What problem does it solve? Coordinating multiple parallel AI workers manually is error-prone: briefs get inconsistent, results arrive in scattered formats, and aggregation becomes guesswork. This Skill provides a structured four-phase workflow to fan out N parallel workers, collect their terminal results, and deliver one consolidated report. ## Core Features & Use Cases - Parallel Fan-Out: Spawns N background workers in a single message, each with a standalone brief covering its own slice or race arm. - Coverage and Race Shapes: Supports partitioning work into slices, racing identical briefs across workers or models, or mixing both, with selection rules (first pass, rank all, best-of) declared up front. - Structured Aggregation: Collects PASS, ISSUES, or BLOCKED results with evidence into a compact table, noting gaps and worker dropouts. - Use Case: Ask to swarm a codebase review across five modules; each worker audits its slice in an isolated worktree, and you receive one report with per-module results, evidenced issues, and any gaps. ## Quick Start Use $swarm to fan out five parallel workers that each review one module of this repository and return a single consolidated report.