Skill audit report
Using SurrealDB with the Python SDK, covering both client/server mode (WebSocket) and embedded mode (in-memory or file-based). Use when connecting to SurrealDB from Python, using the surrealdb Python package, running SurrealDB embedded without a server, or performing CRUD operations from Python code. Triggers: surrealdb Python, Surreal(), AsyncSurreal(), Python SDK, embedded SurrealDB, mem://, file://.
OpenAgentSkill Trust Score
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
WARN48
25 GitHub stars
Stars/forks activity
WARN43
25 stars, 3 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
18d since push
License clarity
PASS86
MIT
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
WARN46
command execution surface, credential or environment access
Install availability
PASS92
npx skills add surrealdb/agent-skills --skill surrealdb-python
Install command safety
PASS92
standard package or runtime install path
Permission surface
FAIL24
secrets or environment access, shell or command execution
Repository evidence
PASS86
https://github.com/surrealdb/agent-skills/tree/main/skills/surrealdb-python
Review status
WARN46
AI review approval is missing
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add surrealdb/agent-skills --skill surrealdb-python
Repository
88
https://github.com/surrealdb/agent-skills/tree/main/skills/surrealdb-python
License
86
MIT
Maintenance
100
18d since push
AI review
55
Review approval is missing
README/SKILL.md completeness
86
Usable description available
Dependency risk
46
command execution surface, credential or environment access
Install command safety
92
standard package or runtime install path
Permission surface
24
secrets or environment access, shell or command execution
Stars/forks activity
43
25 stars, 3 forks; issue activity unavailable in current metadata
Adoption
42
25 GitHub stars
Warnings
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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