.00-0.00ABOVE 41%
VELOCITY33.503.57+29.93ABOVE 94%
RETENTION0.0%25.0%-25.0 PPABOVE 0%
FORKS114146-32ABOVE 44%
SURPRISE0.000.01-0.00ABOVE 34%