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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ts-convert-from-tableau
by thoughtspotConvert or import a Tableau workbook (.twb or .twbx) into ThoughtSpot — parses TWB XML, generates table + model TMLs, validates and imports. Optionally migrates dashboards to liveboards with layout approximation. Direction is always Tableau → ThoughtSpot. Not for ThoughtSpot → Tableau or standalone TML exports.
ts-recipe-formula-hms-display-snowflake
by thoughtspotCreates four Snowflake scalar UDFs that format integer durations (seconds or minutes) as human-readable time strings — HH:MM:SS, DD:HH:MM:SS, HH:MM, DD:HH:MM — then shows the ThoughtSpot formula syntax to display them in any Model. Use this skill whenever the user asks about displaying durations, formatting elapsed time, converting seconds or minutes to a readable format, showing call duration, handle time, SLA elapsed time, ticket age as HH:MM:SS, or any scenario where an integer count of seconds or minutes should appear as a formatted time string in ThoughtSpot.
ts-recipe-formula-business-days-snowflake
by thoughtspotCreates three Snowflake scalar UDFs for calculating business-day (weekday-only) date differences and elapsed time, then shows the ThoughtSpot formula syntax to use them in any Model. Use this skill whenever the user asks about weekday date calculations, business day counts, working day differences, SLA tracking, ticket age, order fulfillment time, or wants to exclude weekends from any date difference in ThoughtSpot or Snowflake. ThoughtSpot's built-in diff_days/diff_hours/diff_minutes count calendar days — if a user needs weekday-only equivalents, invoke this skill.
ts-recipe-formula-hms-display-snowflake
by thoughtspotCreates four Snowflake scalar UDFs that format integer durations (seconds or minutes) as human-readable time strings — HH:MM:SS, DD:HH:MM:SS, HH:MM, DD:HH:MM — then shows the ThoughtSpot formula syntax to display them in any Model. Use this skill whenever the user asks about displaying durations, formatting elapsed time, converting seconds or minutes to a readable format, showing call duration, handle time, SLA elapsed time, ticket age as HH:MM:SS, or any scenario where an integer count of seconds or minutes should appear as a formatted time string in ThoughtSpot.
ts-recipe-formula-business-days-snowflake
by thoughtspotCreates three Snowflake scalar UDFs for calculating business-day (weekday-only) date differences and elapsed time, then shows the ThoughtSpot formula syntax to use them in any Model. Use this skill whenever the user asks about weekday date calculations, business day counts, working day differences, SLA tracking, ticket age, order fulfillment time, or wants to exclude weekends from any date difference in ThoughtSpot or Snowflake. ThoughtSpot's built-in diff_days/diff_hours/diff_minutes count calendar days — if a user needs weekday-only equivalents, invoke this skill.
ts-convert-from-snowflake-sv
by thoughtspotConvert a Snowflake Semantic View into a ThoughtSpot Model by reading the view DDL, mapping tables and joins, translating SQL expressions to ThoughtSpot formulas, and importing the model via the ThoughtSpot REST API.
ts-object-model-coach
by thoughtspotPrepare a ThoughtSpot Model to work well with Spotter. Use when you want to improve how a Model responds to natural language queries — reviewing and writing AI context, synonyms, reference questions, business terms, and data model instructions. Mines real language from existing Liveboards, Answers, and optionally Snowflake query history to ground the coaching in how users actually talk about the data.
ts-profile-databricks
by thoughtspotSet up and manage Databricks connection profiles. Use when configuring a new Databricks workspace, updating credentials, or testing whether an existing profile is working. Supports Service Principal (OAuth M2M) and Personal Access Token auth. Credentials are stored securely in the OS keychain.
ts-profile-thoughtspot
by thoughtspotSet up and manage ThoughtSpot connection profiles. Use when configuring a new ThoughtSpot instance, updating credentials, or testing whether an existing profile is working. Credentials are stored securely in the OS keychain.
ts-variable-timezone
by thoughtspotManage the ts_user_timezone variable in ThoughtSpot — search current values, or set/remove timezone values at org or user level.
ts-convert-to-snowflake-sv
by thoughtspotConvert a ThoughtSpot Worksheet or Model into a Snowflake Semantic View by exporting TML, mapping columns and joins, translating formulas, and creating the view via SYSTEM$CREATE_SEMANTIC_VIEW_FROM_YAML.
ts-profile-thoughtspot
by thoughtspotManage ThoughtSpot connection profiles for Snowflake workspaces — add, list, update, delete, and test profiles. Supports both bearer token and password authentication. Sets up External Access Integration and stores credentials securely using Snowflake Secrets. Token profiles expire after ~24 hours; password profiles remain valid until the password changes.
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.