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.
| // --------------------------------------------------------------------------- | |
| // WB API → Google Sheets — ознакомительная интеграция | |
| // Эндпоинт: GET /api/v5/supplier/reportDetailByPeriod | |
| // Хост: https://statistics-api.wildberries.ru | |
| // --------------------------------------------------------------------------- | |
| /** Добавляет меню «WB API» при открытии таблицы */ | |
| function onOpen() { | |
| SpreadsheetApp.getUi() | |
| .createMenu('WB API') |
| <goal> | |
| - help build out a more refined gameplan on integration given what has already been done so far and address pressing questions that have come up during the integration | |
| - a secondary goal is a proposal on how we implement caching of our results to run evaluation pipelines through - a very important component if we want to be able to grade our work | |
| </goal> | |
| <resources> | |
| /Users/phillipgoldberg/clearml-utils/.worktrees/GENAI-777-clearml-utils-package-creation/pdx_clearml_package_gameplan.md - this is the document ive been using as reference for building out this migration over | |
| /Users/phillipgoldberg/iodine-softstart/.worktrees/migration - this worktree contains the migration branch, where we will take this specific pipeline outlined in the gameplan.md |
Similar to the setup of Squid for HTTP proxy: https://gist.github.com/gmeeker/5e9af73fcf213c67cd86794f506520e6
OpenWRT can be used for SOCKS proxy as well using Dante. No custom builds are required, but some configuration files are currently missing.
opkg install sockd
22 copy/paste-ready prompts for building your own AI agent system. Each prompt builds a functional system or implements a proven best practice you can hand to an AI coding assistant.
Replace placeholders like <your-workspace>, <your-messaging-platform>, and <your-model> with your own values.
| 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.
I wrote an in-depth research prompt to conduct a GPT-Deep-Research on the Manus topic, seeking to replicate it with currently available open source tools. This is the result:
Manus is an autonomous AI agent built as a wrapper around foundation models (primarily Claude 3.5/3.7 and Alibaba's Qwen). It operates in a cloud-based virtual computing environment with full access to tools like web browsers, shell commands, and code execution. The system's key innovation is using executable Python code as its action mechanism ("CodeAct" approach), allowing it to perform complex operations autonomously. The architecture consists of an iterative agent loop (analyze → plan → execute → observe), with specialized modules for planning, knowledge retrieval, and memory management. Manus uses file-based memory to track progress and store information across operations. The system can be replicated using open-source components including CodeActAgent (a fine-tuned Mistral model), Docker for sandbox