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/swarm

@12d587d
by cursorcursor/plugins9.1k stars
857

Fan out N parallel workers, drain them, and return one report. Use for /swarm, 'swarm this', or parallel coverage, races, gauntlets, and exploration.

Use this Skill: https://skilld.dev/gh/cursor/plugins/swarm

This session only. Nothing lands on disk.

SKILL.md

≈39 tokens always: the name and description. ≈683 when used: this file.

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.
  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 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.

Source: SKILL.md on GitHub

No alerts7d3 checks · Risk SAFE
  • Gen Agent Trust Hub7d

    The skill manages parallel subagents to perform tasks and aggregate reports, providing a framework for cloud or local task execution with specific model selection logic.

  • Socket7d

    No alerts

  • Snyk7d

    Risk: LOW · No issues

Signed by skilld at 12d587d. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

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disable-model-invocation
true

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