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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lux
by ariana-dot-devControl the desktop using AI vision (computer-use). Use for GUI automation, clicking buttons, typing in applications, and interacting with desktop software.
ariana
by ariana-dot-devManage other Ariana agents and configure your own environment/automations. Use for spawning agents, sending tasks, checking status, and setting up automations.
parallel-agents
by ariana-dot-devBuild an "agent factory" — configure one cloud VM once, then disk-level fork it into a fleet of identical isolated machines so many agents run in parallel, each with its own filesystem, IPv4, and SSH. Flat per-second pricing makes a fleet ~10x cheaper than E2B/Modal (50 VMs ≈ $432/mo vs ~$4k). Use to fan work across many real machines instead of threads.
cloud-sandbox
by ariana-dot-devSpin up an isolated cloud sandbox — a full root Ubuntu VM (4 vCPU / 8 GB / 80 GB), not a thin isolate or container — to run code, builds, installs, or shell commands off the user's machine. Billed per second, ~10x cheaper than E2B/Modal; Docker and every major toolchain preinstalled. Use whenever work should not touch the host filesystem.
agent-sandbox
by ariana-dot-devGive an AI agent its own sandboxed cloud machine to work in — a full root Ubuntu VM (SSH, Docker, IPv4, all toolchains) where the agent can edit code, run commands, and host services without touching the user's host. Claude Code and Codex harnesses are preinstalled. Per-second billing, ~10x cheaper than E2B/Modal. Use as the execution environment for an autonomous agent.
code-execution
by ariana-dot-devExecute code in a real cloud machine (code-interpreter style) — a full root Ubuntu VM with Python, Node, Go, Rust and every major runtime preinstalled, not a constrained isolate. Per-second billing makes a run cost cents. Use to run scripts, data jobs, or generated code that needs real packages, subprocesses, or Docker.
run-untrusted-code
by ariana-dot-devRun AI-generated or untrusted code safely in a disposable cloud VM instead of on the host, then delete it. Full root Ubuntu with zero blast radius on the user's machine; per-second billing means a throwaway run costs cents. Use whenever you'd execute code you don't fully trust.
cloud-vm
by ariana-dot-devProvision a persistent cloud Linux VM in seconds with SSH and a dedicated IPv4 — a real Ubuntu machine (4 vCPU / 8 GB / 80 GB) you can keep, resume, and snapshot. $20 buys ~555 hours, billed per second, ~10x cheaper than typical sandbox providers. Use when the user wants a remote dev box, a cloud VM, or any always-available Linux machine.
expose-localhost-tunnel
by ariana-dot-devTunnel/expose a localhost service to the internet from inside a cloud VM — get a stable public HTTPS URL (https://<box>-<port>.on.ascii.dev) with TLS and token-gating built in, no separate daemon or account like ngrok. The VM's dedicated IPv4 also allows raw TCP/UDP and bring-your-own domain. Use to share a preview, expose a webhook target, or open a port.
public-url-hosting
by ariana-dot-devHost a service on a stable public HTTPS URL straight from a cloud VM — start your app, run one command, get https://<box>-<port>.on.ascii.dev with managed TLS and token-gating. Supports bring-your-own domain and raw TCP/UDP via a dedicated IPv4. Use to host a web app, API, demo, or webhook endpoint without DNS or certificate setup.
linux-server-ssh
by ariana-dot-devGet a real Linux server in the cloud with full SSH/SCP access and a dedicated IPv4 in seconds — root Ubuntu (4 vCPU / 8 GB / 80 GB), Docker and every major toolchain preinstalled. Billed per second, ~10x cheaper than typical providers. Use when the user needs a remote Linux server to SSH into, run services on, or use as a dev machine.
ephemeral-ci-environment
by ariana-dot-devGet a clean, reproducible ephemeral environment for CI and testing — a fresh root Ubuntu VM with every major toolchain and Docker preinstalled, your GitHub repo auto-cloned in, and the whole thing thrown away after. Per-second billing makes each run cost cents. Use to run tests, reproduce a bug, or validate a build in a pristine environment without polluting the host.
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.