Agent Skills: fleet-retro

Post-mortem on a finished fleet of parallel /loop /auto sessions — measures each session with scripts/fleet-metrics.py (blind-sleep burn, dispatch mode, heartbeat compliance, classifier blocks, state-vs-reality drift, review churn with findings origins and the implementing-tier join, token and estimated-dollar attribution by agent type and model plus the developer-lane split (implementation vs fix batch) — cache-aware, with main-loop thinking share, cost per shipped issue, the context-size distribution (the autocompact gauge), shipped-issue provenance (the treadmill share), and a cross-run trend ledger diffing the last six fleets' headline gauges), reconciles the shipped ledger against git and Linear, audits the issues the run FILED for duplicates and stranded states, then reports ranked findings and applies the fixes you approve. The bookend to /auto-prep. Use when the user says 'fleet retro', 'review the fleet run', 'how did the fleet do', 'post-mortem the auto run', or invokes /fleet-retro.