v0.8.0 - The decision system, with fitted guarantees

STROT is now a complete decision system: compile intent once with an LLM, decide on every record forever - locally, in type-safe JSON, with a fitted statistical guarantee.

Highlights since 0.2.0

• strot.run() - production JIT: content-addressed artifact cache, schema-drift busting, compile-once/execute-forever (92.5% measured grounded routing, ~60ms/record on CPU, $0 marginal, 0 LLM calls per runtime record)
• strot.decide() - typed questions (choice / score / boolean) over records, typed JSON output with probabilities and confidence
• Golden-record promotion gate - candidate artifacts are executed against your known answers inside the compile repair loop; only passing artifacts are ever served
• Conformal calibration (target_risk=alpha) - temperature scaling + split conformal prediction fitted at compile time from an LLM-teacher-labeled sample; dispatched decisions carry a marginal guarantee (wrong <= alpha), everything else abstains
• Compiler-synthesized routing labels + state projection - measured +18pp accuracy over naive integration on the identical model
• Native Julia-1 integration via its typed-decision API; swappable nlu= slot (local checkpoint or hosted APIs)
• Three synthesizer clients: Anthropic, OpenAI, local claude CLI proxy - resolved lazily, only on cache miss
• Episode logging - every compile recorded to episodes.jsonl
• prefetch_nlu() - bake the decision model into images at build time; fully offline production
• New README and developer guide

83 tests; verified end-to-end in clean Docker containers with the real 144M decision model.