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/__init__.py

@@ -0,0 +1,29 @@
+"""ReflACT: Reflective Agent Tuning.
+
+A general-purpose framework for iteratively optimizing LLM agent skills
+through structured reflection and self-improvement.
+
+Pipeline stages:
+  1. Rollout   — execute episodes with current skill
+  2. Reflect   — analyze trajectories, generate patches
+  3. Aggregate — hierarchical merge of patches
+  4. Select    — rank and select top edits
+  5. Update    — apply edits to skill document
+  6. Evaluate  — validate candidate skill, accept/reject
+"""
+
+__version__ = "0.1.0"
+
+from skillopt.types import (  # noqa: F401
+    BatchSpec,
+    Edit,
+    EditOp,
+    FailureSummaryEntry,
+    GateAction,
+    GateResult,
+    MetaReflectResult,
+    Patch,
+    RawPatch,
+    RolloutResult,
+    SlowUpdateResult,
+)