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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update-user-guide
by eli-ericUpdate or extend the end-user documentation in `docs/user-guide/` after shipping a user-facing feature. Use when adding a new workflow, changing user-facing behavior in an existing workflow, expanding what a persona can do, or fulfilling/removing a "Coming soon" item. Targets the per-module folder under `docs/user-guide/<module>/` with its README + `workflows/` pages, and follows the templates in `docs/user-guide/_template/`.
architecture
by eli-ericProject structure and module organization. Use when creating new modules, adding new features, understanding where files should be placed, or exploring the codebase structure.
predicates
by eli-ericPredicate functions and type guards for clean, self-documenting code. Use when writing boolean conditions, type narrowing, validation logic, or creating reusable predicate functions. Covers isEmpty, hasValue, isValid, can/has/should naming patterns.
tables
by eli-ericTable layout patterns with PandaTableV2, sticky headers, pagination, and scroll handling. Use when implementing tables, fixing scroll issues, or working with TableLayoutContainer in different layout contexts.
toast-patterns
by eli-ericToast notification patterns for mutations using toast.promise from sonner. Use when implementing mutation feedback, loading/success/error states, or async operation notifications.
update-technical-docs
by eli-ericUpdate or extend the engineering-facing documentation in `docs/technical/` after a code change that affects architecture, public hook/store surfaces, query/mutation contracts, module structure, or known maintenance items. Use when a feature ships, when a module is split or consolidated, when a new query/mutation is added or removed, when domain vocabulary changes, when an Open question is resolved, or when a decision is worth promoting to an ADR. Targets the cross-cutting pages, the per-feature pages under `docs/technical/<area>.md` and `systems-family/<page>.md`, the glossary in `docs/CONTEXT.md`, and the ADRs in `docs/adr/`.
form-wizard
by eli-ericMulti-step form wizard (Form Wizard V3) with React Hook Form integration. Use when creating multi-step forms, implementing wizard flows, step validation, conditional steps, or onStepComplete handlers.
modals
by eli-ericDynamic modal system using useDynamicModalStore. Use when opening/closing modals, implementing Dialog or Sheet overlays, handling nested modals, or managing z-index for modal layering.
design-system
by eli-ericUI component patterns with shadcn/ui, Zod validation, and Tailwind CSS. Use when creating or modifying UI components, implementing forms with validation, using Dialog/Sheet/Button/Card components, migrating from HeadlessUI, or styling with Tailwind.
data-fetching
by eli-ericData fetching patterns with TanStack Query, queryFetcher, and queryMutate utilities. Use when creating query hooks, mutation hooks, or working with the fetcher utilities.
i18n
by eli-ericInternationalization patterns using react-intl. Use when working with translations, adding new messages to locale files, using useIntl or FormattedMessage, or when user asks about translation patterns in this codebase.
improve-codebase-architecture
by eli-ericFind deepening opportunities in the ELI PANDA codebase, informed by the domain glossary in `docs/CONTEXT.md`, the per-feature pages in `docs/technical/`, the ADRs in `docs/adr/`, and the conventions in `CLAUDE.md`. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make the codebase more testable and AI-navigable.
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.