"""
Purpose Agent Optimization — Epigenetic self-improvement pipeline.

Optimize behavior BEFORE touching model weights:
  1. Fingerprint capabilities from traces
  2. Build filtered datasets from successful trajectories
  3. Create prompt packs (optimized system prompts + skills + examples)
  4. Shadow-evaluate candidates against baselines
  5. Only if plateau persists: plan LoRA/distillation (optional)

Key principle: prompt/skill/memory optimization first. Weight updates last.
"""
from purpose_agent.optimization.fingerprint import CapabilityFingerprint, fingerprint_traces
from purpose_agent.optimization.dataset import TraceDatasetBuilder
from purpose_agent.optimization.prompt_pack import PromptPack, PromptPackBuilder
from purpose_agent.optimization.shadow_eval import ShadowEvaluator
from purpose_agent.optimization.optimizer import AgenticOptimizer, OptimizationState

__all__ = [
    "CapabilityFingerprint", "fingerprint_traces",
    "TraceDatasetBuilder",
    "PromptPack", "PromptPackBuilder",
    "ShadowEvaluator",
    "AgenticOptimizer", "OptimizationState",
]