Skill audit report
Agent Memory Techniques audit report.
Agent memory for LLMs: 30 runnable Jupyter notebooks covering conversation buffers, vector stores, knowledge graphs, episodic and semantic memory, MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks, and production patterns.
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
INFO76
521 GitHub stars
Recent maintenance
PASS100
9d since push
License clarity
PASS86
Apache-2.0
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 NirDiamant/Agent_Memory_Techniques
Repository evidence
PASS86
https://github.com/NirDiamant/Agent_Memory_Techniques
Review status
PASS88
AI review data available
Checks
Install and adoption review
Install path
92
npx skills add NirDiamant/Agent_Memory_Techniques
Repository
88
https://github.com/NirDiamant/Agent_Memory_Techniques
License
86
Apache-2.0
Maintenance
100
9d 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
521 GitHub stars
Warnings
- Quality score needs review
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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