| name | orchestrating-swarms |
|---|---|
| description | Master multi-agent orchestration using Claude Code's TeammateTool and Task system. Use when coordinating multiple agents, running parallel code reviews, creating pipeline workflows with dependencies, building self-organizing task queues, or any task benefiting from divide-and-conquer patterns. |
Master multi-agent orchestration using Claude Code's TeammateTool and Task system.
Over the last few months, projects like Gas Town by Steve Yegge and OpenClaw by Peter Steinberger have made “AI agent orchestrators” feel suddenly mainstream. It is tempting to treat them as a new kind of intelligence, but under the hood they are still a small set of primitives wired together with discipline: an LLM API call, a state loop, tools, memory, and orchestration.
This raises a practical question: what is actually inside an “agent,” and how is it different from ChatGPT (a chat UI over a model) or coding tools like Claude Code (an agentic coding surface)? Gas Town’s README frames it as a “multi‑agent orchest
Companion prompts for the video: OpenClaw after 50 days: 20 real workflows (honest review)
These are the actual prompts I use for each use case shown in the video. Copy-paste them into your agent and adjust for your setup. Most will work as-is or the agent will ask you clarifying questions.
Each prompt describes the intent clearly enough that the agent can figure out the implementation details. You don't need to hand-hold it through every step.
My setup: OpenClaw running on a VPS, Discord as primary interface (separate channels per workflow), Obsidian for notes (markdown-first), Coolify for self-hosted services.
Prompts to recreate each piece of the OpenClaw system. Use these with any AI coding assistant.
1. Personal CRM "Build a personal CRM that automatically scans my Gmail and Google Calendar to discover contacts from the past year. Store them in a SQLite database with vector embeddings so I can query in natural language ('who do I know at NVIDIA?' or 'who haven't I talked to in a while?'). Auto-filter noise senders like marketing emails and newsletters. Build profiles for each contact with their company, role, how I know them, and our interaction history. Add relationship health scores that flag stale relationships, follow-up reminders I can create, snooze, or mark done, and duplicate contact detection with merge suggestions. Link relevant documents from Box to contacts so when I look up a person, I also see related docs."
2. Meeting Action Items (Fathom)
| # OpenClaw Implementation Prompts | |
| Each prompt below is a self-contained brief you can hand to an AI coding assistant (or use as a project spec) to build that use case from scratch. Adapt the specific services to whatever you already use — the patterns are what matter. | |
| --- | |
| ## 1) Personal CRM Intelligence | |
| ``` | |
| Build me a personal CRM system that automatically tracks everyone I interact with, with smart filtering so it only adds real people — not newsletters, bots, or cold outreach. |
This is not a proposal. This documents existing but hidden functionality found in Claude Code v2.1.19 binary, plus speculation on how it could be used.
TeammateTool already exists in Claude Code. We extracted this from the compiled binary at ~/.local/share/claude/versions/2.1.19 using strings analysis. The feature is fully implemented but gated behind feature flags (I9() && qFB()).