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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accessibility
by elasticAccessibility guidance for Kibana. Use this skill when working with or reviewing EUI components, resolving a11y-related (@elastic/eui) ESLint issues, and ensuring proper use of ARIA attributes, focus management, keyboard interactions, and accessible naming conventions.
api-authz
by elasticKibana API route authorization patterns. Use when configuring route security, working with requiredPrivileges, using authzResult for privilege-based branching, opting out of authorization, or naming custom privileges.
activate-connector
by elasticCreates a connector instance in a running Kibana. Use when asked to activate, connect, enable, or instantiate a connector in Kibana.
branch-readiness-checks
by elasticValidate branch readiness before push or PR using base-diff and local-change checks.
catalog-ecommerce
by elasticGuide for building catalog and e-commerce search with Elasticsearch. Use when a developer wants product search, faceted navigation, autocomplete, "did you mean" suggestions, or shopping-oriented search experiences.
cypress-to-scout-migration
by elasticMigrate Kibana Cypress E2E tests (.cy.ts) to Scout (Playwright). Applies to any Kibana plugin or solution. Includes triage gates (duplicate detection, layer analysis, value assessment), Cypress-to-Scout pattern mapping, data cleanup audit, and PR workflow. Use when: (1) migrating a Cypress test to Scout, (2) converting .cy.ts to .spec.ts, (3) planning a Cypress-to-Scout migration batch, (4) rewriting Cypress screens/tasks as Scout page objects, (5) asked "how do I move this Cypress test to Scout/Playwright", (6) asked about differences between Cypress and Scout.
encrypted-saved-objects
by elasticEncrypted Saved Objects (ESO) in Kibana — registration, AAD attribute choices, partial update safety, model version migrations with createModelVersion, canEncrypt checks, and Serverless constraints. Use when creating, modifying, or working with ESO types.
evals-create-suite
by elasticScaffold a new LLM evaluation suite package with Playwright config, evaluate fixture, and package files. Use when creating a new eval suite, adding an evals package for a plugin, or setting up the boilerplate for offline LLM evaluations.
enzyme-to-rtl
by elasticMigrate Enzyme tests to React Testing Library (RTL). Use when converting shallow/mount enzyme tests to RTL render, replacing enzyme selectors with RTL queries, updating snapshot tests, or when the user mentions enzyme migration, RTL migration, or react-testing-library.
perform-agent-builder-eval
by elasticOrchestrate agent-builder evaluation runs — init ES/Kibana/EDOT stack, collect eval parameters, output the run command, and stop services.
elasticsearch-tutorial
by elasticTopic-driven, hands-on Elasticsearch tutorial flow that runs in Kibana Dev Console. Use whenever the user says "walk me through", "give me a tutorial for", "teach me", "show me how X works", "tutorial on", or similar topical learning intent — and they are NOT asking you to build their real, specific use case. Topics are open-ended: any Elasticsearch / Kibana search concept the user names (e.g. mappings, analyzers, bool queries, semantic_text, kNN, RRF, aggregations, ingest pipelines, reranking, data streams, ES|QL). Tutorials use sample data on isolated resources, present every step as a SENSE snippet to run in Dev Tools, and end with cleanup plus pointers to docs and the onboarding / pattern skills.
elasticsearch-onboarding
by elasticPrimary guided playbook for Elasticsearch search in Kibana Agent Builder: intent → data → mapping → Dev Tools API snippets (SENSE), with one question at a time. Load this skill whenever the user wants to learn Elasticsearch search, get started, begin building, take first steps, onboard, follow a walkthrough or tutorial, go from zero to a working query, or get structured help setting up indices and search — including casual openers like hi, help, getting started, new to Elasticsearch, how do I build search, or I want to try search. Use when they need end-to-end onboarding, not a single narrow API answer. If they only ask what they can build with Elastic (exploration without the full playbook), prefer invoking /use-case-library first; you can still load this skill afterward for the guided build.
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.