swarm
cursor/plugins
Fan out N parallel workers, drain them, and return one consolidated report.
What is swarm?
Swarm spawns multiple cloud workers in parallel to cover separate slices, race identical briefs, or mix both strategies. Use it when you need parallel coverage, competitive races, gauntlets, or broad exploration—then aggregate results into a single report.
- Spawn N parallel cloud workers with independent briefs or identical tasks
- Partition work into slices or race workers on the same goal
- Aggregate results with configurable selection rules (first pass, rank all, best-of)
- Return one consolidated report with evidence, gaps, and dropouts
- Support mixed strategies combining slices and races
How to install swarm
npx skills add https://github.com/cursor/plugins --skill swarm- Access to cloud workers (environment: "cloud")
- Worker model specified or available from ~/.cursor/rules/pstack-models.mdc
- Clear done predicate and expected output format defined before spawning
How to use swarm
- 1.Frame the task: define done predicate, choose shape (slices, race, or mixed), set N workers, pick worker model, and ensure each worker has writable output
- 2.Fan out all N workers in one message with subagent_type: generalPurpose, environment: "cloud", run_in_background: true, and the selected model
- 3.Aggregate results by reading terminal output, dropping incomplete results (missing SHAs/method), applying selection rules, and noting gaps or dropouts
- 4.Report one consolidated summary with a compact result table, one-line evidenced issues, and explicit gaps or dropouts
Use cases
- Run parallel test suites across different code sections to speed up validation
- Race multiple approaches to the same problem and select the best result
- Cover separate features or modules simultaneously with independent workers
- Explore multiple solution strategies in parallel and compare outcomes
- Verify commits across different branches or configurations concurrently
- Developers optimizing test and validation speed
- Teams exploring multiple solution paths in parallel
- QA engineers running distributed coverage checks
- Researchers comparing competitive approaches
- Anyone needing parallel task execution with aggregated results
swarm FAQ
Shape refers to how you partition work: slices divide the task into separate pieces (each worker handles one slice), a race has all N workers tackle the same brief competitively, or mixed combines both strategies.
Proceed with N-1 workers and note the dropout in your final report. If a result is incomplete (missing SHAs and method), rerun that worker once; after a second miss, record it as a gap.
Check the `swarm workers` line in ~/.cursor/rules/pstack-models.mdc. If missing or the Task tool rejects it, use grok-4.7-xhigh-fast as default. For auto or inherit-parent, omit the model field so workers use the parent model.
Declare one up front: first pass (use the first successful result), rank all (compare all results), or best-of (select the highest-quality result). Apply it consistently when aggregating.
Use environment: "local" only when a worker needs access to something on the user's computer. Default to environment: "cloud" for parallel execution.
Full instructions (SKILL.md)
Source of truth, from cursor/plugins.
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.
- Frame
- Fan out
- Aggregate
- Report
Phase A: Frame
- State the done predicate and the artifact or report the swarm must return.
- 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, orbest-ofbefore spawning. - Set N from the user or derive it from the shape. N is total workers, not the cloud concurrency limit.
- Pick the worker model from the
swarm workersline in~/.cursor/rules/pstack-models.mdc. If the rule or that line is missing, usegrok-4.7-xhigh-fast. Forautoorinherit-parent, omitmodelso 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. - 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 rerun 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.
Related skills
More from cursor/plugins and the wider catalog.

tdd
Write focused regression tests before fixing bugs with clear, cheap test paths.

teach
Explain code and systems plainly by combining how and why analysis into one clear account.

technical-writing
Layered technical-writing standard: Diátaxis structure, Google style, STE rules, Global English syntax.

thermo-nuclear-code-quality-review
Extremely strict code quality review focused on maintainability, abstraction, and structural simplification.

thermo-nuclear-review
Comprehensive security and correctness audit of branch changes for bugs, breaking changes, and vulnerabilities.

thermos
Run parallel thermo-nuclear code reviews and synthesize findings for comprehensive branch audits.