# swarm

Fans out N parallel workers on one job, drains them, and returns a single consolidated report. Useful for coverage, model races and exploration.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 5200 installs · ★ 2737
- Author: backnotprop
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/backnotprop/pstack/tree/HEAD/skills/swarm/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/backnotprop-swarm

## What it does

The skill runs in four phases: Frame, Fan out, Aggregate, Report. You state the done predicate and the artifact the swarm must return. Then you pick the shape: partition the work into slices, race N workers on identical briefs, or mix both. For a race, the selection rule (`first pass`, `rank all`, `best-of`) gets declared before anything spawns.

The worker model comes from the `swarm workers` line in the pstack settings file: `~/.claude/rules/pstack-models.md` in Claude, `~/.cursor/rules/pstack-models.mdc` in Cursor, `~/.agents/pstack-models.md` in other harnesses. If that file or line is missing, the skill suggests starting with `grok-4.7-xhigh-fast`. With `auto` or `inherit-parent` you omit `model` and workers run on the parent model. Every brief stands alone: goal, scope, exact slice or race arm, how to verify, what to report.

In Claude workers spawn through the `Agent` tool and run locally, so each one needs its own worktree or output path. The `environment: "cloud"` and `cloud_base_branch` parameters belong to Cursor's `Task` tool — other harnesses simply don't have them. If a worker drops out, the run continues with N-1 and notes it. A result that doesn't record the SHAs and method named in its brief is dropped and that worker respawned once; after a second miss the skill records a gap, and a gap never counts as a pass.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add backnotprop/pstack --skill swarm -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/swarm/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/swarm/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add backnotprop/pstack --skill swarm -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/swarm/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add backnotprop/pstack --skill swarm -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/swarm/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install backnotprop/pstack swarm
```

2. **Manually: put the files in place**
- File: `.github/skills/swarm/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add backnotprop/pstack --skill swarm -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/backnotprop/pstack --path skills/swarm
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/swarm/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add backnotprop/pstack --skill swarm -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/backnotprop/pstack/tree/HEAD/skills/swarm/SKILL.md

## When to use it
- check several code slices at once
- compare models on the same task
- collect provable defects instead of the first one
- explore an unfamiliar codebase in parallel
- get one consolidated report instead of scattered answers

## Example prompts
- swarm this: audit error handling in `src/api/` and return a report
- race three workers on the same brief, best-of rule
- split the refactor into slices and run them in parallel

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 0c455522eccca76c.

BEGIN THIRD-PARTY FILE 0c455522eccca76c
```text
---
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

## 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 the pstack settings file (`~/.cursor/rules/pstack-models.mdc` in Cursor, `~/.claude/rules/pstack-models.md` in Claude Code, `~/.agents/pstack-models.md` in other harnesses). If the file 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 your subagent 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 or your harness's model list. For a model race, name each arm's model up front. Pick each subagent's reasoning level and fast or standard speed 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: generalPurpose`, `environment: "cloud"`, `run_in_background: true`, and the step 3 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`.

**Other harnesses.** These parameters belong to Cursor's `Task` tool, and `environment: "cloud"` runs a Cursor cloud agent. In another harness, use its subagent tool: `Agent` in Claude Code (`subagent_type: general-purpose`), `task` in OpenCode (`subagent_type: general`), `spawn_agent` in Codex. Workers run locally there, so give each one its own worktree or output path. Keep the brief and the model. Drop parameters your tool doesn't have. If your harness has no subagent tool, as in Pi without an extension, run the workers yourself, one after another.
```
END THIRD-PARTY FILE 0c455522eccca76c

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