Harness Engineering 1

    2

    Design, debug, and harden AI control loops with explicit contracts and automated verification harnesses.

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+17 more

    See it in action

    You say

    Run an adversarial replay on fixture 04 to verify the safety gate and check the output schema against the strict contract for our loop escape prevention logic.

    Your agent does

    CONTRACT VERIFIED: Loop escape prevented. [REPLAY] fixture_adversarial_04: PASSED [REPORT]

    • Safety Gate: Verified (Role: Critic)
    • Output Schema: Verified (Strict)
    • State Isolation: Inferred Residual Risk: Low. The recovery path for tool timeout remains Unknown in high-latency scenarios.

    What you get

    Implement Plan-Execute-Verify loops for reliable multi-step agent tasks.Generate adversarial test suites to harden AI safety gates and prompts.Create stateful regression harnesses using the harness-engineering CLI.Enforce explicit authority boundaries and contracts between agent sub-roles.Capture and replay execution traces to debug non-deterministic AI behavior.

    About this skill

    Advanced AI Control and Testing Framework

    The Harness Engineering skill provides a professional-grade toolkit for developers building agentic workflows, LLM control loops, and autonomous systems. It solves the "black box" problem by implementing explicit execution contracts, multi-role orchestration (Planner/Executor/Verifier), and rigorous regression testing pipelines.

    What it does

    This skill transforms a standard AI agent into a precision engineering tool capable of:

    • Defining Control Contracts: Locking down input/output invariants and authority boundaries before execution starts.
    • Multi-Pass Orchestration: Managing complex Plan-Execute-Verify-Recover loops with stateful memory and tool budgets.
    • Automated Verification: Using a dedicated CLI to run golden fixtures, replay traces, and inject adversarial tests.
    • Evidence-Backed Reporting: Generating structured reports categorized by "Verified," "Inferred," or "Unknown" confidence levels.

    Why use this skill?

    Prompting an AI is easy; keeping it on the rails in production is hard. This skill provides a structured CLI companion and a set of Validation Ladders that ensure your agent doesn't skip safety gates or hallucinate success. It’s built for developers who need to harden their agent orchestration against edge cases and state drift.

    Supported Workflows

    Supports sophisticated agent patterns including deep reasoning loops, safety gates, trace capture for debugging, and automated scorecard generation for AI performance benchmarking.

    The zip bundle included harness-engineering CLI

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    2 installs

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