Runs StepCOVNet GPU training and MERT extraction on Windows via WSL with TensorFlow CUDA libraries. Use for train_onset, overfit scripts, extract_mert_features with cuda, or when TensorFlow sees zero GPUs in WSL.
One-prompt agent autoresearch loop for StepCOVNet: goal + time budget → orient, hypothesize, run, log, repeat until success or deadline. Use when the user says "run autoresearch", gives a research goal with a budget, or names a profile (e.g. ar-tide-overfit). Not for unattended Python planners (--hours / lattice).
Deprecated alias — use autoresearch skill with profile ar-tide-overfit.
Starts a research session and logs EXP and NOTE entries with timestamps. Use when beginning research work, logging experiments, or writing discussion notes. Promote findings to PAPER_OUTLINE only when drafting the paper.
Audit and sync agent brain — read all rules from disk, reconcile skills/catalogs/routing. Triggers on refresh agent brain, refresh all rules, audit rules, or after steering promotion. Read-only audit script; agent writes doc updates.
Triggers on process mistakes and steering corrections; run steering-correction-promotion for artifact choice, context optimization, and JRN receipt. Journal alone is incomplete.
On user steering corrections or remember-this, pick the smallest durable artifact (skill section, scoped rule, or rare always-apply rule), slim AGENTS.md, optimize agent-brain context, then prepend JRN. Use instead of creating new alwaysApply rules by default.
Diagnoses onset event F1, train/eval matching mismatches, confidence collapse, and threshold effects. Use when F1 is zero or low, conv1d collapse is suspected, or the user asks about Hungarian vs ordered assignment, matching tolerance, or debug_onset_overfit output.