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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scienceworld-container-inspector
by taomiaoThis skill performs a 'look at' action to inspect the contents of a specific container or device. It should be triggered when the agent needs to verify what is inside a container (e.g., checking if lead is in the blast furnace) or monitor the state of contents (e.g., solid vs. liquid). The skill outputs a detailed list of contents and their states, providing essential feedback for process monitoring.
scienceworld-substance-preparator
by taomiaoThis skill transfers a target substance into an appropriate container for processing (e.g., a pot for heating, a beaker for mixing). It should be triggered after acquiring the substance and before setting up an apparatus. The skill selects a suitable empty container and moves the substance into it.
spectra-ask
by PsychQuantQuery openspec/documents and answer questions
troubleshoot-separation
by pjt222Diagnosticar y resolver problemas de separación cromatográfica en GC y HPLC, incluyendo resolución insuficiente, forma de pico deficiente, problemas de línea base, pérdida de presión, contaminación de columna y pérdida de eficiencia. Usar cuando un método cromatográfico deja de funcionar correctamente, cuando se observe degradación progresiva del rendimiento a lo largo del tiempo, cuando aparezcan picos extra o artefactos inesperados, o cuando el método no cumpla los criterios de aceptación durante la validación.
troubleshoot-separation
by pjt222Systematically diagnose and resolve chromatographic separation problems: document symptoms, identify root causes for peak shape and retention anomalies, evaluate matrix effects, and implement targeted fixes using a one-variable-at-a-time approach for GC and HPLC systems.
troubleshoot-separation
by pjt222Systematically diagnose and resolve chromatographic separation problems: document symptoms, identify root causes for peak shape and retention anomalies, evaluate matrix effects, and implement targeted fixes using a one-variable-at-a-time approach for GC and HPLC systems.
troubleshoot-separation
by pjt222系统地诊断和解决色谱分离问题：记录症状、识别峰形和保留时间异常的根本原因、 评估基质效应，并使用逐一变量法对 GC 和 HPLC 系统实施针对性修复。
interpret-chromatogram
by pjt222Interpret a chromatogram from GC or HPLC analysis: verify system suitability parameters, identify peaks by retention time and spectral matching, perform accurate peak integration, calculate chromatographic figures of merit, and assess overall peak quality for reliable quantitation.
troubleshoot-separation
by pjt222Systematically diagnose and resolve chromatographic separation problems: document symptoms, identify root causes for peak shape and retention anomalies, evaluate matrix effects, and implement targeted fixes using a one-variable-at-a-time approach for GC and HPLC systems.
chemistry-lab-techniques-guide
by sandraschiChemistry expert covering organic, inorganic, physical chemistry, lab safety, and experimental techniques
gcms-processing
by dailycafiProcess GC-MS data for metabolomics and volatile compound analysis including peak detection, deconvolution, and NIST library matching. Use when: user has GC-MS data, needs retention index calculation, wants to identify volatiles, match EI spectra against NIST, or process derivatized metabolites. Triggers: GC-MS, gas chromatography, volatile analysis, NIST library, retention index, Kovats index, EI spectrum, electron ionization, deconvolution, AMDIS, TMS derivatives, volatile profiling, headspace analysis, SPME.
reagent-expiry-alert
by GeorgeDoors888Scan reagent barcodes or IDs, log expiration dates, and generate multi-level alerts before reagent expiry to support laboratory inventory management.
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.