agent-native-audit

Auditing AI agent architecture involves evaluating alignment with core principles like actions, context, and discovery. This skill orchestrates parallel sub-agents to score each principle across your codebase, producing a comprehensive scorecard and actionable recommendations.

Agent-native architecture is a system design optimized for end-to-end AI agents, covering tools, workspace, and prompt-native features. Scoring it surfaces alignment gaps against governance and safety principles, ensuring reliability before deployment.

Generating a code review scorecard for an AI agent codebase requires running parallel sub-agents to evaluate eight architectural areas. The process yields explicit per-principle scores and a final summary with concrete recommendations for improvement.

Yes, validating AI agent governance and safety is achieved by running a structured audit workflow. It assesses architectural features like CRUD operations and UI discovery, producing a metric-driven report to identify reliability gaps.

Yes, the agent architecture audit operates without external dependencies. It leverages internal sub-agents with clearly defined prompts to execute the structured evaluation workflow directly against the target repository.