/swarm
Fan out N parallel workers, drain them, and return one report. Use for /swarm, 'swarm this', or parallel coverage, races, gauntlets, and exploration.
$ npx -y skills add cursor/plugins --skill swarm --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/swarm
Context preview
The summary Claude sees to decide when to auto-load this skill.
Fan out N parallel workers, drain them, and return one report. Use for /swarm, 'swarm this', or parallel coverage, races, gauntlets, and exploration.
SKILL.md
swarm.SKILL.mdname: 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
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 `~/.cursor/rules/pstack-models.mdc` when present. Otherwise use `grok-4.5-fast-xhigh`. For a model race, name each arm's model up front. 5. Give each worker its own writable output when it writes. Use a worktree, branch, or `/tmp/swarm-<slug>/worker-<n>/`.
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 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.
Read more
name: 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
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 `~/.cursor/rules/pstack-models.mdc` when present. Otherwise use `grok-4.5-fast-xhigh`. For a model race, name each arm's model up front. 5. Give each worker its own writable output when it writes. Use a worktree, branch, or `/tmp/swarm-<slug>/worker-<n>/`.
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 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.
Official Cursor plugins for popular developer tools, frameworks, and SaaS products. Each plugin is a standalone directory at the repository root with its own .cursor-plugin/plugin.json manifest.
Repo: cursor/plugins
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