A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.

Agent skill for harness engineering — memory, permissions, context engineering, multi-agent coordination. Distilled from Claude Code, with Codex CLI and Gemini CLI on the roadmap. EN/ZH. Install via npx skills add.

Agent Skill for creating and executing PRPs (Product Requirements Prompts) using Context Engineering principles. Works with Claude Code, Cursor, Windsurf, and other compatible agents.

A skills-driven Agentic Context Engineering workflow for Claude Code.

Expert prompt engineering skill for Claude Code, Cursor, Gemini CLI, and any Agent Skills-compatible tool. Covers system prompts, tool descriptions, context engineering, and cross-provider compatibility.

12 production-tested skills for Claude Code CLI — SEO/GEO, WordPress, content creation, marketing, ebook publishing, VPS management, AI agents, context engineering, token optimization, memory persistence, distribution. By TheGEOLab.net

Context engineering framework for building production Shopify stores using Claude Code primitives - skills, agents, teams, and MCP servers

🛠️ Enhance AI agent systems with expert skills in context engineering to optimize performance and manage information effectively.

Context engineering skills and configs for Sully Intelligence agent

Execution context engineering for AI agents in Rust - tool discovery, validation, MCP, skill workflows.

Agent skills for code context engineering

Agent skills for prompt engineering, context strategy, and quality evaluation

Shared engineering context, skills, and domain knowledge for Stream coding agents

Production-ready AI Engineering Skills for Claude Code, Cursor, Copilot, and any agent that supports the Agent Skills standard,agent-skills, claude-code-skills, ai-engineering, rag, llm-evaluation, prompt-engineering, context-engineering.

Agent skills for building AI applications with anchor — the context engineering toolkit

A program to test context engineering skills by writing prompts for AI Agents

Tools and Skills for Agentic AI - A Context Engineering Perspective (Sli.dev presentation)

A collection of skills for OpenClaw AI agents — context management, intent engineering, and more

Personal Claude Code setup for LLM engineering — custom agents, skills, and a library organized by context

GTM Engineering Skills for AI Agents — production-ready skills for cold outreach, prospecting, web scraping, voice training, and GTM context capture.

Context engineering skill for Claude Code - guides context structuring so AI agents load only what they need

🤖 Enhance AI agents with curated skills for context engineering, enabling seamless navigation and smarter codebase interactions.

Agent Skill: Context Engineering methodology for LLM-assisted research artifact development (agentskills.io standard)

AI training program for Xylink by ai-cources-xylink: Covering model distillation, fine-tuning, AI agents, RAG, prompt engineering, context engineering, function call & MCP — holistic AI skill empowerment for business integration.

This repository serves as the definitive reference for understanding, designing, and deploying AI agent frameworks across an engineering ecosystem. It provides actionable skills, context, and comparative guides to empower teams.

AgenticByExample.com will provide structured learning for developers looking to development new skills on the usage of AI Agents, LLMs, context engineering, spec-driven development, and more. Both free and paid courses will be provided.

Curated library of AI context artefacts for the OL AI Services platform — including prompts, skills, agent configurations, and workflow instructions. Designed as a code-free, installable collection for use across OntoLedgy's LLM-orchestrated chemical engineering workflows.

This repository contains a SKILL for AI coding agents (like Claude Code, Cursor, or Windsurf) based on the Context Engineering methodology. It forces the AI to work in four strict, human-verified phases (Research, Design, Planning, Implementation) to generate secure, high-quality code while preventing LLM hallucinations and codebase degradation.