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...
xip-writing-reference
by ShareXReference for writing effective XerahS Improvement Proposals (XIPs), including structure, templates, review checks, and implementation patterns. Use sync-xips for creating, editing, and syncing XIP GitHub issues and local backups.
avalonia-api
by ShareXComprehensive reference for Avalonia UI framework including XAML syntax, controls, data binding, MVVM patterns, styling, custom controls, layout system, responsive layout, navigation, and best practices. Covers CommunityToolkit.Mvvm integration, compiled bindings, dependency properties, attached properties, control templates, container queries, and cross-platform development patterns.
draft-blog-post
by ShareXMaintain XerahS daily development blog drafts under docs/blog using the YYYY/YYYY-MM/blog-YYYYMMDD.md layout. Use when asked to create, update, or consolidate the current UTC+8 blog post from new feature work, bug fixes, build/tooling changes, or recent git history.
port-imageeditor
by ShareXUse the local ShareX checkout as the source of truth for ShareX.ImageEditor, find the latest upstream commit that touches it, and port or sync the matching changes into the XerahS ShareX.ImageEditor submodule with path-aware diffing and build gates.
publish-release
by ShareXOrchestrate XerahS release flow in strict order: run maintenance prep first, update-changelog second (optional only if docs/CHANGELOG.md is intentionally absent), verify build, bump/commit/push/tag while syncing Chocolatey version metadata, monitor GitHub Actions every 2 minutes, ensure standard release notes content, then set pre-release by default (use explicit opt-out for stable). On failures, inspect logs, fix root cause, and retry with the next patch release.
run-maintenance
by ShareXRepository maintenance preparation for XerahS. Use before release or changelog work to sync repositories, inspect submodule state, identify version/changelog needs, and hand off commit/push/version rules to git-workflow.
sync-xips
by ShareXCreate and maintain XerahS Improvement Proposals (XIPs) with GitHub as source of truth and docs/proposals/xip folder as backup. Use when creating or editing XIPs, syncing XIPs between GitHub issues and the docs/proposals/xip folder, or when the user mentions XIP, GitHub issues for XIP, or local XIP files.
update-changelog
by ShareXRules and workflows for updating docs/CHANGELOG.md, including version grouping, aggressive consolidation, and GitHub tag-linked release headings.
sharex-architecture-and-porting
by ShareXPlatform abstraction rules, porting guidelines, and architecture standards for XerahS
refactoring-audit-workflow
by ShareXA systematic workflow for identifying code pain points, planning refactoring, and creating GitHub issues.
build-android
by ShareXBuild and deploy XerahS Android apps (Kotlin/Mobile.Kt, Avalonia, MAUI) to emulator/device via adb. Covers JAVA_HOME for Gradle, Compose KeyboardOptions/foundation, file locks, EmbedAssembliesIntoApk, init white-screen, single-node builds (-m:1), and cold-boot emulator. Never wait more than 5 minutes for a build—if it exceeds that, treat as failure and fix locks or parallelism.
build-common
by ShareXShared XerahS build guardrails for all platforms. Use with platform build skills when handling timeouts, stale dotnet processes, file locks, single-node MSBuild, build-server shutdown, and centrally managed dependency versions.
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.