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    LLM Eval Framework Builder

    1

    Give it your task, your current failure modes, and your quality bar; it returns an eval suite you can run in CI.

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    LLM Eval Framework Builder

    LLM Eval Framework Builder

    Example session with this skill installed

    We have a GPT-4o-based support-ticket summarizer that writes a 3-bullet summary plus a priority label for about 2,000 tickets a week. Before we change the prompt, I want an eval framework we can run in CI.

    • Read your context and instructions
    • Compiled the llm eval framework

    **Eval framework for the ticket summarizer:** 4 quality dimensions, a 150-item golden set, mixed graders and a CI gate. ## Quality dimensions | Dimension | Definition | Grader | |---|---|---| | Format | Exactly 3 bullets, each under 25 words, valid priority label | Code | | Priority accuracy | Label matches the human label | Code (exact match) | | Faithfulness | No claim absent from the ticket | Model-graded, 1-4 rubric | | Coverage | Customer problem, attempted fix and requested action all present | Model-graded, 1-4 rubric | ## Golden dataset - 150 tickets: 100 sampled by category, 30 hard cases (multi-issue, angry tone, non-English), 20 edge cases (empty body, signature only). - Version the file in the repo; add new failure cases, never edit old ones silently. ## Judge calibration 1. Two humans score 40 items on faithfulness and coverage. 2. Run the judge on the same 40; compare agreement per dimension. 3. Revise the rubric until judge and humans agree closely, then freeze the judge prompt and model version. ## CI regression rules - Format and priority graders run on every PR; any format failure blocks merge. - Faithfulness mean may not drop below the current baseline; a drop blocks. - Coverage drop of more than 0.2 on the 1-4 scale requires a named reviewer to approve. ## Next steps - Collect the 150 tickets and human labels. - Run the calibration round before trusting judge scores. - Record the current prompt's scores as the baseline.

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    About this skill

    You changed the prompt, tried four inputs, it looked better, you shipped — and three days later support tickets say outputs are worse for an entire class of inputs you didn't test. Eval-less LLM development is just deferred debugging with a user-facing blast radius. This skill builds your evaluation framework: the test case set that covers your real distribution (not just the examples you had handy), the scoring rubric that maps to the quality dimensions that actually matter, the automated evaluation pipeline that runs on every prompt change, and the regression detection that tells you when a model upgrade quietly breaks your use case. It also covers the tricky parts — LLM-as-judge calibration, handling non-determinism in pass/fail metrics, and the human evaluation spot-checks that catch what automation misses. Give it your task, your current failure modes, and your quality bar; it returns an eval suite you can run in CI.

    - Does not execute code-based tests directly. - Requires user to provide initial real-world examples. - Judge calibration requires human input for accuracy.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Listed4 months ago
    Updated12 days ago

    What's inside

    Frequently Asked Questions

    This framework eliminates 'vibe-based' development by providing a structured methodology for measuring prompt performance, ensuring that updates improve your model without causing regressions in other areas.

    The framework is designed to be platform-agnostic; it generates architectural blueprints and rubrics that integrate seamlessly with LangChain, LlamaIndex, and standard CI/CD pipelines.

    You’ll receive a comprehensive evaluation strategy, including custom-tailored grading rubrics, a roadmap for building your 'golden dataset,' and technical specifications for automated quality gates.

    Yes, it includes specialized workflows for 'LLM-as-a-Judge' systems and provides guidelines for human calibration to ensure your automated metrics align with real-world user expectations.

    Setup involves providing your specific product goals and tech stack details; the agent then generates a tailored implementation plan, reducing the time to build a robust testing suite from weeks to hours.

    The framework is built to scale with your project, allowing you to add new dimensions, updated datasets, and more complex rubrics as your application's requirements evolve over time.