feat: LLM accuracy testing framework with 17-test catalog

Framework runs a category/subcategory test catalog against multiple
LLMs through an OpenAI-compatible endpoint. Deterministic evaluators
score each response. Reports render as table, markdown, or JSON.

internal/eval/composite.go

@@ -0,0 +1,70 @@
+package eval
+
+import (
+	"context"
+	"fmt"
+	"strings"
+)
+
+// WeightedEvaluator pairs an Evaluator with its share of the composite score.
+type WeightedEvaluator struct {
+	Eval   Evaluator
+	Weight float64
+}
+
+// W builds a WeightedEvaluator; a convenience constructor for All.
+func W(e Evaluator, weight float64) WeightedEvaluator {
+	return WeightedEvaluator{Eval: e, Weight: weight}
+}
+
+// compositeEval computes a weighted mean over its sub-evaluators, ignoring
+// sub-scores that are Skipped.
+type compositeEval struct {
+	weighted []WeightedEvaluator
+}
+
+// All returns an Evaluator that combines weighted sub-evaluators into a
+// single weighted-mean Score. Sub-scores marked Skipped are excluded from
+// both the numerator and the weight total; if every sub-evaluator is
+// skipped, the composite itself is Skipped.
+func All(weighted ...WeightedEvaluator) Evaluator {
+	return compositeEval{weighted: weighted}
+}
+
+// Mean wraps evaluators with equal weight 1 and combines them via All. A
+// convenience for the common case where every sub-check counts the same.
+// Named Mean, not Equal, to avoid reading as a synonym of the Equals
+// evaluator (S12) - Mean computes an unweighted average of N sub-scores,
+// while Equals is a single trimmed, case-insensitive string comparison.
+func Mean(evaluators ...Evaluator) Evaluator {
+	weighted := make([]WeightedEvaluator, 0, len(evaluators))
+	for _, e := range evaluators {
+		weighted = append(weighted, W(e, 1))
+	}
+	return All(weighted...)
+}
+
+func (c compositeEval) Evaluate(ctx context.Context, response string) Score {
+	var weightedSum, activeWeight float64
+	details := make([]string, 0, len(c.weighted))
+
+	for _, we := range c.weighted {
+		s := we.Eval.Evaluate(ctx, response)
+		if s.Skipped {
+			details = append(details, fmt.Sprintf("skipped (w=%.2f): %s", we.Weight, s.Detail))
+			continue
+		}
+		activeWeight += we.Weight
+		weightedSum += s.Value * we.Weight
+		details = append(details, fmt.Sprintf("%.2f (w=%.2f): %s", s.Value, we.Weight, s.Detail))
+	}
+
+	if activeWeight == 0 {
+		return Score{Skipped: true, Detail: "all sub-evaluators skipped: " + strings.Join(details, "; ")}
+	}
+
+	return Score{
+		Value:  clamp01(weightedSum / activeWeight),
+		Detail: strings.Join(details, "; "),
+	}
+}