Three additions driven by the goal of price-aware, model-flexible sleep:
1. DualBackend + build_backend(): route attempt->TARGET model and
reflect/judge->OPTIMIZER model (SkillOpt's target-vs-optimizer split).
gbrain runner gains --optimizer-backend/-model + --target-backend/-model.
2. run_transfer.py: sleep-scenario cross-model transfer. Optimize a skill on a
SOURCE model (e.g. cheap haiku), freeze it, evaluate held-out on a TARGET
model (e.g. expensive sonnet) with no further optimization — plus a direct
reference. Mirrors the SkillOpt paper's transfer table; quantifies the
"optimize cheap overnight, deploy anywhere" value prop.
3. llm_miner.py: turn real harvested transcripts into TaskRecords WITH checkable
rule/rubric judges, wired into the cycle for non-mock backends, so real-data
lift becomes measurable (heuristic miner remains the no-API fallback).
Fixed a str.format brace bug the new unit test caught.
19 tests pass.
Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
Yifan Yang