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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doc-exec-log-append
by w5851以 append-only 方式向执行台账追加记录，默认不回读历史正文，降低上下文污染。关键词：执行记录、台账追加、append-only、batch、不回读上下文。
mcp-inspire
by w5851Use when Codex needs to retrieve scholarly literature through INSPIRE-first workflows, including INSPIRE search/sorting, open-access arXiv PDF download, compliant triage between open files and publisher landing pages, and supervised Playwright institution-login handoff for legally authorized full-text access.
literature-review
by w5851Structured multi-database literature review workflow for academic synthesis with reproducible search logs and thematic evidence mapping. Use when you need rigorous review methodology (scope, screening, synthesis), not general brainstorming.
literature-reproduction-spike
by w5851在 Julia_RelaxTime 中执行隔离式文献复现 Spike。适用于指定论文图表、公式或数值结论的独立 tempN 沙箱复刻、文献事实表、未明示口径审计、复现证据链和二选一 verdict：与文献对齐 / 文献信息不足以复现。关键词：isolated reproduction spike, temp, 文献复现, 独立复现, 复刻, figure reproduction, formula audit, verdict
api-doc-authoring
by w5851为 Julia_RelaxTime 编写或更新 API 文档时使用。适用于按三层视图组织 API 文档：面向用户入口、职责核心、导出 API 全集；并要求导出 API 全集必须通过脚本自动生成而不是人工维护。关键词：API 文档、导出函数、公共接口、职责核心、导出 API 全集、docs/api。
baseline-regression-governance
by w5851管理数值基线回归的全流程规范（基线生成、存储、测试校验、CI门禁与变更准入），适用于固定点/采样点回归、防止版本更新引入数值漂移。关键词：baseline, regression, fixed points, rtol, atol, smoke, nightly, PNJL, transport
citation-management
by w5851Citation operations skill for DOI/arXiv metadata normalization, BibTeX curation, duplicate cleanup, and reference consistency checks. Use when managing reference quality and bibliography integrity for papers and reports.
codex-task-harness
by w5851将宽泛的 Julia_RelaxTime 任务收束为可执行 harness：边界、风险、验证、交付物与推荐 skill。适用于跨后端、前端契约、文档、论文、研究分析的混合任务。关键词：task harness, scope, validation, skill routing, execution plan
deep-research
by w5851Comprehensive cross-domain research synthesis skill focused on broad background gathering, multi-source summaries, and citation-organized overviews. Use for general topic research and perspective mapping, not for implementation-level method selection.
doc-archive
by w5851Archive docs/dev/active markdown tasks into docs/dev/archived with required metadata frontmatter, using scripts/dev/archive_docs.jl and validating format compliance.
doc-coauthoring
by w5851Collaborative technical writing workflow for specs, architecture notes, and research manuscripts. Use when drafting or revising structured documents that require clear audience targeting, iterative refinement, and consistency checks.
doc-design-from-zero
by w5851从零设计开发文档（需求文档/技术方案/实施任务单）并输出可勾选结构。适用于新功能立项、重构规划、研究任务分解。关键词：开发文档设计、任务单、DoD、里程碑、验收标准、MVP。
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.