Generalized versions of all root .md files used by OpenClaw. These files are loaded into the agent's system prompt on every request (except MEMORY.md which is conditional).
Copy these as starting points and customize for your own setup. Replace <placeholders> with your values.
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.
You are given a task to integrate an existing React component in the codebase.
The codebase should support:
If it doesn't, provide instructions on how to setup the project via shadcn CLI, install Tailwind or TypeScript.
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
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
We need a way to give each Discord channel its own persistent "computer" running Claude Code CLI, where:
These rules define how an AI coding agent should plan, execute, verify, communicate, and recover when working in a real codebase. Optimize for correctness, minimalism, and developer experience.
| name | description |
|---|---|
orchestrating-swarms |
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.
Date: 2026-01-25 Analysis: Side-by-side comparison of Claude Flow V3 swarm architecture (developed by rUv) and Claude Code's TeammateTool (discovered in v2.1.19)
A detailed analysis reveals striking architectural similarities between Claude Flow V3's swarm system and Claude Code's TeammateTool. The terminology differs, but the core concepts, data structures, and workflows are nearly identical.
Building high-quality React Native animations requires deep knowledge of animation principles, performance optimization, and React Native Reanimated patterns. While AI assistance is powerful, vibecoding cannot be ignored—having balanced context and workflow can significantly speed up the development process. This repository is designed to be an active AI-friendly environment where developers can create, experiment, and play with animations. Every effort has been made to structure the codebase, provide comprehensive context, and maintain consistent patterns that make it suitable for AI-assisted development. The goal is to create a React Native animation laboratory where developers can explore animation techniques with all related data and context readily available.
Each animation in this repository is wrapped by a unique slug identifier. This slug-based system simplifies detecting all files related to a specific animation, which is a core req