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...
git-commit
by kubev2vCompose CI-valid commit messages with Jira linking and sign-off. Use when the user asks to commit, write a commit message, or git commit. Never execute the commit — only present the message and command for the user to run.
git-commit
by kubev2vGenerate git commit messages following MTV project conventions. Use when the user asks to commit, create a commit, write a commit message, or stage and commit changes.
upstream-downstream
by kubev2vGuide for using upstream and downstream variables and conditional content in AsciiDoc modules. Use when writing, editing, or reviewing .adoc files, when using product names, CLI commands, namespaces, or operator names in documentation, or when adding build-specific content for upstream (Forklift) vs downstream (MTV).
i18n-memsource
by kubev2vAutomates the Memsource/Phrase i18n translation workflow for OCP console plugins. Use when the user asks to upload translations, download translations, check translation status, memsource upload, memsource download, i18n upload, i18n download, send for translation, or get translations.
types-update
by kubev2vAutomates the full lifecycle of updating @forklift-ui/types: discovers upstream versions, creates a Jira ticket, runs type generation scripts in the forked types repo, handles KubeVirt/CDI conflicts, creates a PR and GitHub release, waits for npm publish, then bumps the consumer project and monitors CI. Use when the user asks to update types, bump types, update forklift-ui/types, types package update, or regenerate types.
waiting-for-build
by kubev2vQuery the active sprint on the Migrations & Networking Frontend Jira board for all MTV tickets in the "Waiting on build" column, find their merge commits in the local git repo, and check which are included in a given build (supplied as commit hashes). Renders results in an interactive canvas. Use when the user asks about "waiting for build", which tickets are in the build, build inclusion check, sprint tickets vs build, or wants to know what MTV fixes are waiting for QA.
create-plan-for-jira
by kubev2vCreates a plan to address a Jira issue. Use it when the user wants you to suggest how to address an issue or implement a feature.
create-pull-request
by kubev2vCreate pull request. Use when the user asks to create a pull request after some refactor.
validate
by kubev2vExecutes code quality checks. Use when the user asks to validate changes after a refactor or to report test coverage
govc
by kubev2vVMware vSphere automation with govc. Use when the user wants to list inventory, power VMs, clone, snapshot, import OVA, or inspect datastores/hosts on vCenter/ESXi. Output CLI for the user to run — do not execute govc yourself unless they ask.
inventory-ec2
by kubev2vAmazon EC2 inventory field reference and TSL query examples. Use when querying EC2 provider inventory (instances, volumes, networks, storage types).
inventory-hyperv
by kubev2vHyper-V inventory field reference and TSL query examples. Use when querying Hyper-V provider inventory (VMs, disks, networks, storage).
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.