# AI Eval Lab

> Evaluate an AI hackathon feature against a simple baseline using task cases, failure labels, latency, and cost before making reliability claims.

- Skill: `shreyp087/ai-eval-lab` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add shreyp087/ai-eval-lab`
- Raw SKILL.md: https://api.skillmd.com/api/skills/shreyp087/ai-eval-lab/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: Shreyp087 (https://skillmd.com/u/shreyp087)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/shreyp087/ai-eval-lab

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# AI evaluation lab

Define the actual task and the decision the evaluation must support. A model that sounds persuasive is not necessarily useful. Compare against a simple baseline such as search, rules, an unassisted workflow, or the previous implementation.

Build a small versioned case set covering typical inputs, difficult inputs, missing information, and an abuse case relevant to the feature. Separate development examples from held-out examples. Mark synthetic cases and avoid pretending they represent real user validation. Do not upload private case data to a provider without authorization.

For each case record input, expected behavior, acceptable variation, severity if wrong, and scoring method. Prefer executable checks for structured outputs and human inspection for subjective properties. If using an LLM judge, calibrate it against human examples, randomize answer order where appropriate, and report disagreements.

Record provider/model/version when available, prompts, parameters, tool definitions, dataset revision, repetitions, latency at the user boundary, token usage, and cost source. Measure provider charges only when actual usage/rates are available; otherwise label estimates. For nondeterministic behavior repeat representative cases and report the number of trials.

Inspect failures by cause: retrieval, missing evidence, reasoning, invalid format, tool misuse, refusal, timeout, or ambiguous target. Improve the largest consequential cluster, rerun affected cases, and check held-out regressions. Do not optimize solely to visible examples or overwrite failing cases to inflate a score.

For retrieved content test whether malicious instructions in a document can influence tools or leak unrelated data. Test output validation and enforce action permissions outside the model. Use a local dummy fixture for these checks.

Deliver baseline comparison, case counts and limits, failure examples, measured latency/cost or clearly labeled estimates, and the next justified improvement. A tiny benchmark is engineering feedback, not proof of general reliability or a winning probability.