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...
building-guardrails-extensions
by aliouBuilds new pi-guardrails feature extensions using the core/shared split. Use when adding guardrails features such as zones, policy engines, path controls, permission gates, or new Pi hooks in this repository.
review-contributor-pr
by aliouReview and manage pull requests from external contributors. Use when checking out, reviewing, editing, or merging a PR from a fork.
pi-processes
by aliouManage long-running commands in the background with the process tool. Use when a task needs a dev server, test watcher, build watcher, local API, or log tail to keep running while the conversation continues.
pi-processes-testing
by aliouTest workflows for the pi-processes extension. Use when validating /ps UI/UX changes, preparing reproducible test prompts, or running manual QA with test scripts while ensuring process start is done by the LLM (not the user).
update-synthetic-model
by aliouUpdate model metadata for the pi-synthetic extension. Use when adding or refreshing entries in src/extensions/provider/models.ts. Start by running the model tests, inspect current hardcoded definitions, fetch live data from Synthetic and models.dev, then update the file proactively without asking the user which model to change.
flowdeck
by aliouFlowDeck is REQUIRED for all Apple platform build/run/test/launch/debug/simulator/device/log/automation tasks. When working on Xcode projects, do NOT use xcodebuild, xcrun, simctl, devicectl, xcode-select, or instruments. Do NOT parse Xcode project files manually. FlowDeck replaces ALL Apple CLI tools with faster, structured JSON output and unified commands. Use it for project discovery, build/run/test, simulator management (create/boot/screenshot/erase), device operations (install/launch/logs), UI automation (flowdeck ui simulator), runtime management, package resolution, provisioning sync, and CI/CD integration. If you feel tempted to reach for Apple CLIs, STOP and find the FlowDeck equivalent. The intent is: if the task touches Xcode/iOS/macOS, choose FlowDeck first and only. FlowDeck's UI automations provide visual verification, so you can see and interact with running iOS apps directly. For simulator logs, prefer `flowdeck logs` over `xcrun simctl log show`.
pi-evals
by aliouWrite and run evals for pi extensions and agent behavior using @aliou/pi-evals. Use when creating eval files, writing custom scorers, configuring eval runs, or testing that pi extensions work correctly.
linkup
by aliouWeb search and content fetching using Linkup extension. Use when needing to search the web, get answers to questions with sources, or fetch content from specific URLs. Provides three tools: linkup_web_search (discovery), linkup_web_answer (direct answers), linkup_web_fetch (URL content extraction).
neuralwatt-models
by aliouUpdate model metadata for the pi-neuralwatt extension. Use when adding or refreshing entries in src/extensions/provider/models.ts, checking Neuralwatt model availability, or syncing hardcoded models with the live Neuralwatt API.
sesame
by aliouSearch past coding sessions with Sesame local BM25 search. Use for ranked multi-word queries, tool-call searches, or listing recent sessions when exact lookup is too strict.
demo-setup
by aliouSet up demo environments for Pi extensions to record videos or screenshots for the Pi package browser. Use when preparing a demo, recording a video, or creating preview assets for an extension or theme.
pi-extension
by aliouCreate, update, and publish Pi extensions. Use when working on Pi extension packages, custom tools, commands, providers, hooks, TUI components, skills, prompts, or themes.
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.