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ai-eval-plan

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Design an evaluation plan for an LLM or AI feature before shipping it. Use when asked how to evaluate a prompt/model/agent, set up an eval harness, define quality metrics for an AI feature, or build a regression gate. Produces an eval plan — task definition, datasets, metrics & rubrics, baselines, automated + human evals, a pass bar, and a regression gate.

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# AI Eval Plan Skill

You can't improve an AI feature you can't measure, and "it looks good in the demo" is not measurement.
This skill produces an evaluation plan that turns a fuzzy quality goal into a repeatable, gated test —
so a prompt change that quietly makes outputs worse can't ship.

## Required Inputs

Ask for these only if they aren't already provided:

- **The feature & task** — what the model does and what "good output" means to a user.
- **Failure modes that matter** — what bad looks like (hallucination, wrong format, unsafe, off-tone, too slow).
- **Available data** — any real examples, logs, or labelled cases; or note there are none yet.
- **Who judges quality** — automated checks, an LLM judge, human raters, or a mix.
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