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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google-gemini-image
by beam-ai-teamGenerate, edit, and refine images with Google Gemini. Load when user says 'generate image', 'edit image', 'refine image', 'text to image', 'gemini image', 'modify image'.
follow-up-automation
by beam-ai-teamStreamlined follow-up system for sales calls based on call transcripts. Load when user mentions "follow-up", "follow up email", "call follow-up", "send follow-up", "post-call follow-up", "follow-up automation", "create follow-up", "generate follow-up", or when user provides a call transcript and needs to send follow-up communication and update CRM. Analyzes call transcripts to extract technical requirements, decision makers, timeline, and pain points, then generates concise technical follow-up emails (under 200 words) and updates CRM with lead status and next steps.
1on1-review
by beam-ai-teamDeep expert coaching review of a 1-on-1 meeting. Triggers: '1on1 review', 'review my 1on1 with [name]', 'coach me on my 1on1', 'how did my 1on1 go', 'deep review my 1on1', 'expert feedback on my meeting with [name]'.
1on1-prep
by beam-ai-teamPrepare for an upcoming 1-on-1. Triggers: 'prep my 1on1 with [name]', '1on1 prep', 'prepare for my meeting with [name]', 'what should I cover with [name]', 'get ready for 1on1'.
1on1-followup
by beam-ai-teamQuick post-meeting follow-up for 1-on-1s. Triggers: '1on1 followup', 'follow up on my 1on1', '1on1 with [name]', 'debrief my 1on1', 'process my 1on1 with [name]'.
google-tasks
by beam-ai-teamManage Google Tasks and task lists. Load when user mentions 'google tasks', 'tasks', 'todo list', 'create task', 'complete task', or references task/todo management.
allhands-prep
by beam-ai-teamPrepare all-hands meetings for Beam. Load when user says 'prepare all-hands', 'all-hands prep', 'plan all-hands meeting', 'create all-hands agenda', or mentions preparing for company-wide meetings. Helps structure the meeting, draft talking points, create prep checklists, and follow Beam's all-hands guidelines.
quick-start
by beam-ai-teamComplete onboarding: welcome, language, goals, integrations, workspace, first PROJECT, permissions.
setup-goals
by beam-ai-teamLoad when user says 'setup goals', 'personalize beam next', 'set my goals', 'define my role'. Captures role, goals, preferences. 8-10 min.
document-nodes
by beam-ai-teamGenerate human-readable documentation and checklists from Node JSON files. Use when asked to document nodes, create checklists for node processing, or explain node extraction logic. Processes all nodes in the Nodes directory systematically.
beam-credit-analysis
by beam-ai-teamAnalyze Beam.ai agent credit consumption per execution path. Load when user says 'credit analysis', 'beam credit analysis', 'agent credit consumption', 'how many credits does this agent use', 'cost per path', 'analyze agent credits', 'credit breakdown', or provides a Beam agent URL and asks about credits or cost.
calculate-beam-agent-pricing
by beam-ai-teamDesign node architecture and calculate comprehensive pricing for Beam AI agents based on requirements. Load when user says 'calculate agent pricing', 'price this agent', 'design agent architecture', 'estimate agent cost', 'node breakdown for agent', or needs detailed cost analysis for a Beam agent project. Generates complete node-by-node breakdown with credit consumption, monthly economics, optimization strategies, and client pricing models.
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.