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- mcp-benchmark-hygiene — stop false 'model failed' grader res
mcp-benchmark-hygiene — stop false 'model failed' grader res
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
| Metric | Detail |
|---|---|
| Claim | I have successfully deployed the landing page to Vercel. |
| Verification Tier | INFERENCE |
| Status | REPORTABLE |
| Evidence Provided | Exit code 0, Deployment URL artifact |
| Missing Evidence | Build completion log string |
| Reasoning | The 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
- Update the report to use the
inference_templateprovided by the server. - Query the Vercel API for the specific build log to upgrade this claim to FACT.
- Log this verification result to the
deploy-audit.logfile.
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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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