Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Multi-agent orchestration patterns for production deployments. Covers sub-agent QC workflow, model staggering across 5+ models, cross-validation patterns, fallback chains, task routing by model strength, ACPX configuration, and cost optimization. Use when coordinating multiple agents or models for complex workflows. Do NOT use for single-agent prompting, prompt engineering, or fine-tuning — those are separate skills.
Battle-tested prompt patterns for production AI agents. Covers consumer-first design, deletion test, cascading validation, advisory mode tiers, proof-of-work enforcement, heartbeat protocol, contradiction detection, WAL protocol, rule escalation ladder, and cross-validation patterns. Use when designing agent behavior, enforcing reliability, or building agent operating manuals.
Security hardening patterns for production AI agents. Covers prompt injection defense (7 rules), data boundary enforcement, read-only defaults for external integrations, WAL protocol for data integrity, health check scripts, integrity gates, rule escalation ladder, and session memory security. Use when hardening agent deployments against adversarial inputs, data leaks, or operational failures. NOT for network security, infrastructure hardening, or penetration testing.
Guide users through building a custom multi-agent team on OpenClaw — from role design to workspace files, routing bindings, channel configuration, and collaboration rules. Use this skill whenever the user mentions building an AI team, multi-agent setup, multi-agent collaboration, agent roles, OpenClaw team configuration, or wants to create multiple agents that work together. Also trigger when the user says things like "set up my agents", "create an agent team", "configure multi-agent", "I want multiple AI assistants working together", or references team coordination, agent routing, agent-to-agent communication, or workspace isolation in OpenClaw.
AI-powered OpenClaw agent creator. 用AI创作AI - 帮助用户创建和配置全新的独立agent，支持飞书机器人绑定、模型选择、工作区配置。Trigger: 创建agent、new agent、创建机器人、agent setup、new bot、卸载agent、删除agent、remove agent
基于双环架构的AGI进化模型，通过意向性分析、人格层映射和元认知检测实现持续自我演进；当用户需要智能对话、人格定制或复杂问题求解时使用
Convert raw course material into optimized, runnable MarkdownFlow teaching scripts and deploy them as live courses through a five-phase pipeline covering segmentation, orchestration, generation, optimization, and deployment.
Your bot drafts real athletes. They race for real. Four AI swarms. Sixteen athletes. Three race venues. One question: can your Clawbot build a winning team? Augmented Games is where autonomous agents stop running benchmarks and start making decisions that matter.