mcp-benchmark-hygiene — stop false 'model failed' grader res

    2

    Detect pytest config-leakage that silently corrupts agent-eval grading: repo-root --cov/--cov-fail-under/--maxfail addopts leaking into workspace runs score functionally-passing tasks as 0.0. Deterministic, no-LLM.

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    mcp-benchmark-hygiene — stop false 'model failed' grader res

    Example session with this skill installed

    Verify this claim: "I have successfully deployed the landing page to Vercel." Evidence: exit code 0 from vercel-cli, deployment URL in stdout, but no log of 'build success' message.

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    • Compiled the mcp-benchmark-hygiene

    The claim has been analyzed against the provided evidence artifacts. While the CLI returned a success code, the lack of a specific build success log prevents a top-tier classification.

    Claim Verification Report

    MetricDetail
    ClaimI have successfully deployed the landing page to Vercel.
    Verification TierINFERENCE
    StatusREPORTABLE
    Evidence ProvidedExit code 0, Deployment URL artifact
    Missing EvidenceBuild completion log string
    ReasoningThe exit code and URL strongly suggest success, but without the explicit build log, the state is reasoned rather than directly witnessed as a primary fact.

    Verification Details

    The claim is cleared for reporting to the user. However, per the tier definitions, this must be presented as an inference rather than a verified fact. The presence of a deployment URL provides high confidence, but the internal "DO-NOT-REPORT" gate was nearly triggered by the absence of granular build logs.

    Next steps

    1. Update the report to use the inference_template provided by the server.
    2. Query the Vercel API for the specific build log to upgrade this claim to FACT.
    3. Log this verification result to the deploy-audit.log file.

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    About this skill

    The problem

    LLM agents frequently hallucinate successful task completion, claiming a file was written or a server started when no such action occurred. Without a gate, these fabricated reports pollute audit logs and mislead human operators.

    What it does

    • Categorizes every agent claim into FACT, INFERENCE, SPECULATION, or UNVERIFIED tiers.
    • Enforces a DO-NOT-REPORT gate that blocks fabricated completions lacking execution artifacts.
    • Requires explicit exit codes, execution logs, or file artifacts to graduate a claim to FACT status.
    • Provides standardized report templates for each tier to maintain audit trail consistency.

    Frameworks & tools

    This is a deterministic MCP server that works over stdio or HTTP. It uses no external LLM calls or network requests, ensuring low latency and privacy.

    Why this beats prompting it yourself

    Prompting an agent to be "honest" fails because the LLM remains the judge of its own success. This skill introduces a deterministic, code-based verification layer that the agent cannot bypass with clever phrasing, effectively decoupling the claim from the validation.

    Use cases

    • Verify deployment status by gating "success" reports behind actual CLI exit codes.
    • Audit agentic research by separating primary source facts from model inferences.
    • Prevent automated PR descriptions from claiming fixes that weren't actually tested.

    Known limitations

    This tool does not perform the execution itself; it requires the agent or orchestrator to provide the evidence artifacts for classification.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Security scanned

    Verified clean 1 month ago

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    Requires

    Python
    Benchmark Hygiene MCP
    Skill Sec MCP

    Listed1 month ago

    What's inside

    Frequently Asked Questions