---
name: cursor-swarm
description: "Fan out N parallel workers, drain them, and return one report. Use for /swarm, 'swarm this', or parallel coverage, races, gauntlets, and exploration."
disable-model-invocation: true
metadata:
  decimalai-skill-id: "1e4f4ed7-1973-4503-a5e1-680c4192ff2c"
  decimalai-version-id: "f398d31a-9ba0-4a85-9034-e27f4578fb05"
  decimalai-version: "5"
  decimalai-body-sha256: "205ae3aab631acc2365a9e28c309b04c725b77d517d9fce8dda154b4a3c4e67a"
  decimalai-name: "cursor/swarm"
  decimalai-format: "1"
  decimalai-invocation: "user"
  decimalai-category: "agent-tooling"
  decimalai-source: "https://app.decimal.ai/s/cursor-swarm@5/SKILL.md"
---
# 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 the `swarm workers` line in `~/.cursor/rules/pstack-models.mdc`. If the rule or that line is missing, use `grok-4.7-xhigh-fast`. For `auto` or `inherit-parent`, omit `model` so the workers run on the parent model. If the Task tool rejects a slug, use the default and say so. If it rejects the default, use the closest valid slug of the same family from its error message. For a model race, name each arm's model up front. Pick each subagent's reasoning level and fast or standard speed for the job, per the Subagents section of the **poteto-mode** skill.
5. Give each worker its own writable output when it writes. When workers verify or measure commits, each brief names the exact SHAs. A measurement brief also names the method (sample count, what one sample is, order). The worker records both in its result.

## Phase B: Fan out

Spawn all N workers in one message with `subagent_type: generalPurpose`, `environment: "cloud"`, `run_in_background: true`, and the step 4 model, left unset for `auto` or `inherit-parent`. 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. A worker that can prove a defect reports `ISSUES` and lists every issue it can prove, not only the first.

If a worker drops out, proceed with N-1 and note it.

## Phase C: Aggregate

Read the terminal results. Drop a result that does not record the SHAs and method its brief names, and respawn that worker once. After a second miss, record a gap. A gap does not count as a pass. 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.