Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    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 are building a customer support bot for a FinTech app using LangChain. I need to set up an evaluation framework to ensure it doesn't give bad financial advice and follows our brand voice.

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

    Evaluation Framework for FinTech Support Bot

    Quality Dimensions: Financial Accuracy (Pass/Fail), Regulatory Compliance (Safety), and Brand Tone (1-5 Likert Scale).
    2. Golden Dataset: 50 pairs of real support tickets + 10 adversarial 'jailbreak' attempts.
    3. CI Integration: Block merges if 'Financial Accuracy' drops below 100% on the golden set.

    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

      Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.

    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.

    Skills folder by agent

    Click the path to copy it. Create the folder if it does not exist yet.

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    Security scanned

    Verified clean 2 months ago

    • Passed all security checks, Safe to install

    Listed3 months ago
    Updated1 month ago

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