AI Trace Runner is a skill that wraps AI agent execution in a structured pipeline designed to eliminate unverifiable task completion claims. Instead of relying on bare prompting, it creates a request contract at the start of each run, generates an explicit execution plan, logs every material action taken, and assembles a final evidence bundle where every claim is tied to a specific file path, command output, or observation.
A core problem it addresses is hallucinated confidence — agents reporting success when a task actually failed or was silently bypassed. AI Trace Runner counters this by forcing explicit verification at each step rather than accepting an agent’s self-reported outcome at face value.
The skill includes built-in AI-smell detection, which strips robotic filler language from outputs so that results remain precise and audit-ready. It also integrates with project.yaml for dependency and execution topology management, making it suitable for multi-step workflows with defined ordering constraints.
Human-in-the-loop gates are built in for destructive actions, ensuring an agent cannot proceed through high-risk operations without explicit approval. The skill works with standard CLI and shell environments and with any file-system or API-based tool within the agent’s scope.
This skill is aimed at developers who need to debug complex agent behaviors, produce evidence of code changes, or demonstrate compliance through a verifiable audit trail.