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.
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openlink-license-manager
by OpenLinkSoftwareStart, stop, restart, enable at boot, and check the status of the OpenLink License Manager (oplmgr) daemon on macOS, Linux, and Windows. Detects OS and init system automatically. Use when the user asks to start, stop, restart, enable, disable, or check the status of the OpenLink License Manager or oplmgr.
openlink-license-reader
by OpenLinkSoftwareRead and display OpenLink Software license files (.lic) in a beautified, human-readable format. Parses ASN.1 DER-encoded license files using openssl, extracts all license fields, flags expired licenses visually, and supports single-file or directory-wide scanning. Use when the user asks to read, inspect, check, or list OpenLink license files.
openlink-request-broker-configurator
by OpenLinkSoftwareConfigure and manage the OpenLink Request Broker (oplrqb) rule book (oplrqb.ini). Primary use: configure ODBC Agent rules from host DSNs and JDBC Agent rules from discovered JDBC driver JARs. Also covers general Mapping Rules, DB Agent sections (generic_*), Environment sections, broker global settings, alias sections, reinit/restart, log viewing, and version detection. Two modes: (C) www_sv HTTP Admin Assistant on port 8000 — preferred; (A) direct file edit + CLI — fallback. Use when the user asks to configure ODBC or JDBC agents, add or edit mapping rules, view or edit the broker rule book, restart or reinitialize the broker.
opml-rss-reader
by OpenLinkSoftwareManage, explore, and troubleshoot OPML, RSS, and Atom news feeds using predefined SPARQL/SPASQL queries against OpenLink's linked-data infrastructure. Use this skill whenever the user wants to explore an OPML or RSS/Atom feed URL, retrieve the latest news posts from a feed, diagnose feed processing issues, or configure feed-related settings. Also handles feed auto-discovery when the user provides a plain web page URL — detects feeds via HTML <link rel="alternate"> tags, HTTP Link headers, and common path conventions. Trigger on phrases like "Explore the OPML news source", "Explore the RSS or Atom news source", "Explore the latest edition of", "Discover feeds at", "Find RSS feeds on", or any request referencing OPML/RSS/Atom feed URLs. Full query templates are in references/query-templates.md — load that file before executing any predefined query.
rdf-det-variant-generator
by OpenLinkSoftwareCreate or update a custom DAV DET variant based on RDF Import DET for Virtuoso. Use when the task is to scaffold, modify, or document a DET that ingests RDF documents into the Quad Store, supports one or more Virtuoso-supported RDF document types, implements the required _DAV_* hook family, or needs verification SQL, WebDAV probes, and VAD/source integration notes.
rdf-infographic-skill
by OpenLinkSoftwareGenerate sophisticated, interactive HTML infographics and optional Markdown companion documents from RDF data in any format (Turtle, RDF/XML, N-Triples, JSON-LD). Transform knowledge graphs into visually stunning, data-driven narratives with advanced CSS effects, dynamic interactions, floating navigation, smooth animations, comprehensive metadata, and Markdown variants when requested. Use when converting RDF datasets or SPARQL results into engaging, responsive infographic pages, Markdown companions, marketing assets, documentation, or knowledge exploration artifacts.
rss-feed-generator
by OpenLinkSoftwareGenerate valid RSS 2.0 or Atom 1.0 feeds from web pages that contain post lists but lack a native feed. Triggers on phrases like "generate a feed for", "create an RSS feed from", "make an Atom feed for", "this page has no RSS", or any request to produce a feed URL or feed XML from a blog/news/post-list page. The skill fetches the page, extracts post metadata, and outputs well-formed feed XML plus a self-hostable HTML wrapper with an embedded feed discovery link tag.
uriburner-opal-agent-skills
by OpenLinkSoftwareComprehensive toolkit for URIBurner MCP Server enabling semantic data discovery, Knowledge Graph exploration, SPARQL/SQL query execution, RDF sponging, and database management. Use native MCP tools for queries; ChatPromptComplete only when user explicitly requests Gemini-powered analysis.
virtuoso-support-agent
by OpenLinkSoftwareTechnical support and database management for OpenLink Virtuoso Server with RDF Views generation, SPARQL queries, and comprehensive database operations. Provides assistance with installation, configuration, troubleshooting, RDF data management, SQL/SPARQL/GraphQL queries, automated RDF Views generation from relational database tables, entity discovery, and metadata management using 23 specialized MCP tools.
wikidata-query-skill
by OpenLinkSoftwareTransform natural language questions into SPARQL queries for Wikidata and generate beautiful HTML results pages. Query the Wikidata knowledge base using plain English prompts.
linked-data-skills
by OpenLinkSoftwareGenerates Knowledge Graphs from two source types: (A) relational database objects via Virtuoso RDF Views, or (B) documents/text transformed to RDF using schema.org terms. PATH RDBMS — STRICT 5-step workflow: ask local-vs-DSN, enumerate tables, resolve hostname, confirm IRI patterns, generate TBox+ABox+rewrite rules, verify with entity samples. PATH D — 4-step workflow: collect document + {page_url} + format (JSON-LD or Turtle), generate RDF via prompt template, post- generation review (syntax fix, additional Q&A/entity types), save to user-designated folder. TOOL HIERARCHY: read queries use Demo.demo.execute_spasql_query; writes use EXECUTE_SQL_SCRIPT; RDF generation uses chatPromptComplete.
kg-generator
by OpenLinkSoftwareGenerate comprehensive Knowledge Graphs (RDF-Turtle by default, or JSON-LD and other RDF serializations on request) from content at file: or http(s): scheme URLs. Uses curated prompt templates: a Generic template for general web content (producing JSON-LD), and a Business and Market Analysis template for strategy/analysis content (producing RDF-Turtle with NAICS industry code identifiers, lightweight ontology, FAQ, glossary, and HowTo sections). Trigger when users ask to: generate a knowledge graph, generate RDF or RDF-Turtle, generate JSON-LD, convert a URL to structured semantic data, or extract schema.org data from a page or document.
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.