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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>