Skill audit report
All Agentic Architectures audit report.
35 production-grade agentic AI architectures (Reflexion, LATS, GraphRAG, MemGPT, Voyager, BrowserAgent, ...) — a Python library and runnable textbook with multi-provider LLM support and a 17-task benchmark leaderboard.
OpenAgentSkill Trust Score
Stars, maintenance, license, docs, dependency risk, and installability.
The Trust Score is OpenAgentSkill's adoption layer. It is designed to help an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
PASS86
3.5K GitHub stars
Recent maintenance
PASS100
8d since push
License clarity
PASS86
MIT
README/SKILL.md completeness
PASS90
Metadata includes enough usage and workflow context
Dependency risk
PASS90
no major dependency risk hints in public metadata
Install availability
PASS92
npx skills add FareedKhan-dev/all-agentic-architectures
Repository evidence
PASS86
https://github.com/FareedKhan-dev/all-agentic-architectures
Review status
PASS88
AI review data available
Checks
Install and adoption review
Install path
92
npx skills add FareedKhan-dev/all-agentic-architectures
Repository
88
https://github.com/FareedKhan-dev/all-agentic-architectures
License
86
MIT
Maintenance
100
8d since push
AI review
88
Approved with no listed issues
README/SKILL.md completeness
90
Usable description available
Dependency risk
90
no major dependency risk hints in public metadata
Adoption
88
3.5K GitHub stars
Warnings
No major warnings detected from available metadata.
Method
This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.
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