domain-eval-set — Your Held-Out Domain Benchmark

"Compiled program beats baseline on a held-out test set (not the val set used in optimization)." — DSPy SOP exit criterion [dspy.ai/learn/optimization/overview/]

"Build the eval loop before optimizing anything. Every subsequent change must be gated on these numbers." — LlamaIndex SOP Stage 2

This is an ENHANCE overlay skill. It produces one artifact — a versioned, sealed, human-labeled set of 50–200 examples drawn from your domain — that other skills consume: [[agentsop-regression-gate]] enforces it on every PR, [[agentsop-metric-design]] defines the scoring function applied to each example, and [[lm-evaluation-harness]] runs the complementary public-capability axis. The core claim: public benchmarks tell you the model is smart in general; only a held-out domain set tells you it works on your task. The latter is the one that predicts production.