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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ns-api
by NethServerWrite or modify a NethSecurity Python RPCD API script or hook. Use when creating or updating `ns.*` RPCD API endpoints, handling UCI configuration changes, managing pre/post-commit hooks, defining ACL permissions, and documenting methods in OpenAPI 3.1.0. Covers stdin/stdout JSON protocol, error handling, naming conventions, code style, and spec file updates.
python-nethsecurity
by NethServerWrite or modify Python scripts for NethSecurity packages. Use when creating new Python scripts, configuring package build systems, or writing utilities. Covers shebang, license headers, extension handling, ruff compliance, available modules, and UCI commit conventions.
openwrt-package-update
by NethServerUse when updating any forked OpenWrt package in a NethSecurity workspace from the upstream openwrt/packages feed. Covers the full update cycle: version bump, merging upstream file changes, updating dependent ns-api handlers and migration scripts, UCI overlay defaults, and correlated documentation. Triggers on updating a package by name (adblock, mwan3, banip, rsyslog, snort3, keepalived, python-jinja2, python-semver, and similar forks), syncing with upstream, or any request to align a local package fork with openwrt/packages — even if upstream is not mentioned explicitly.
openwrt-package
by NethServerCreate or modify an OpenWrt ns-* package including Makefile, config fragments, and upstream patches. Use when building new packages for NethSecurity, managing package dependencies, patching upstream feeds, or modifying build configurations. Covers naming conventions, required Makefile fields, architecture selection, external version management, and patch workflows.
vue-components
by NethServerWrite, edit, or review Vue 3 components and views in this project. Use for creating new components, fixing template issues, applying Ne* component library, handling props/emits, i18n strings, loading/error state, and icon usage. Covers script setup patterns, typed props, Ne component substitutions, and anti-patterns to avoid.
testing
by NethServerWrite or review Vitest unit tests in this project. Use when adding tests for lib utilities, valibot schemas, composables, or validation helpers. Covers TDD approach, meaningful test design (edge cases, pitfalls), what not to test, mocking ubusCall, and test file location conventions.
typescript
by NethServerWrite TypeScript in this project correctly. Use when defining types for API responses, avoiding any, typing props/emits/refs, using generics with ubusCall, or fixing ESLint TypeScript warnings. Covers clean ESLint-compliant patterns without suppression comments.
forms-validation
by NethServerBuild forms, validate user input, handle submit actions, or display field errors. Use when writing valibot schemas, wiring up MessageBag, handling ValidationError from ubusCall, or building a form submit flow with useMutation. Covers all project validation helpers (IP, hostname, MAC, CIDR, etc.) and the correct pattern for resetting state.
data-fetching
by NethServerWrite new API calls, server-state queries, or mutations in this project. Use when adding useQuery, useMutation, ubusCall, or migrating manual loading/error refs to TanStack Query. Covers query keys, typed responses, invalidation, pagination with keepPreviousData, and the legacy patterns to recognise and replace.
composables
by NethServerCreate or refactor Vue 3 composables (use* functions) in src/composables/. Use when building shared reactive logic, migrating legacy manual-fetch composables to TanStack Query, deciding between a composable and a Pinia store, or structuring type exports. Covers the modern useQuery pattern and how to recognise the old pattern that must not be replicated.
nethserver-pr
by NethServerCreate and manage pull requests following NethServer contribution conventions. Use when the user asks to open a PR, submit a contribution, or mentions "/pr". Supports: (1) PR title and description structure with a ready-to-use template, (2) Correct issue link format for NethServer/NethVoice and NethSecurity, (3) Reviewer and self-assignee selection, (4) Merge commit conventions with issue references for automation, (5) Draft PR guidance for work-in-progress contributions
nethserver-issue
by NethServerWrite well-structured GitHub issues following NethServer contribution guidelines. Use when the user asks to open an issue, file a bug report, request a feature, or mentions "/issue". Supports: (1) Bug report template with component, reproduction steps, and expected behavior, (2) Feature request template with user value and references, (3) Issue type selection (Bug/Feature/Design/Backend/Frontend/Task/Draft), (4) Label guidance (testing, verified, milestone goal), (5) Security vulnerability reporting rules and correct tracker URLs
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.