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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work-agent
by treasure-dataUse when the user wants to create, configure, schedule, or run an agent in Treasure Work. Covers AGENTS.md authoring, the `agent_*` MCP tools, on-demand vs scheduled agents, lifecycle (draft → active → paused), and chat-based result inspection. Triggers on "create an agent", "set up an agent", "schedule a task", "set up a recurring job", "automate daily report", "cron job", "run X every weekday", etc.
field-agent-deployment
by treasure-dataBest practices for developing, testing, and deploying production-ready Field Agents including R&D workflows, version control, testing strategies, and release management
field-agent-visualization
by treasure-dataProfessional Plotly visualization best practices for Field Agents including chart specifications, color palettes, formatting standards, and JSON structure requirements for executive-ready data visualizations
field-agent-documentation
by treasure-dataComprehensive template and guidelines for documenting Field Agents including technical specifications, system prompts, tool specifications, user interactions, and standardized documentation structure
trino-optimizer
by treasure-dataTD Trino performance optimization including CTAS (5x faster), UDP bucketing for ID lookups, magic comments for join distribution, REGEXP_LIKE vs LIKE, and approx functions.
rt-personalization-validation
by treasure-dataValidates RT Personalization Entity payloads before creation to prevent common API errors. MUST be invoked proactively during rt-setup-personalization workflow at Step 9c before making the entity creation API call. Also use when encountering "Attribute payload can't be blank" errors or when reviewing personalization entity JSON.
pptx
by treasure-dataUse this skill any time the user wants to create a new PowerPoint presentation, slide deck, pitch deck, or .pptx file from scratch. Covers creating business presentations, quarterly reports, project proposals, product roadmaps, training materials, and any multi-slide document destined for PowerPoint. Works by generating HTML/CSS slides (which LLMs excel at), rendering them in agent-browser for pixel-accurate DOM position extraction, and assembling the final PPTX with native PowerPoint charts, tables, and images via PptxGenJS. Includes bundled scripts for validation, DOM extraction, and PPTX assembly. Do NOT use this skill for editing existing PPTX files, converting other formats to PPTX, or extracting content from PPTX files.
google-slides-review
by treasure-dataUse this skill at the end of a Google Slides deck-creation flow run by the same agent in the current session, before reporting the finished URL to the user. Catches the failure modes a creating agent reliably misses — surviving placeholder text, unswapped template imagery, layout overflow, hidden-original leaks. Trigger when the parent google-slides workflow reaches Step 9 / QA, or when the user asks "is this deck ready?" / "QA my deck" about a deck this agent itself just edited. Do NOT trigger for reviewing decks the agent did not create — without the Step 5 pre-fill snapshot the leak check silently degrades and the result is misleading.
time-filtering
by treasure-dataAdvanced td_interval patterns including offset dates (-1d/2025-10-01, -7d/-1d, 0M/now), td_interval_range for debugging, td_time_string for display formatting, and partition pruning optimization.
email-campaign
by treasure-dataThis skill should be used when the user asks to "create an email", "build an email campaign", "design an email template", "generate an email for a segment", "preview an email", or "push an email to Engage". Generates enterprise-grade HTML email templates with live preview in Treasure Studio and natural language editing, then pushes the final version to Treasure Engage.
work
by treasure-dataUse when the user asks to create, update, list, or manage work items, goals, notes, guides, or references in a Treasure Work workspace. Triggers on "create a task", "add a work item", "move to done", "show my tasks", "goal progress", "create a note", "what's next", or any workspace document management. Also use when operating on files in goals/, items/, guides/, notes/, or references/ folders.
brand-onboarding
by treasure-dataInteractive wizard to create comprehensive brand guidelines for your company. Set up brand colors, fonts, voice, messaging, legal requirements, and accessibility standards. Use when setting up brand guidelines, creating brand standards, or configuring brand compliance. One-time setup enables all creative skills.
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.