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...
syncable-entity-cache-and-transform
by twentyhqCreate cache services and transformation utilities for syncable entities in Twenty. Use when implementing entity-to-flat conversions, input DTO transpilation to universal flat entities, or cache recomputation for syncable entities.
create-app
by twentyhqUse when the user wants to create or scaffold a new Twenty app
develop-app
by twentyhqUse when the user wants to add or modify Twenty app entities, including objects, layouts, logic functions, and front components inside an existing Twenty app.
twenty-record-presentation
by twentyhqRetrieve and present Twenty CRM records as readable summaries or tables, using the connected Twenty MCP server to discover fields, fetch relevant data, format dates and values, build record links, and avoid raw API output.
syncable-entity-builder-and-validation
by twentyhqCreate validation logic and migration action builders for syncable entities in Twenty. Use when implementing business rule validation, uniqueness checks, foreign key validation, or building workspace migration actions for syncable entities. Validators never throw and never mutate.
syncable-entity-testing
by twentyhqCreate comprehensive integration tests for syncable entities in Twenty. Use when writing integration tests for metadata entities, covering validator exceptions, input transpilation errors, and CRUD operations. Tests are MANDATORY for all syncable entities.
syncable-entity-runner-and-actions
by twentyhqImplement action handlers for executing workspace migrations in Twenty. Use when creating database operations for syncable entities, implementing universal-to-flat entity transpilation, or handling create/update/delete actions in the runner layer.
syncable-entity-types-and-constants
by twentyhqDefine types, entities, and central constant registrations for syncable entities in Twenty's workspace migration system. Use when creating new syncable entities, defining TypeORM entities, flat entity types, or registering in central constants (ALL_ENTITY_PROPERTIES_CONFIGURATION_BY_METADATA_NAME, ALL_ONE_TO_MANY_METADATA_RELATIONS, ALL_MANY_TO_ONE_METADATA_FOREIGN_KEY, ALL_MANY_TO_ONE_METADATA_RELATIONS).
syncable-entity-integration
by twentyhqWire syncable entity services into NestJS modules, create service layer and resolvers for Twenty entities. Use when registering builders, validators, and action handlers in modules, creating business services, or exposing entities via GraphQL API with proper exception handling.
use-twenty-mcp
by twentyhqUse when the user wants Codex to connect to an existing Twenty workspace through MCP, retrieve or inspect workspace records and metadata, or present Twenty CRM data as readable Markdown with formatted dates, values, record links, and compact tables instead of raw API output.
twenty-partner-design-doc
by twentyhqUse when turning a qualified Twenty lead — a call-summary brief plus any client braindump/docs — into a partner brief a partner can scope and quote from. Trigger when pointed at a lead folder (e.g. partners-experience/<LEAD>/) and asked to "draft a design doc", "create a partner brief", "translate this into Twenty terms", "scope this for a partner", or "prep the partner handoff". Default output is zero-inference (only what the client said; empty sections get a placeholder). Pass --full for inference mode. Chains after twenty-lead-intro-call-summary.
twenty-lead-intro-call-summary
by twentyhqTurn a sales/discovery-call transcript into a faithful, structured qualification brief for Twenty's partner/CRM pipeline. Use this whenever the user has a call recording or transcript — including a meetily recordings folder or a transcripts.json — and wants to summarize, recap, qualify, or extract a brief from a prospect/discovery call. Trigger even when they don't say "brief": phrases like "summarize this call", "what did we learn from the X call", "qualify this lead from the call", "turn this transcript into something I can hand a partner", or pointing at a transcript file all count. Produces a tight deal one-pager (company, needs, implementation complexity, does-it-need-a-partner, partner-facing brief) plus an appendix (product/GTM feedback, terminology, quotes). It is a faithful extraction, not a lossy summary.
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.