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...
httpstat
by reorxDiagnose website and API performance using httpstat — a curl wrapper that visualizes HTTP timing breakdowns (DNS, TCP, TLS, server processing, content transfer). Use this skill whenever the user wants to debug slow websites, analyze HTTP/HTTPS latency, profile API response times, understand curl timing output, find network bottlenecks, check TLS handshake speed, measure Time to First Byte (TTFB), or troubleshoot any connection performance issue. Also trigger when the user has a curl command and wants to understand where time is being spent, or when they paste httpstat output and want help interpreting it. Even if the user doesn't mention "httpstat" by name — if they're asking "why is this endpoint slow?" or "what's taking so long?" for an HTTP request, this skill applies.
hnread
by reorxRead and analyze Hacker News discussion threads. Fetches an HN thread URL, converts it to flat markdown using hn_flat.py, then extracts and presents the most valuable discussion insights — critical rebuttals, personal anecdotes, technical corrections, and notable debates. Use when the user provides an HN URL and wants a curated summary of the discussion.
flyctl
by reorxUse when deploying services to Fly.io, configuring fly.toml, managing Fly machines, volumes, domains, health checks, or troubleshooting Fly deployments. Trigger this skill whenever the user mentions Fly.io, flyctl, fly deploy, fly.toml, or wants to run containers on Fly — even if they just say "deploy this to Fly" or "put this on fly.io".
create-skill
by reorxThis is the skill to create skills. Use when user want to create a new skill. 中文语境下，当用户需要创建一个新技能时，使用这个技能。
systools
by reorxSystem ops toolkit for ports and macOS diagnostics. Use for: inspecting or killing processes on a port ("what's on 3000", "port 8080 is taken"); macOS health snapshots — CPU temp, load, memory, swap, disk, network ("how's my mac", "is it overheating"); live memory I/O pressure — pageouts, swap churn, compressor activity ("why is my mac slow", "is it swapping"); WindowServer CPU/GPU diagnostics ("WindowServer is hot", "UI feels laggy"); and listing/managing macOS Background Task Management items shown in System Settings. macOS-only except port management.
obsidian
by reorxSearch notes, open files, and create zettelkasten notes in Obsidian using the Obsidian CLI. Use this skill whenever the user mentions Obsidian, their vault, searching notes, finding notes by tag or content, opening a note, creating a new note, zettelkasten, zettel, or doing anything related to their Obsidian knowledge base — even if they just say "find that note about X", "save this as a note", "open my note on Y", "create a zettel note", or "new zettel". Also triggers when the user wants to look up information they've previously written down, or wants to save research/content to their personal knowledge base.
llm-models
by reorxLook up current LLM model identifiers, pricing, and specs across providers using models.dev data. Use this skill whenever the user asks about model IDs, model names, pricing, context windows, or what models a provider offers — even if they don't say "models.dev". Also use when the user is writing code that needs a model ID and seems uncertain, when comparing costs between models or providers, or when checking what's the latest model from any provider. Covers 90+ providers including OpenAI, Anthropic, Google, Mistral, DeepSeek, xAI, Cohere, and many more.
topic-prompt
by reorxSet, update, or manage the /new session prompt for Telegram group topics. Use when the user sends `/topic-prompt` followed by a subcommand or prompt text in a Telegram group topic. Also triggers when user wants to check, change, or clear a topic's session prompt.
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.