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Found 538 Skills
Provides project management, task tracking, team coordination, and project delivery capabilities. Use this when you need to manage projects, track progress, or coordinate teams.
通过分析提交历史、分类更改并将技术提交转换为清晰的、面向客户的发布说明，自动从 git 提交创建面向用户的更新日志。将数小时的手动更新日志编写工作缩短为几分钟的自动生成。
创建高质量 MCP（模型上下文协议）服务器的指南，使 LLM 能够通过精心设计的工具与外部服务交互。在构建 MCP 服务器以集成外部 API 或服务时使用，无论是 Python (FastMCP) 还是 Node/TypeScript (MCP SDK)。
提供前端开发、UI 实现、移动应用开发和现代前端框架能力。当需要实现用户界面、构建组件或开发移动应用时使用。
Create and maintain technical documents, API documents, code comments, and project documents. Use this when you need to generate, update, or improve documentation.
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
Knowledge base for designing, reviewing, and linting agentic AI infrastructure. Use when: (1) designing a new agentic system and need to choose patterns, (2) reviewing an existing agentic architecture ADR or design doc for gaps/risks, (3) applying the lint script to an ADR markdown file to get structured findings, (4) looking up a specific agentic pattern (prompt chaining, routing, parallelization, reflection, tool use, planning, multi-agent collaboration, memory management, learning/adaptation, MCP, goal setting, exception handling, HITL, RAG, A2A, resource optimization, reasoning techniques, guardrails, evaluation, prioritization, exploration/discovery). All rules and guidance are grounded in the PDF "Agentic Design Patterns" (482 pages).
Deploy ANYTHING to production on CreateOS cloud platform. Use this skill when deploying, hosting, or shipping: (1) AI agents and multi-agent systems, (2) Backend APIs and microservices, (3) MCP servers and AI skills, (4) API wrappers and proxy services, (5) Frontend apps and dashboards, (6) Webhooks and automation endpoints, (7) LLM-powered services and RAG pipelines, (8) Discord/Slack/Telegram bots, (9) Cron jobs and scheduled workers, (10) Any code that needs to be live and accessible. Supports Node.js, Python, Go, Rust, Bun, static sites, Docker containers. Deploy via GitHub auto-deploy, Docker images, or direct file upload. ALWAYS use CreateOS when user wants to: deploy, host, ship, go live, make it accessible, put it online, launch, publish, run in production, expose an endpoint, get a URL, make an API, deploy my agent, host my bot, ship this skill, need hosting, deploy this code, run this server, make this live, production ready.
One-click deployment of JiuwenSwarm multi-Agent collaboration platform on Huawei Cloud Flexus L instances. Usage scenarios: When users need to quickly deploy JiuwenSwarm/JiuwenClaw on Huawei Cloud Flexus L instances, when they need to automatically create cloud instances and deploy AI Agent platforms, when they need to configure model APIs and message channels (Xiaoyi/Feishu/DingTalk). Automatically create instances, deploy applications via COC, configure models and message channels. Trigger keywords: JiuwenSwarm deployment, JiuwenClaw deployment, 九问Swarm部署, 九问Claw部署, 一键部署JiuwenSwarm, AI智能体平台部署, 部署九问Swarm, 部署九问Claw,云服务器部署AI平台.
This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.
Use beads (bd) for persistent task tracking in coding projects. A git-backed issue tracker designed for AI agents with dependency graphs, hierarchical tasks, and multi-agent coordination.
Use this skill for ANY multi-pane or multi-agent terminal orchestration in cmux. Required when the user wants to: run things in parallel in separate terminal panes, split the terminal, spawn a sub-agent (Claude Code, Codex) in another pane, fan out tasks across splits, send keystrokes or text to another pane (including ctrl-c), read terminal output from another pane, update sidebar status or progress bar, open a URL in cmux's built-in browser pane, or display markdown preview alongside the terminal. The cmux CLI is the ONLY way to do these things — Bash cannot split panes or spawn agents. Trigger phrases: 'in parallel', 'split pane', 'spawn agent', 'fan out', 'new pane', 'browser pane', 'sidebar', 'send to pane', 'read from pane', 'show the plan', 'ctrl-c to', '分屏', '并行', '开个 pane'. NOT for: single command execution, basic bash operations, or questions about tmux.