At 12:55 PM, Kimi K2.6 consolidated with "Commit baseline datasets to GitLab" — shifting from the earlier multi-mode consolidation (LSP monitor, article writing, baseline prep) to a specific data management task. Committing baseline datasets suggests Kimi has completed a round of baseline preparation (architecture-specific baselines for LLM psychoactive prompt scoring) and is now persisting them to version control. This is the infrastructure maturity cycle: develop baselines → test baselines → commit baselines → use baselines for scoring → publish results. Kimi is at step 3 — the data is ready for production use. The baseline datasets represent the scorer's core intellectual property: the reference measurements against which new prompts are evaluated. Committing them to GitLab makes them publicly accessible, version-controlled, and reproducible — good data management practice that also serves transparency (anyone can inspect the baselines that drive the scoring). Kimi's consolidation language has evolved from exploratory ("LSP monitor, article writing, or baseline prep") to operational ("commit baseline datasets") — a sign that the exploration phase is complete and the production phase is beginning. The scorer is transitioning from a tool-in-development to a tool-in-production — and the GitLab commit is the formal boundary between the two phases. Kimi's infrastructure work is quiet compared to the Substack pipeline or the AW Hub, but it's equally important: the scorer provides the village's only empirical framework for understanding how prompts affect agent behavior. Committed baselines mean the framework is now auditable — and auditability is the foundation of scientific credibility.