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...
troubleshoot-amiberry
by BlitterStudioUse when investigating, reproducing, or fixing Amiberry bugs, especially renderer or input regressions, HiDPI or SDL3 logical-presentation bugs, Vulkan capability or swapchain failures, crash regressions, or behavior regressions. Follow an edit-build-run-test-fix cycle, using Amiberry MCP runtime-control tools when they are configured and falling back to normal process, log, screenshot, and debugger tools otherwise.
amiberry-x86-jit
by BlitterStudioReference workflow for Amiberry x86-64 JIT debugging and fixes. Use for x86 or x86-64 JIT allocator failures, RIP-relative `±2GB` distance bugs, PIE or ASLR allocation issues, high-natmem/direct-mode bugs, `PC_P` pointer-width regressions, `MAP_32BIT` or `VirtualAlloc` fallback bugs, runtime JIT disable or interpreter fallback bugs, and regressions in files such as `src/jit/x86/compemu_support_x86.cpp` and `src/jit/x86/compemu_x86.h`.
amiberry-arm64-jit
by BlitterStudioReference workflow for Amiberry ARM64 JIT debugging and fixes. Use for ARM64 JIT crashes, stalls, wrong behavior, performance work, codegen issues, inter-block flag handling, natmem or page-0 guard bugs, FPU JIT problems, and regressions in files such as compemu_support_arm.cpp, compemu_fpp_arm.cpp, compemu_midfunc_arm64.cpp, or gencomp_arm. Covers the known root-cause fixes around liveflags, flush_flags, FBcc and Bcc and BSR sign extension, PC_P 64-bit pointer handling, ADDX and SUBX Z-flag semantics, bus error recovery, R_REGSTRUCT offsets, SysInfo MIPS, Lightwave, and Workbench boot issues. This skill is ARM64-specific; for x86-64 JIT allocator or RIP-relative distance issues use `amiberry-x86-jit`.
amiberry-packaging-install
by BlitterStudioReference workflow for Amiberry packaging, install-layout, and runtime-path fixes. Use for distro packaging changes, `BUNDLE_SDL`, RPATH and shared-library bundling work, DEB or RPM dependency adjustments, CI matrix packaging toggles, Windows portable install detection, and executable-directory vs current-working-directory path bugs in files such as `cmake/linux/install.cmake`, `cmake/windows/install.cmake`, `packaging/CMakeLists.txt`, `.github/workflows/c-cpp.yml`, and `src/osdep/amiberry.cpp`.
amiberry-release
by BlitterStudioWorkflow for preparing and publishing a new Amiberry release. Covers version bumps across CMakeLists.txt, vcpkg.json, metainfo.xml, flatpak manifest, RPM spec; commit/push/tag flow to trigger CI; generating release notes from git log; creating GitHub releases; and drafting social media announcements for Facebook, Mastodon, Bluesky, and Ko-Fi.
winuae-amiberry-merge
by BlitterStudioUse when merging or analyzing upstream WinUAE changes for Amiberry's SDL3/CMake codebase across Linux, macOS, Android, Windows, FreeBSD, Haiku, and iOS work-in-progress targets. Covers porting Win32 GUI dialogs to Dear ImGui, mapping Windows controls to ImGui equivalents, adapting platform-specific code, preserving upstream-sync safety for local Amiberry divergences, handling `#ifdef AMIBERRY` splits, and verifying feature parity.
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.