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/tests/mathutil.go

@@ -0,0 +1,23 @@
+package tests
+
+import "math"
+
+// cosineSimilarity computes the cosine similarity of two equal-length
+// vectors: dot(a,b) / (|a| * |b|). Used to derive ground-truth expected
+// values for numeric tests at catalog-registration time rather than
+// hardcoding a pre-computed constant.
+func cosineSimilarity(a, b []float64) float64 {
+	var dot, normA, normB float64
+	for i := range a {
+		dot += a[i] * b[i]
+		normA += a[i] * a[i]
+		normB += b[i] * b[i]
+	}
+	return dot / (math.Sqrt(normA) * math.Sqrt(normB))
+}
+
+// round4dp rounds v to 4 decimal places, matching the precision the
+// py-cosine prompt asks the model to answer to.
+func round4dp(v float64) float64 {
+	return math.Round(v*10000) / 10000
+}