verification

Agent Verifier

Purpose

Run comprehensive verification on AI agent code. This orchestrator invokes focused verification skills and consolidates results into a unified report. All analysis happens locally—code never leaves your machine.

When to Use

Trigger this skill when the user asks to:

• "verify agent" (primary invocation)
• "verify my agent"
• "audit agent"
• "full verification"
• "verify my code" (when agent patterns are detected)
• "check compliance"

Available Verification Modes

Command	Skill	What it checks
"verify agent"	This skill	Full suite (all below)
"verify agent security"	verify-security	Secrets, dependencies, input validation
"verify agent patterns"	verify-patterns	Loops, retries, tools, context size
"verify agent quality"	verify-quality	Naming, organization, documentation
"verify agent language"	verify-language	Type hints, idioms, language best practices

Process

Step 1: Context Discovery

Scan the project to identify:

1. Primary language:

   • Check for pyproject.toml, package.json, go.mod
   • Look at file extensions in src/ or project root

2. Agent framework (if any):

   • langgraph in imports → LangGraph
   • crewai in imports → CrewAI
   • autogen in imports → AutoGen
   • langchain in imports → LangChain
   • Direct SDK usage → Custom agent

3. Kahuna integration:

   • Check if .kahuna/ directory exists
   • If yes, read .kahuna/context-guide.md for organizational rules

Record the detected context for reporting.

Step 2: Run Security Checks

Load the verify-security skill and execute its process.

This checks for:

• Hardcoded secrets and API keys
• Dependency version pinning
• Input validation patterns
• Error message exposure
• Secure defaults

Record all findings.

Step 3: Run Pattern Checks

Load the verify-patterns skill and execute its process.

This checks for:

• Loop safety (termination conditions)
• Retry limit enforcement
• Tool registry consistency
• Context size awareness
• LangGraph cycle analysis (if applicable)