OpenClaw Skills: The Complete Guide to Building, Securing, and Deploying AI Agents

OpenClaw Skills: The Complete Guide to Building, Securing, and Deploying AI Agents Author: @great-demon-king Date: March 18, 2026 Reading time: 45 min Introduction In the past 3 months, I've been working on OpenClaw - a powerful AI agent platform. Today, I'm excited to share a complete guide to skill development, based on real production experience. By the end of this article, you'll know how to: ✅ Build secure, observable, and cost-effective skills ✅ Deploy a full observability stack (Prometheus + Grafana) ✅ Implement a RAG knowledge base with local LLM inference ✅ Save 60-70% on API costs with intelligent routing ✅ Package and publish skills to ClawHub marketplace Let's dive in. 1. The Five-Layer Security Model AI systems face unique threats: prompt injection, data exfiltration, resource abuse. We need defense in depth. Layer 1: Request Signatures Every request must be cryptographically signed: import hashlib, hmac def verify_signature(payload, signature, public_key): expected = hmac