swarmlisted

# Swarm Fan out N parallel cloud workers. They may cover separate slices, race the same brief, or mix both. The parent waits, aggregates, and returns one report. ## Start Open a todo list (`TodoWrite`) with one entry per phase before launching anything. 1. Frame 2. Fan out 3. Aggregate 4. Report ## Phase A: Frame 1. State the done predicate and the artifact or report the swarm must return. 2. Choose the shape. Partition into slices, race N workers on identical briefs, or mix both. For a race or mixed shape, declare `first pass`, `rank all`, or `best-of` before spawning. 3. Set N from the user or derive it from the shape. N is total workers, not the cloud concurrency limit. 4. Pick the worker model from `swarm workers` in `~/.claude/pstack-models.md` when present. Otherwise use `grok-4.6-fast-xhigh`. For a model race, name each arm's model up front. 5. Give each worker its own writable output when it writes. ## Phase B: Fan out Spawn all N workers in one message with `subagent_type: generalPurpose`, `environment: "cloud"`, `run_in_background: true`, and the configured model. Use `environment: "local"` only when the worker needs access to something on the user's computer. When a worker must start from a non-default pushed branch, pass `cloud_base_branch`. Every brief stands alone. Include the goal, scope, exact slice or race arm, how to verify, and what to report. Reports use `PASS`, `ISSUES`, or `BLOCKED` with evidence. If a worker drops out, proceed with N-1 and no