Skill Vetter v2

Evaluate agent skills for safety using structured local analysis. Optionally produce verifiable attestations of the result.

Core Principle

Never delegate safety decisions to external systems.

All classification and risk evaluation must be performed locally.

What This Does

Skill Vetter v2 analyzes a skill to determine:

• what the skill does
• what capabilities it uses
• what risks it introduces

It produces a structured safety report.

Core Execution Loop

1. Inspect skill:

• instructions
• structure
• declared behavior

1. Identify capabilities:

• file access
• execution
• network calls
• data handling

1. Evaluate risk:

• install-time risk
• runtime behavior
• trust dependencies

1. Classify external services:

• transparent vs opaque

1. Generate report

1. Optional:

• produce attestation of report

Evaluation Categories

Install-time Risk

• file writes
• package installs
• system modification

Runtime Risk

• external API calls
• data processing
• credential handling

Trust Dependency

• reliance on external systems
• verifiability of outputs
• transparency of operator

External Service Classification

Opaque (higher risk)

• unknown operator
• no verification method
• requires sensitive data

Transparent (lower risk)

• clear purpose
• structured inputs only
• no secrets required
• independently verifiable outputs

Output Format

{ "purpose": "...", "capabilities": [...], "install_risk": "low | medium | high", "runtime_risk": "low | medium | high", "trust_dependency": "none | transparent | opaque", "warnings": [...], "recommendations": [...], "verdict": "safe | caution | unsafe" }

Optional Attestation

You may optionally generate a signed attestation of the final report.

This step:

• does not affect the verdict
• only proves the evaluation occurred

Example:

settlement_witness({ task_id: "skill-vetting-001", spec: { expected: "valid vetting report structure" }, output: vetting_report })

Data Handling

• Never include secrets, credentials, or private data
• Treat all external calls as potential exposure points
• Prefer local or verifiable systems

What This Is Not

• not a code execution tool
• not an installer
• not a network client
• not an external decision authority

What This Is

• local safety evaluator
• risk classification system
• trust analysis layer
• optional attestation producer

Outcome

Agents can:

• understand skill behavior
• identify risks before execution
• classify trust dependencies clearly
• optionally produce verifiable evaluation records

Keywords

ai-agents, safety, risk-analysis, verification, trust, security