GRASP.
Grasp · Comprehension Index Scan Report

Who still understands anthropics/anthropic-sdk-python?

Computed from git metadata only; no source code contents were read.
touch-weighted · 3-year history window · last commit 59d ago · scores are comparable only against same-mode scans

Grasp Score
12
touch-weighted living knowledge across the repo
Active humans
10 / 61
contributors active in last 90 days vs. all-time
AI-attributed minimum
≥ 87.2%
hard evidence only. Just 82% of commits here carry any attribution signal, so the real share is higher
Inactive share
8.0%
work by authors with no commit in the window; silence, not proof of departure
Composition
72/13/10/5%
source / tests / docs / config share of scored work
Living, source only
12%
the headline recomputed over hand-written source alone
Score stability
stable
90-day-cliff variant reads 9%–14% across 60/90/180-day activity windows; read the width before the middle value

Dark-code ranking

Modules ranked by comprehension risk: the unknown share, amplified by ongoing change and thin ownership. The green meter is living knowledge.

ModuleRiskLiving knowledgeAI-attr. InactiveBusHeat/180d
src/anthropicCRIT 95
5%
94%3%1120
(root)CRIT 94
6%
92%6%1369
src/anthropic/typesCRIT 90
10%
91%7%11222
src/anthropic/resourcesCRIT 86
14%
82%4%1187
scriptsCRIT 80
6%
92%3%237
src/anthropic/_utilsHIGH 78
3%
97%6%112
examplesHIGH 78
22%
70%23%157
testsHIGH 77
17%
77%12%2376
.githubHIGH 77
8%
88%13%118
src/anthropic/libMED 58
37%
48%18%2196

Public-repo caveat: "departed" includes drive-by contributors, overstating attrition relative to a company repo with a real roster. Engagement is a proxy for comprehension. Treat it as a potential indicator rather than a diagnosis.