gastown

Coordinate multi-agent workflows across rigs with GUPP-based hook execution.

Updated Feb 11, 2026
One-click install
npx skills add https://github.com/leto-labs/openclaw-bootstrap-config --skill gastown-leto-labs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gastown
Source: https://github.com/leto-labs/openclaw-bootstrap-config/tree/main/.agents/skills/gastown
Command: npx skills add https://github.com/leto-labs/openclaw-bootstrap-config --skill gastown-leto-labs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Gas Town coordinates AI workers across rigs to automate end-to-end task execution, from assignment on the hook to final merge, enabling scalable multi-agent workflows with resilience.

Core Features & Use Cases

  • Cross-rig orchestration coordinates polecats, crew, witnesses, and refinery to complete multi-step tasks.
  • Workflow visibility with convoys, hooks, and Beads memory for persistence.
  • Diagnostics & escalation automate health checks and human intervention when issues arise.

Quick Start

Sling a bead to a rig to trigger a polecat and observe the hook-driven workflow.

Frequently Asked Questions about gastown

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

FAQPage Schema
How do I orchestrate AI agents across multiple rigs for task automation?▼

Multi-agent workflow orchestration coordinates AI workers across rigs to automate task execution from assignment to final merge. It applies cross-rig coordination using transient polecats, persistent crew, and per-rig watchers for development and deployment tasks.

How does hook-driven execution work for multi-step AI workflows?▼

Hook-driven execution triggers AI workers via GUPP-based hooks, supporting sling, convoy, and refinery triggers. This mechanism automates end-to-end task execution by coordinating polecats, crew, witnesses, and refinery to complete multi-step workflows.

What's the best way to automate health checks and escalation for AI worker pipelines?▼

Automated diagnostics and escalation handle health checks and trigger human intervention when issues arise during workflow execution. This ensures resilient multi-agent orchestration by monitoring worker pipelines and managing failure recovery automatically.

Does multi-agent workflow orchestration support persistent memory for cross-rig task coordination?▼

Multi-agent workflow orchestration integrates with the Beans memory system for persistence, providing workflow visibility across convoys and hooks. This allows cross-rig projects to maintain state and coordinate polecats and crew effectively throughout task execution.

Can I trigger a specific AI worker on a remote rig to start an automated workflow?▼

Triggering a specific AI worker on a remote rig is done by slinging a bead to that rig, which initiates a polecat and activates the hook-driven workflow. This starts the orchestrated multi-step task execution process automatically.

When do I need cross-rig orchestration for AI agent workflows?▼

Cross-rig orchestration is needed when multi-step tasks require coordination across multiple rigs with transient polecats and persistent crew for development, integration, and deployment. It enables scalable multi-agent workflows with resilience for complex project pipelines.