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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competitor-feature-research
by mantinedevResearch new features shipped by other React and Vue component libraries over a given time period and compare them against Mantine to find gaps and divergences. Use when the user wants competitive analysis of UI libraries, to track what competitors added recently, to find missing Mantine features, or asks things like "what did MUI/Chakra/Ant add since X", "what features are we missing", "compare our components to competitors", "feature gap analysis". Operates from the Mantine monorepo.
sync-versions
by mantinedevSync Mantine GitHub releases into packages/@mantinex/mantine-meta/src/versions.ts. Use when the user wants to update the versions list, sync release dates, add missing patch versions, or update changelog links for older major versions. Triggers on requests like "sync versions", "update versions.ts", "add missing releases".
update-dependencies
by mantinedevUpdate all dependencies to latest minor versions across the Mantine monorepo, validate, test, build, and generate a report. Use when the user wants to update dependencies, upgrade packages, run dependency maintenance, or asks to "update deps", "upgrade dependencies", "dependency update", "/update-dependencies".
merge-cascade
by mantinedevMerge changes down the release branch chain (master → 9.0 → 9.1 → … → 9.X). Resolves merge conflicts, pushes to the correct remote per branch. Use when the user wants to cascade/propagate changes from master through all minor release branches (e.g., "merge cascade", "propagate master", "cascade merge", "/merge-cascade").
new-mantine-release
by mantinedevInitialize a new Mantine minor or major release: creates a git branch, sets up the changelog MDX page, updates mdx-meta-data.ts, mdx-nav-data.ts, and versions.ts, and commits. Use when the user wants to start a new release (e.g., "new release 9.1.0", "/new-mantine-release 9.1.0", "initialize 9.2.0 release").
codex-code-review
by mantinedevRun an automated code review of unstaged changes using Codex CLI, then fix issues and re-review in a loop until approved. Use when the user asks to review changes with Codex, run a code review cycle, or invokes /codex-code-review.
mantine-form
by mantinedevBuild forms using @mantine/form in the mantine-9 repository. Use this skill when: (1) setting up a form with useForm, (2) adding validation rules or async validation, (3) working with nested object or array fields, (4) sharing form state across components with createFormContext, (5) using uncontrolled mode for performance, (6) using the standalone useField hook, or (7) any task involving useForm, getInputProps, onSubmit, insertListItem, or form validation.
mantine-custom-components
by mantinedevBuild custom components that integrate with Mantine's theming, Styles API, and core features. Use this skill when: (1) creating a new component using factory(), polymorphicFactory(), or genericFactory(), (2) adding Styles API support (classNames, styles, vars, unstyled), (3) implementing CSS variables via createVarsResolver, (4) building compound components with sub-components and shared context, (5) registering a component with MantineProvider via Component.extend(), or (6) any task involving Factory, useProps, useStyles, BoxProps, StylesApiProps, or ElementProps in @mantine/core.
mantine-combobox
by mantinedevBuild custom dropdown/select/autocomplete/multiselect components using Mantine's Combobox primitives. Use this skill when: (1) creating a new custom select-like component with Combobox primitives, (2) building a searchable dropdown, (3) implementing a multi-select or tags input variant, (4) customizing option rendering, (5) adding custom filtering logic, or (6) any task involving useCombobox, Combobox.Target, Combobox.Option, or Combobox.Dropdown.
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.