# Swarm

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

- Skill: `scriptedalchemy/swarm` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add scriptedalchemy/swarm`
- Raw SKILL.md: https://api.skillmd.com/api/skills/scriptedalchemy/swarm/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: ScriptedAlchemy (https://skillmd.com/u/scriptedalchemy)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/scriptedalchemy/swarm

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Before following this workflow, read [the Codex runtime contract](../../CODEX.md). It defines plugin paths, model configuration, delegation, and persistence.

# Swarm

Fan out N parallel Codex 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 host concurrency limit.
4. Pick the worker model from `swarm workers` in `~/.codex/pstack-models.md` when present. Otherwise use `gpt-5.6-luna`. For a model race, name each arm's model up front.
5. Give each worker its own writable output when it writes.

## Phase B: Fan out

Spawn workers with the available Codex collaboration tools and configured model and reasoning effort. Keep at most the host's available worker capacity in flight, refilling as workers finish until all N have run. Use the default agent only if the current schema exposes an agent selector. All workers use the current host; authenticated tools, browser state, and simulator access follow its permissions.

For concurrent writers, create separate git worktrees at the requested branch and pass their absolute directories in the briefs. Read-only workers can share the checkout; independent report files outside a checkout do not require worktrees. Serialize workers that need the same browser or simulator state.

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.

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

## Phase C: Aggregate

Read the terminal results. 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.