Swarm
SkillAI & modelsFan out N parallel workers, drain them, and return one report. Use for /swarm, 'swarm this', or parallel coverage, races, gauntlets, and exploration.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Swarm skill
What this skill tells your AI
The instructions your AI receives, as published by michael-denyer/pstack-claude in plugins/pstack/skills/swarm/SKILL.md and read by ahel’s review.
On Codex, read the platform mapping, including its per-skill notes, before following this skill.
Fan out N parallel 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.
- Frame
- Fan out
- Aggregate
- Report
Phase A: Frame
- State the done predicate and the artifact or report the swarm must return.
- 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, orbest-ofbefore spawning. - Set N from the user or derive it from the shape. N is total workers, not the number that run at once.
- Pick the worker model from
swarm workersin~/.claude/pstack-models.mdwhen present. Otherwise use the default in Models. For a model race, name each arm's model up front. - Give each worker its own writable output when it writes.
Phase B: Fan out
Spawn all N workers in one message with subagent_type: "general-purpose", run_in_background: true, and the configured model. Claude Code subagents all run on this machine, so isolation comes from the worktree or output directory assigned in Phase A, not from a remote environment.
When a worker must start from a non-default branch, check that branch out in the worker's own worktree and name the worktree path in its brief.
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.
Models
Role defaults, stamped from plugins/pstack/models.json (edit there, rerun tools/generate.mjs). A matching role line in ~/.claude/pstack-models.md overrides each at runtime; see /setup-pstack.
- swarm workers:
claude-opus-5
Signals
- GitHub stars
- 341
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
swarm-michael-denyer- Source
- github.com/michael-denyer/pstack-claude