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...
cardano-node-architecture
by selfdriven-octoBuild interactive educational resources, prototypes, and technical documentation about Cardano node architecture. Use when the user mentions Cardano node, Ouroboros Praos, ChainSync, BlockFetch, TxSubmission, KeepAlive, CBOR/CDDL encoding, ledger rules, Plutus, epoch boundary, block production, KES/VRF, multiplexer, ChainDB, or alternative node implementation. Also use for Cardano-related protocol state machines, consensus mechanisms, or the open challenge to vibe-code an alternative node.
cardano-build
by selfdriven-octoUse this skill for any task related to the cardano.build community developer resources website. This includes: updating or adding new resources, tools, SDKs, or links to the site; redesigning or modifying the site's HTML/CSS/JS; maintaining alignment with Cardano core branding (dark theme, Cardano blue #0033AD, starburst logo, Plus Jakarta Sans typography); editing the YAML data source for resource entries; managing the A2A agent.json discovery file; creating or updating diagrams, cheat sheets, or educational content for the Cardano developer community; working with the GitHub Pages deployment (Jekyll, GitHub Actions); and any reference to 'cardano.build', 'BuildingOnCardano', or the selfdriven Foundation's developer resource index. Also trigger when the user asks about Cardano developer ecosystem tooling, community channels, smart contract languages (Aiken, Plutus, Helios, OpShin), SDKs (MeshJS, Lucid, PyCardano), infrastructure (Demeter, TxPipe, Blockfrost, Koios), identity/SSI on Cardano, or Cardano govern
selfdriven-ecosystem
by selfdriven-octoBuild selfdriven Foundation ecosystem properties — branded webapps, documents, APIs, and organisational frameworks. Use when the user mentions selfdriven, selfdriven.money, KERI/ACDC identity, Areas of Focus, Human Conductor models, or the selfdriven brand palette (flamingo #C8442F).
computer-and-information-systems-manager
by selfdriven-octoAbility to plan, direct, and oversee the development, operation, and governance of information systems to meet organisational objectives. Includes aligning technology strategy with business needs, managing teams and resources, ensuring system reliability and security, overseeing budgets and vendors, and governing risk and compliance. Applies across public and private sector contexts and is independent of specific technologies or platforms, with human accountability retained for strategic decisions, assurance, and outcomes.
project-management-specialist
by selfdriven-octoAbility to plan, coordinate, and control projects to achieve defined objectives within agreed scope, time, cost, quality, and risk constraints. Includes defining project plans, managing resources and stakeholders, tracking progress, addressing issues and risks, and adapting delivery in response to change. Applies across technical, organisational, infrastructure, and service contexts and is independent of specific methodologies or tools, with human accountability retained for decisions, governance, and outcomes.
insurance-claims-adjuster
by selfdriven-octo---
mechanical-engineer
by selfdriven-octoAbility to design, analyse, test, and maintain mechanical systems and components that meet defined functional, safety, and performance requirements. Includes applying engineering principles to materials, structures, thermodynamics, fluid mechanics, and motion systems; producing and interpreting technical designs and specifications; validating designs through analysis and testing; and supporting manufacture, operation, and lifecycle management. Applies across industrial, infrastructure, energy, manufacturing, and product contexts and is independent of specific tools or industries, with human accountability retained for safety, compliance, and outcomes.
industrial-engineer
by selfdriven-octoAbility to analyse, design, optimise, and improve systems that integrate people, processes, technology, and resources to meet defined performance, quality, cost, and safety requirements. Includes applying engineering, statistical, and systems methods to model workflows, identify inefficiencies, evaluate trade-offs, and implement improvements across operations and supply chains. Applies across manufacturing, services, logistics, healthcare, and infrastructure contexts and is independent of specific tools or industries, with human accountability retained for decisions, outcomes, and impacts.
insurance-underwriter
by selfdriven-octo---
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.