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/eval.go

@@ -0,0 +1,40 @@
+// Package eval provides deterministic evaluators that score a (normalized)
+// LLM response against an expected answer. All evaluators are pure functions
+// of the response text plus evaluator configuration; none call the network.
+package eval
+
+import "context"
+
+// Score is the outcome of evaluating one response. Value is in [0, 1] unless
+// Skipped is true (e.g. a required toolchain is missing), in which case
+// Value carries no meaning and the test is excluded from aggregates.
+type Score struct {
+	Detail  string
+	Value   float64
+	Skipped bool
+}
+
+// Evaluator scores a normalized response string.
+type Evaluator interface {
+	Evaluate(ctx context.Context, response string) Score
+}
+
+// EvaluatorFunc adapts a plain function to the Evaluator interface.
+type EvaluatorFunc func(ctx context.Context, response string) Score
+
+// Evaluate implements Evaluator.
+func (f EvaluatorFunc) Evaluate(ctx context.Context, response string) Score {
+	return f(ctx, response)
+}
+
+// clamp01 constrains v to the closed interval [0, 1].
+func clamp01(v float64) float64 {
+	switch {
+	case v < 0:
+		return 0
+	case v > 1:
+		return 1
+	default:
+		return v
+	}
+}