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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p4-duplicate-stream
by ShanWeng-WTDuplicates a Perforce (P4) stream and all its downstream child streams under new names. Supports renaming via substring replacement or fully custom naming. Use this skill whenever the user wants to duplicate, clone, or copy a P4 stream hierarchy, create a parallel set of streams from an existing stream tree, or mentions "duplicate stream", "clone stream hierarchy", "copy stream tree", "duplicate p4 stream", or wants to replicate a stream and its children with new names. Also trigger when the user asks to "create streams like" an existing set but with different names.
p4-export
by ShanWeng-WTExports all files from a Perforce changelist to a local directory, preserving the relative directory structure. Use when the user wants to extract or export the contents of a specific P4 changelist to a local directory.
p4-list-changelists
by ShanWeng-WTLists submitted Perforce (P4) changelists using filters for date period, owner, description text, and changelist number range. Use this skill whenever the user wants to list, search, find, filter, report, or populate P4 changelists/CLs by submitter, date, description, or CL range, including natural language dates like today, yesterday, last week, or past week.
p4-merge-cl
by ShanWeng-WTMerges a specific Perforce changelist from a source stream into one or more target streams using p4 merge/integrate. Handles shelving already-open files, syncing to latest, finding the CL by number or description, safe resolve, and creating new pending changelists. Supports parallel dispatch for multiple source→target pairs. Trigger whenever the user wants to merge, integrate, or propagate a specific P4 changelist between streams — even if they phrase it as "merge CL from X to Y", "integrate this changelist across branches", "merge from source to target", or "batch merge across streams".
p4-move-conflict-files
by ShanWeng-WTMoves all unresolved conflict files from a given Perforce (P4) changelist into a new pending P4 changelist. Trigger when the user asks to move, isolate, or separate P4 conflict files out of a p4 changelist, or mentions "p4 conflict CL", "p4 resolve conflicts", "move p4 conflicts", or "conflict changelist" in the context of Perforce.
p4-port-cl
by ShanWeng-WTPorts the changes from a submitted Perforce changelist in one workspace/stream to one or more other workspaces/streams. Trigger whenever the user wants to apply, port, propagate, replicate, copy, or mirror a P4 changelist's edits across multiple workspaces or streams — even if they phrase it as "apply the same changes", "do the same thing in other branches", "copy CL to other workspaces", or "make the same fix across all versions".
p4-stream-remap-ignore-manager
by ShanWeng-WTStrictly audits Perforce stream Remapped and Ignored sections. Follows a strict interactive workflow to export configurations and generate unified comparison reports.
p4-workspace-check
by ShanWeng-WTValidates that the active Perforce (P4) client workspace matches the current working directory before executing workspace-dependent P4 commands, and auto-corrects mismatches by setting P4CLIENT to the correct workspace.
p4-claude-simplify
by ShanWeng-WTReviews all changed files in a Perforce (p4) workspace for code reuse, quality, and efficiency by launching three parallel review agents, then fixes any issues found.
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.