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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

    DimensionDefinitionGrader
    FormatExactly 3 bullets, each under 25 words, valid priority labelCode
    Priority accuracyLabel matches the human labelCode (exact match)
    FaithfulnessNo claim absent from the ticketModel-graded, 1-4 rubric
    CoverageCustomer problem, attempted fix and requested action all presentModel-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.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Define multi-dimensional scoring rubrics for LLM-as-a-judge workflowsConstruct high-signal golden datasets from production logs and edge casesAutomate regression testing for prompts within CI/CD pipelinesCalibrate automated judges against human ground-truth labelsCompare performance across different models (e.g. GPT-4 vs Claude 3.5)

    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.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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