Skip to main content

swarm

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

ソース情報

リポジトリ
cursor/plugins
ソースの最終更新活動
2026年9月23日 20:03
検出された SKILL.md の言語
英語
スター
9,052
フォーク
849

インストール方法

デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。

ソースファイルを確認

インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。

SKILL.md を表示中

SKILL.md
ソースの指示 · 読み取り専用プレビュー
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 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.
GitHubで見る