swarm

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 todolist 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 the pstack settings file when present (~/.cursor/rules/pstack-models.mdc in Cursor, ~/.agents/pstack-models.md in other harnesses). 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.

Other harnesses. These parameters belong to Cursor's Task tool, and environment: "cloud" runs a Cursor cloud agent. In another harness, use its subagent tool: Agent in Claude Code (subagent_type: general-purpose), task in OpenCode (subagent_type: general), spawn_agent in Codex. Workers run locally there, so give each one its own worktree or output path. Keep the brief and the model. Drop parameters your tool doesn't have. If your harness has no subagent tool, as in Pi without an extension, run the workers yourself, one after another.

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 note it.

Phase C: Aggregate

Read the terminal results. For coverage, every required slice needs a result. For a race, apply the selection rule declared up front. Use first pass, rank all, or best-of. Do not paste raw worker dumps.

Keep a compact result table, one-line evidenced issues, and explicit gaps or dropouts.

Phase D: Report

Return one consolidated in-chat report with the table, issue one-liners, gaps or dropouts, and the race rule when used.