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dspy-evaluation-harness

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Build DSPy evaluation harnesses with rich-feedback metrics that are essential for GEPA optimization. Use when writing a metric function, calling dspy.Evaluate, splitting dev/val sets, debugging "why is my optimizer not improving?", or designing CI-ready DSPy eval suites.

skills/dspy-evaluation-harness/SKILL.md

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# DSPy Evaluation Harness (3.2.x)

The metric is usually more important than the program. For `dspy.GEPA` especially, the quality of **textual feedback** in your metric determines whether optimization converges.

## Two rules

1. **Return a `dspy.Prediction(score=..., feedback=...)`, not a dict.** `dspy.Evaluate`'s parallel executor aggregates scores via sum, which breaks on dict outputs (`TypeError: unsupported operand type(s) for +: 'int' and 'dict'`). `dspy.Prediction` supports `__float__`/`__add__` and is what GEPA's adapter natively unwraps. A bare float still works for pure `dspy.Evaluate` scoring, but GEPA needs the score+feedback pair.
2. **Separate valset.** Never optimize and evaluate on the same examples. Optimizers overfit fast.

## Canonical rich-feedback metric

```python
import dspy
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