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    ai-trace-runner

    ai-trace-runner

    by Roy Yuen

    Transform ambiguous AI tasks into auditable execution traces with verified evidence and AI-smell detection.

    Updated Apr 2026
    Security scanned
    One-time purchase

    $5

    One-time purchase · Own forever

    ⚡ Also available via Agensi MCP — your AI agent can load this skill on demand via MCP. Learn more →

    Included in download

    • Generate auditable logs for compliance-sensitive code changes.
    • Debug failed agent runs by comparing planned steps vs. actual execution logs.
    • terminal, file_read, file_write automation included
    • Includes example output and usage patterns
    • Instant install

    See it in action

    [Verification Report]
- Criterion: 'Update API routes' -> VERIFIED (See diff in Evidence Bundle)
- Criterion: 'Test coverage > 80%' -> BLOCKED (Missing jest config)

[AI-Smell Report]
Score: 4/5 (Repetitive hedging detected)
Cleanup: Removed "As an AI..." and "I have successfully..." phrases.

    Screenshots

    About This Skill

    What it does

    AI Trace Runner is a comprehensive execution wrapper for AI agents that turns ambiguous tasks into auditable, verifiable run records. It replaces basic prompting with a structured pipeline: creating a request contract, generating an execution plan, logging every material action, and assembling an evidence bundle. It ensures that every claim made by the AI is backed by a specific file path, command output, or observation.

    Why use this skill

    Standard AI agents often suffer from 'hallucinated confidence'—claiming a task is done when it actually failed or was bypassed. This skill solves the lack of transparency in agentic workflows by forcing explicit verification. It includes built-in "AI-smell" detection to strip away robotic fluff and project.yaml integration for rigorous dependency management. It is ideal for developers who need to debug complex agent behaviors or provide ironclad proof of work for code changes and system audits.

    Supported tools

    • Standard CLI and shell environments
    • Project management via project.yaml execution topologies
    • Any file-system or API-based tool within the agent's scope
    • Integrated human-in-the-loop gates for destructive actions

    📖 Learn more: Best DevOps & Deployment Skills for Claude Code →

    Use Cases

    • Generate auditable logs for compliance-sensitive code changes.
    • Debug failed agent runs by comparing planned steps vs. actual execution logs.
    • Verify AI claims against concrete evidence like diffs and command outputs.
    • Standardize agent reporting using project.yaml execution topologies.
    • Clean up 'robotic' AI summaries using built-in smell detection.

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    Security Scanned

    Passed automated security review

    Permissions

    Terminal / Shell
    Read Files
    Write Files

    Allowed Hosts

    registry.npmjs.org
    github.com

    File Scopes

    examples/**
    src/**

    Tags

    debugging
    observability
    devops
    agent-verification
    audit-log

    Creator

    Roy Yuen

    Frequently Asked Questions

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