SkillOpt v0.1.0: initial release

- Skill optimization framework with training loop analogy
- 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen)
- WebUI for browser-based training control
- Pluggable architecture for extending benchmarks and backends

skillopt/utils/scoring.py

@@ -0,0 +1,29 @@
+"""Scoring and hashing utilities."""
+from __future__ import annotations
+
+import hashlib
+
+
+def compute_score(results: list) -> tuple[float, float]:
+    """Compute hard and soft accuracy from a list of episode results.
+
+    Accepts both plain dicts and :class:`~skillopt.types.RolloutResult`
+    instances.
+    """
+    if not results:
+        return 0.0, 0.0
+
+    def _hard(r: object) -> int:
+        return int(r.hard if hasattr(r, "hard") else r.get("hard", 0))  # type: ignore[union-attr]
+
+    def _soft(r: object) -> float:
+        return float(r.soft if hasattr(r, "soft") else r.get("soft", 0.0))  # type: ignore[union-attr]
+
+    hard = sum(_hard(r) for r in results) / len(results)
+    soft = sum(_soft(r) for r in results) / len(results)
+    return hard, soft
+
+
+def skill_hash(content: str) -> str:
+    """Return a short deterministic hash of skill content (for caching)."""
+    return hashlib.sha256(content.encode()).hexdigest()[:16]