Skill audit report
Build, review, run, validate, migrate, or extend AgentFEM finite-element projects. Use for AgentFEM studies, meshes, materials, constraints, loads, solution steps, results, campaigns, scientific datasets, surrogate/PINN/neural-operator integration, verification, public API extensions, and agent or GUI integration.
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
FAIL30
20 GitHub stars
Stars/forks activity
FAIL32
20 stars, 3 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
2d since push
License clarity
PASS86
Apache-2.0
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
WARN46
credential or environment access, external package install surface
Install availability
PASS92
npx skills add haoming-luo/agentfem --skill agentfem
Install command safety
PASS92
standard package or runtime install path
Permission surface
FAIL34
secrets or environment access, filesystem or document access
Repository evidence
PASS86
https://github.com/haoming-luo/agentfem/tree/main/skills/agentfem
Review status
WARN46
AI review approval is missing
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add haoming-luo/agentfem --skill agentfem
Repository
88
https://github.com/haoming-luo/agentfem/tree/main/skills/agentfem
License
86
Apache-2.0
Maintenance
100
2d since push
AI review
55
Review approval is missing
README/SKILL.md completeness
86
Usable description available
Dependency risk
46
credential or environment access, external package install surface
Install command safety
92
standard package or runtime install path
Permission surface
34
secrets or environment access, filesystem or document access
Stars/forks activity
32
20 stars, 3 forks; issue activity unavailable in current metadata
Adoption
42
20 GitHub stars
Financial decision safety
58
Research-only use: do not treat output as financial advice or execute a position without human approval.
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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