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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todo-to-node
by CurryTangConvert a project TODO item into a ResearchOps tree node via LLM. Use when a user wants to expand a TODO into an executable research plan node with commands, checks, and acceptance criteria.
sync-workspace
by CurryTangSync local and remote project files bidirectionally. Handles code, resources, papers, and experiment outputs. Use when user says "sync", "push files", "pull files", "upload code", "download results".
create-env
by CurryTangSet up a reproducible Python project environment on the remote server using pixi or uv. Use when executing a project environment setup node generated by the jumpstart mechanism.
deliverable-step-report
by CurryTangGenerate a deliverable report for each research step using template.md and reference/x_guideline.md, then persist it to .researchops/deliverables and upload to SSH target when the project location is remote.
node-run-clarify
by CurryTangInteractive Q&A to gather run context before executing a ResearchOps tree node step. Use when a node is about to be run to collect key context the agent will need (e.g. dataset path, design doc, hyperparameters).
project-todo-manager
by CurryTangManage project-management TODO workflows in ResearchOps. Use this skill when a user asks to generate, prioritize, clear, classify, or execute TODO items, or asks what to do next for project management tasks.
repo-sync-updater
by CurryTangSynchronize a local or WSL clone with its upstream branch and verify backend runtime prerequisites for this project. Use when users ask to check whether the server is up to date, pull latest changes, fix drift between local and remote branches, or resolve backend tracker runtime issues after updates (especially Playwright browser missing errors).
resource-kb-researcher
by CurryTangAnswer research questions by mining the project's resource repository (resource/) with explicit file-path citations. Use this skill when KB chat misses context or when users ask to verify paper/code evidence from resource files.
todo-dsl-generator
by CurryTangConvert rough user ideas/proposals/chat transcripts into structured TODO DSL steps, with per-step knowledge/codebase references for low-bias orchestration.
agent-instructions-generator
by CurryTangGenerate baseline CLAUDE.md and AGENTS.md instruction files for a project. Use when a repo is missing instruction files or needs standardized agent guidance.
aris-register
by CurryTang"Register or update an ARIS run on the web dashboard. Internal utility used by other skills and CLAUDE.md auto-registration. Call at skill start to register, at skill end to report completion. If ~/.claude/aris-api.json is absent, does nothing (zero-impact)."
auto-review-loop
by CurryTangAutonomous multi-round research review loop. Supports two modes: (1) Plan-driven: takes an implementation plan file, executes TODO items respecting dependency DAG, uses Codex MCP to verify completion of each item. (2) Free-form: iterates review → fix → re-review until positive assessment. Use when user says 'auto review loop', 'review until it passes', or wants iterative improvement.
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.