Skill UI

Spawns N parallel AI subagents that compete on the same task using isolated git worktrees. A coordinator dispatches, monitors, evaluates results by metric or LLM judge, then merges the winning branch. Ideal for parallel optimization, content variations, refactors, and research exploration.

Provides comprehensive guidelines for designing, auditing, and documenting empirical evidence for high-level AI research submissions (e.g., AAAI). It covers essential methodologies including strong baselines, single-factor ablations, robustness testing (shift, seed, prompt), statistical significance, and ensuring perfect alignment with reproducibility checklists and ethical reporting. Use this guide before submission to ensure empirical evidence strongly supports all claimed AI contributions.