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...
1panel-app-builder
by arch3rProUse when packaging Docker deployments as 1Panel local app store apps, including GitHub projects, docker-compose.yml files, docker run commands, app metadata, version directories, icons, README files, and 1Panel validation.
manage-skills
by arch3rProManage the user's Skills-Manager-Plus skill library through smp: install, update, remove, enable or disable, sync, search, adopt local skills, manage presets, inspect workspaces, and operate plugin, ClawHub, Git backup, WebDAV, and data backup workflows. Use this whenever the user wants to install, find, update, remove, list, sync, import, adopt, or back up skills through Skills-Manager-Plus instead of writing directly into one agent's skills directory.
public-trend-radar
by arch3rProUse when an agent needs public-channel trend discovery, trend cards, AI/technical trend signals, product/business trend signals, or open public sources as upstream context.
rss-ai-digest
by arch3rProUse when an agent needs quick RSS or Atom subscription digests, daily news, key information, OPML import, new AI or technical article discovery, keyword/date/author/category/language filtering, scoring, or seen-item dedupe.
rss-source-curator
by arch3rProUse when an agent needs to evaluate RSS source quality, review feed health, generate source curation actions, apply reviewed registry patches, or maintain RSS/OPML source registries.
subscription-research-agent
by arch3rProUse when an agent needs deep research from subscription sources, local research workspaces, archived evidence, evidence briefs, source-backed research memos, hypothesis tracking, or multi-step synthesis.
obsidian-kanban
by arch3rProCreate and maintain Obsidian Kanban boards stored in Markdown, with a GTD-friendly workflow for capturing tasks, adding cards, organizing lists, and maintaining task details such as inline tags, Kanban date and time tokens, linked-note priority metadata, and subtasks. Use when working with Obsidian Kanban board files, when the user asks to add or manage cards, or when a lightweight task card should be upgraded into a linked note for more complex project tracking.
obsidian-markdown
by arch3rProCreate and edit Obsidian Flavored Markdown with wikilinks, embeds, callouts, properties, and other Obsidian-specific syntax. Use when working with .md files in Obsidian, or when the user mentions wikilinks, callouts, frontmatter, tags, embeds, or Obsidian notes.
skill-manager
by arch3rProManage ArkSpace by creating skills, recording upstream sources, assigning roles, validating registries, and guiding mirror, adapted, local, and reference-only skill updates.
provider-manager
by arch3rProUse when configuring ArkSpace providers, fixing missing provider URLs or API keys, checking provider readiness, or setting up multiple API key rotation.
searxng-search
by arch3rProUse when querying a configured self-hosted SearXNG instance, the SearXNG Search API, or privacy-oriented metasearch through ArkSpace web_search routing.
tavily-extract
by arch3rProUse when extracting clean content from URLs through Tavily, especially JavaScript-rendered pages, multiple URLs, query-focused extraction, or ArkSpace web_fetch routing that selects Tavily.
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.