Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
Querying local SQLite index...
game-engine-resources
by gmh5225Guide for game-engine internals, source trees, plugins, and engine-specific security research. Use this skill when researching Unreal, Unity, Source, Godot, custom engines, engine detectors, engine explorers, or engine protection patterns relevant to modding, reverse engineering, and anti-cheat.
game-hacking-techniques
by gmh5225Guide for game-hacking technique taxonomy and threat modeling relevant to game security. Use this skill when researching memory access, code injection, overlays, input simulation, engine-specific attack surfaces, or how modern anti-cheat systems constrain user-mode, kernel-mode, hypervisor, and DMA-based cheat implementations.
awesome-game-security-overview
by gmh5225Guide for understanding and contributing to the awesome-game-security curated resource list. Use this skill when adding new resources, organizing categories, mapping topics across anti-cheat, Windows kernel, DMA, reverse engineering, and game-engine research, or maintaining README.md format consistency.
windows-kernel-security
by gmh5225Guide for Windows kernel internals and security mechanisms used in game protection and low-level research. Use this skill when working with drivers, IRQL-sensitive callbacks, EPROCESS, ETHREAD, MMVAD internals, IOCTL paths, DSE, PatchGuard, HVCI, PiDDBCache, MmUnloadedDrivers, or kernel memory inspection.
reverse-engineering-tools
by gmh5225Guide for reverse engineering protected games and anti-cheat components across user mode, kernel mode, and hypervisor-aware environments. Use this skill when analyzing drivers, IOCTL protocols, callback registration, injected-code artifacts, integrity checks, protected binaries, or debugging security-sensitive game components.
anti-cheat-systems
by gmh5225Guide for modern game anti-cheat architecture, Windows kernel monitoring, and detection tradeoffs. Use this skill when analyzing EAC, BattlEye, Vanguard, FACEIT AC, kernel callbacks, handle protection, manual-map detection, boot-start drivers, BYOVD, DMA threats, or behavioral telemetry in game security research.
dma-attack-techniques
by gmh5225Guide for PCIe DMA threat modeling, FPGA-based memory access, and defensive implications in game security. Use this skill when researching pcileech, BAR and TLP behavior, page-table walking, IOMMU or VT-d, device impersonation, firmware mimicry, or DMA detection and mitigation in game security research.
mobile-security
by gmh5225Guide for Android and iOS game security, reversing, and anti-cheat-adjacent platform research. Use this skill when working with APK or IPA analysis, IL2CPP mobile titles, Frida, Zygisk or Magisk, jailbreak or root detection bypass, Android kernel modules, emulator detection, or mobile anti-cheat systems.
graphics-api-hooking
by gmh5225Guide for graphics API interception, overlay rendering, and render-pipeline analysis across DirectX, OpenGL, and Vulkan. Use this skill when working with Present or SwapBuffers hooks, DXGI swap chains, shader or draw-call interception, screenshot-sensitive overlays, or graphics debugging in game security research.
llvm-security
by gmh5225Expertise in LLVM security features including sanitizers, hardening techniques, exploit mitigations, and secure compilation. Use this skill when implementing security-focused compiler features, analyzing vulnerabilities, or hardening applications.
llvm-learning
by gmh5225Comprehensive learning resources and tutorials for LLVM, Clang, and compiler development. Use this skill when helping users learn LLVM internals, find educational resources, or understand compiler concepts.
llvm-obfuscation
by gmh5225Expertise in LLVM-based code obfuscation techniques including OLLVM, control flow flattening, string encryption, virtualization, and anti-analysis methods. Use this skill when working on code protection, anti-reverse engineering, or implementing custom obfuscation passes.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.