Skill audit report

Quivr audit report.

Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.

REVIEWED · REVIEWSafe to tryGenerated Jun 16, 2026Heuristic metadata audit
85
Audit
83
Trust
95
Quality
85
Security
62
Maintain
92
Install

OpenAgentSkill Trust Score

83
Strong shortlist

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

PASS

100

39K GitHub stars

Recent maintenance

INFO

62

11mo since push

License clarity

WARN

42

Unknown

README/SKILL.md completeness

PASS

90

Metadata includes enough usage and workflow context

Dependency risk

INFO

64

external package install surface, network or browser surface

Install availability

PASS

92

npx skills add QuivrHQ/quivr

Repository evidence

PASS

86

https://github.com/QuivrHQ/quivr

Review status

PASS

88

AI review data available

Checks

Install and adoption review

5 passed · 5 review

Install path

92

PASS

npx skills add QuivrHQ/quivr

Repository

88

PASS

https://github.com/QuivrHQ/quivr

License

45

CHECK

Unknown

Maintenance

62

CHECK

11mo since push

AI review

88

PASS

Approved with no listed issues

README/SKILL.md completeness

90

PASS

Usable description available

Dependency risk

64

CHECK

external package install surface, network or browser surface

Adoption

88

PASS

39K GitHub stars

Warnings

  • License is unclear
  • License clarity: Unknown

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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