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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agent-browser
by kortix-aiBrowser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
dotenvx-secrets
by kortix-aiHow this repo manages API secrets and the three local-dev environments (local/dev/prod). They are dotenvx-ENCRYPTED in git and the keys live in Dotenv Armor. Load this WHENEVER you touch a secret, API key, token, credential, or any apps/api/.env* file; whenever the user pastes a key/token/secret to store or use; whenever choosing/switching which environment to run (local vs dev vs prod); and whenever adding, reading, rotating, or sharing a secret.
document-review
by kortix-aiUse for structured review, fact-checking, annotation, and correction of documents such as PDF, DOCX, PPTX, and XLSX files.
domain-research
by kortix-aiFree domain research and availability checking. No API keys or credentials required. Uses RDAP (1195+ TLDs) with whois CLI fallback for universal coverage. Checks if domains are available, searches keywords across TLDs, performs WHOIS/RDAP lookups, checks expiry dates, and finds nameservers. Use when the agent needs to: check if a domain is available, search for domains, find who owns a domain, check domain expiration, get nameservers, bulk check domains, or do any domain research. Triggers on: 'check domain', 'is domain available', 'search domains', 'domain availability', 'who owns this domain', 'whois', 'domain expiry', 'when does domain expire', 'nameservers for', 'domain research', 'find domains for', 'domain ideas', 'bulk domain check'.
deep-research
by kortix-aiDeep research agent skill. Use when the user needs thorough, scientific, truth-seeking research on any topic -- investigating claims, finding primary sources, synthesizing evidence, producing cited reports. Triggers on: 'research this', 'investigate', 'deep dive', 'find sources', 'what does the evidence say', 'literature review', 'fact check', 'analyze the research on', any request requiring multi-source investigation with citations.
elevenlabs
by kortix-aiElevenLabs audio generation — text-to-speech, voice cloning, and sound effects. Use this skill any time the agent needs to: convert text to spoken audio, narrate documents or content, generate voiceovers, clone voices from audio samples, create sound effects, or produce any audio output from text. Supports multiple voices, languages, models, voice cloning, batch processing, and sound effect generation. Requires ELEVENLABS_API_KEY.
kortix-system
by kortix-aiCanonical reference for a Kortix project: the platform model (repo-native projects, sessions on ephemeral branches, the strict boundary between `kortix.toml` and OpenCode config under `.kortix/opencode/`); the full `kortix.toml` manifest (keys, trigger fields, secrets contract, `[[apps]]` deploy surface); the complete `kortix` CLI (commands, flags, the project-scoped token model, the in-sandbox `KORTIX_TOKEN`); the change-request (CR) system for landing session work on `main` (an agent MUST open a CR to merge); the session sandbox runtime (which supports Docker and Docker-in-Docker); and the OpenCode runtime (agents, skills, commands, tools, plugins, MCP servers, permissions, AGENTS.md rules, models). Load whenever the user asks how Kortix works, about `kortix.toml`, the `kortix` CLI, anything under `.kortix/opencode/`, how to merge/ship/land work on `main`, change requests/CRs/PRs, or to author/edit any OpenCode primitive.
kortix-memory
by kortix-aiHow to read, write, and curate project memory in `.kortix/memory/` — the project brain. Load this skill whenever you (or the memory-reflector agent) need to add, update, or reorganize what this project knows about itself. Defines the rubric for what belongs in memory, the file structure, and the change-request flow for landing memory edits on `main`.
kortix-system
by kortix-aiCanonical reference for a Kortix project: the platform model (repo-native projects, sessions on ephemeral branches, the strict boundary between `kortix.toml` and OpenCode config under `.kortix/opencode/`); the full `kortix.toml` manifest (keys, trigger fields, secrets contract, `[[apps]]` deploy surface); the complete `kortix` CLI (commands, flags, the project-scoped token model, the in-sandbox `KORTIX_TOKEN`); the change-request (CR) system for landing session work on `main` (an agent MUST open a CR to merge); the session sandbox runtime (which supports Docker and Docker-in-Docker); and the OpenCode runtime (agents, skills, commands, tools, plugins, MCP servers, permissions, AGENTS.md rules, models). Load whenever the user asks how Kortix works, about `kortix.toml`, the `kortix` CLI, anything under `.kortix/opencode/`, how to merge/ship/land work on `main`, change requests/CRs/PRs, or to author/edit any OpenCode primitive.
kortix-executor
by kortix-aiHow to reach third-party systems from a Kortix session via the Executor — one interface to every configured integration (Pipedream, MCP, OpenAPI, GraphQL, HTTP), exposed as the `kortix-executor` MCP server's tools (connectors, discover, describe, call). Load whenever the user asks the agent to DO something in an external app/API (send an email, create a Stripe charge, post to Slack, query an internal API, call any SaaS), asks "what integrations/connectors/tools do I have", asks to add/configure a connector, or asks about `[[connectors]]` in kortix.toml. The agent must use the Executor's MCP tools rather than hand-rolling API calls with raw tokens.
kortix-memory
by kortix-aiHow to read, write, and curate project memory in `.kortix/memory/` — the project brain. Load this skill whenever you (or the memory-reflector agent) need to add, update, or reorganize what this project knows about itself. Defines the rubric for what belongs in memory, the file structure, and the change-request flow for landing memory edits on `main`.
kortix-slack
by kortix-aiHow to answer in Slack as a teammate. Covers the live plan-block stream (`slack step` with --detail/--output, `slack send` to finalize the answer), file uploads, posting to other channels/threads, reactions, search, message editing/deletion, and the tone the bot should use. Load this when the turn is triggered from Slack (the prompt mentions a Slack workspace/channel/thread, or when `$SLACK_BOT_TOKEN` is set in the env), or when the user asks how to do anything in Slack.
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.