Registry › Compare › Avoid Ai Writing vs Notebooklm Py

Avoid Ai Writing vs Notebooklm Py

Both are Grade A and 1.8 points apart, so choose on what you weigh most. Avoid Ai Writing leads on transparency and maintenance; Notebooklm Py leads on supply-chain integrity and adoption.

A Avoid Ai Writing 88.1

Skill that audits and rewrites content to remove AI writing patterns. Use it with your favorite agents including Claude Code, OpenClaw, Codex, and Hermes.

conorbronsdon/avoid-ai-writing · #6 overall · #6 Agent Skills · coverage B (3/5)

Choose Avoid Ai Writing if transparency and maintenance matter most.

+1.0Maintenance
+0.6Transparency: OSSF Scorecard 7.1 against 6.3

A Notebooklm Py 89.9

Unofficial Python API and agentic skill for Google Gemini Notebook. Full programmatic access to Gemini Notebook's features—including capabilities the web UI doesn't expose—via Python, CLI, and AI agents like Claude Code, Codex, and OpenClaw.

teng-lin/notebooklm-py · #4 overall · #4 Agent Skills · coverage B (3/5)

Choose Notebooklm Py if supply-chain integrity and adoption matter most.

+3.3Adoption: 38.2k weekly downloads against 1.4k
+0.8Safety / Integrity: 69% of recent commits signed, against 33%

Where they differ

18.0

Safety / IntegrityNotebooklm Py +0.8

18.8

14.5

TransparencyAvoid Ai Writing +0.6

13.9

19.9

MaintenanceAvoid Ai Writing +1.0

18.9

13.1

AdoptionNotebooklm Py +3.3

16.4

+4.6

Runtime calibrationAvoid Ai Writing +0.7

+3.9

1 dimension identical: Identity 18.0 · Full evidence table

An independent, evidence-based trust comparison of Avoid Ai Writing and Notebooklm Py, two Agent Skills projects in the HVTracker registry. Scores come from public, checkable signals — supply-chain provenance, OSSF Scorecard, maintenance, and adoption — not popularity.

Full evidence

Signal	Avoid Ai Writingconorbronsdon/avoid-ai-writing	Notebooklm Pyteng-lin/notebooklm-py
HVTrust score	88.1	89.9
Evidence grade	A	A
Coverage grade	B	B
Overall rank	#6	#4
Rank in Agent Skills	#6	#4
GitHub stars	5.2k	19.8k
Last updated	1d ago	today
Build provenance	Yes	Yes
OSSF Scorecard	7.1 / 10	6.3 / 10
License	MIT	MIT
Downloads	1k/wk	38k/wk
Trust dimensions (points earned)
Safety / integrity / 25	18.0	18.8
Identity & provenance / 18	18.0	18.0
Transparency / 17	14.5	13.9
Maintenance / 20	19.9	18.9
Adoption / 20	13.1	16.4
Runtime capability surface (full matrix)
MCP server	Implemented	Implemented
External providers	1 — Anthropic	—
Requires API keys	No	No
Plugin surface	plugins	—
Provenance drift	Match	Match

Open in the live compare tool → Avoid Ai Writing profile Notebooklm Py profile More Agent Skills →

How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption, scaled by an evidence-confidence factor. Grade bands: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Signals refresh daily. Full methodology v4.4 →