control_looplisted

# control_loop — the router that learns The final systemic loop: every routing decision + outcome is recorded, and the effort→tier thresholds (read live by [[auto_router]]) are nudged from the data — keep more work local when local is reliable, escalate sooner when it isn't. ```bash python -m skills.control_loop.cli analyze # local %, savings, escalation/success python -m skills.control_loop.cli adapt # proposed threshold change + why python -m skills.control_loop.cli adapt --apply # write it; the router uses it next time python -m skills.control_loop.cli reset # back to defaults ``` ## How the loop closes 1. **Measure** — `auto_router.run` records each call to `~/.botte/control-ledger.jsonl` (effort, tier, local/cloud, tokens saved, escalated, success). 2. **Analyze** — aggregate: local %, escalation rate, success rate, tokens saved. 3. **Adapt** — conservative rule, needs ≥10 samples, small steps, clamped: - local reliable (success ≥85%, escalation <15%) → **raise** the LOCAL boundary (keep more work local → more savings). - local insufficient (escalation >30%) → **lower** it (escalate borderline sooner). 4. **Apply** — write `~/.botte/routing-thresholds.json`; `auto_router.effort` reads it live, so the next decisions reflect what actually worked. This is the Karpathy/second-brain pattern applied to routing: the system gets smarter from its own outcomes. Exposed via [[llm_mcp]] as `routing_stats`. Related: [[au