swarm

Orchestrates parallel cloud workers to cover slices or race briefs and returns one aggregated report.

Updated Jul 29, 2026
One-click install
npx skills add https://github.com/mmdmcy/fluttAIrbar --skill swarm-mmdmcy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: swarm
Source: https://github.com/mmdmcy/fluttAIrbar/tree/main/plugins/pstack/skills/swarm
Command: npx skills add https://github.com/mmdmcy/fluttAIrbar --skill swarm-mmdmcy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Coordinating multiple parallel AI workers manually is error-prone: briefs get inconsistent, results arrive in scattered formats, and aggregation becomes guesswork. This Skill provides a structured four-phase workflow to fan out N parallel workers, collect their terminal results, and deliver one consolidated report. ## Core Features & Use Cases - Parallel Fan-Out: Spawns N background workers in a single message, each with a standalone brief covering its own slice or race arm. - Coverage and Race Shapes: Supports partitioning work into slices, racing identical briefs across workers or models, or mixing both, with selection rules (first pass, rank all, best-of) declared up front. - Structured Aggregation: Collects PASS, ISSUES, or BLOCKED results with evidence into a compact table, noting gaps and worker dropouts. - Use Case: Ask to swarm a codebase review across five modules; each worker audits its slice in an isolated worktree, and you receive one report with per-module results, evidenced issues, and any gaps. ## Quick Start Use $swarm to fan out five parallel workers that each review one module of this repository and return a single consolidated report.

Frequently Asked Questions about swarm

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run parallel AI workers on a coding task?▼

Use the swarm workflow: frame a done predicate, choose a coverage or race shape, then spawn N background workers in one message with standalone briefs. Each worker reports PASS, ISSUES, or BLOCKED with evidence, and the parent aggregates everything into one report.

What is the difference between coverage and race shapes in a swarm?▼

Coverage partitions the task into distinct slices so each worker handles a different scope. A race gives N workers identical briefs and applies a declared selection rule such as first pass, rank all, or best-of to pick the winning result.

Can each swarm worker use a different model?▼

Yes. The worker model is picked from the swarm workers entry in ~/.codex/pstack-models.md when present, otherwise it defaults to auto. For a model race, each arm's model must be named up front before spawning.

What happens if a swarm worker drops out or fails?▼

The workflow proceeds with N-1 workers and explicitly notes the dropout in the final report. Aggregation still requires every required slice to have a result for coverage shapes, and gaps are listed alongside the result table.

How do swarm workers avoid overwriting each other's changes?▼

Each worker that writes gets its own writable output, such as a dedicated worktree, branch, or a directory like /tmp/swarm-<slug>/worker-<n>/. Workers needing a non-default starting point receive a cloud_base_branch parameter.