Individual skill

agent-eval

Use when measuring whether an LLM or agent system actually got better and gating merges on it: golden sets, fixing an inflated LLM-as-judge, scoring RAG (faithfulness, contextual recall) or agent trajectories (tool correctness, completion), or picking an eval framework. NOT building the agent loop, tools or RAG plumbing (that is `building-agents`).

skills/agent-eval/SKILL.md

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# Measure agent quality you can defend and gate on

Turn "the agent feels better" into a number you can put in a PR check. You own the eval dataset, the scorer mix, the LLM-as-judge calibration, and the block-on-regression CI gate — framework-neutral, provider-neutral.

## Do NOT use — route instead

| The ask | Route to | Why it is not this skill |
| --- | --- | --- |
| Build the agent loop, tools, RAG plumbing | `building-agents` | It builds the system; you score it. They cross-link. |
| "Make the answers shorter / rewrite the prompt" | `prompt-engineering` | Evals say it is worse; that skill changes the words. You never edit the prompt. |
| pytest/jest on deterministic functions | `testing-py` / `testing-web` | Assert-equals on pure code, not stochastic outputs scored by a judge. |
| Dashboards / tracing of live production traffic | `observability` | Online monitoring; you are offline + pre-merge. |
| Red-team, jailbreak, prompt injection | `agent-safety` | Adversarial coverage, not quality measurement. |
| Per-token cost budgets and accounting | `cost-tracking` | You report cost-per-task as one metric; the discipline lives there. |