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adjohnadjohn/pstack/skills/swarm/SKILL.md

swarm

Skill9 starsChanged 13 days agoMITView on GitHub

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

87 other projects use a version of this, 45 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use make-bot-ui.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • eryzerzSkill

    make-bot-ui

    Use when building a custom UI (page, dashboard, buttons) that should wake a Grok Bot over a webhook, when the user must provide a webhook sender key, or when exposing that UI on Tailscale.

    Also in 19 projects that use this

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 83 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    When a worker must start from a non-default pushed branch, say so in its brief: it checks out that branch first, in its own worktree (git …

    line 29
  2. Distinctive:

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

    line 33

“Common” is measured: kept by at least 6 in 10 of the 83 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Swarm

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. Pick the worker model from the swarm workers line in ~/.claude/pstack-models.md. If the file or that line is missing, use haiku. For auto or inherit-parent, omit model so the workers run on the parent model. If the Agent tool rejects a slug, use the default and say so. If it rejects the default, use the nearest Claude tier from its error message. For a model race, name each arm's model up front. Pick each subagent's reasoning level (the Agent effort) for the job, per the Subagents section of the poteto-mode skill.
  4. 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: general-purpose, run_in_background: true, and the step 3 model, left unset for auto or inherit-parent.

When a worker must start from a non-default pushed branch, say so in its brief: it checks out that branch first, in its own worktree (git worktree add <dir> origin/<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.

Phase D: Report

Return one consolidated in-chat report with a result table, issue one-liners, gaps or dropouts, and the race rule when used.

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