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...
bib-search-citation
by bahayonghangSearch and cite from local BibTeX/BibLaTeX .bib libraries, including Zotero exports. Use to find, filter, preview, export, or generate LaTeX/Typst citation snippets by topic, author, year, venue, DOI, arXiv ID, keywords, abstract, fields, recency, or claim support. Do not use for manuscript writing or polishing.
cover-letter
by bahayonghangSubmission cover-letter assistant for existing LaTeX manuscripts. Use to generate, optimize, align-check, preflight, and journal-fit-check cover letters against paper evidence, novelty claims, and target venue expectations. Also handles Chinese requests (写投稿信 / 致编辑信). Do not use for editing main.tex, full manuscript audit, bibliography search, or a job-application 求职信.
latex-paper-en
by bahayonghangEnglish LaTeX assistant for existing .tex journal or conference papers. Use for compile repair, venue formatting, bibliography/citation checks, section writing, logic, related work, tables, pseudocode, de-AI polish, translation, adaptation, and submission readiness; use latex-thesis-zh for Chinese theses and paper-audit for critique.
latex-thesis-zh
by bahayonghang中文 LaTeX 学位论文助手，面向已有 .tex 硕博论文项目与高校模板。用于编译诊断、GB/T 7714、模板/章节结构、术语一致性、绪论/方法/实验/结论主线、文献综述、研究空白、摘要标题、三线表和去 AI 味；英文论文用 latex-paper-en，审稿总评用 paper-audit。
paper-audit
by bahayonghangReviewer-style audit and submission gate for academic papers in .tex, .typ, or .pdf. Use for peer-review critique, readiness/gate decisions, blocker triage, revision roadmaps, journal-style reports, and re-audits. Do not use for source editing, sentence polishing, bibliography search, or compile repair.
typst-paper
by bahayonghangTypst paper assistant for existing .typ manuscripts in English or Chinese. Use for compile/export diagnosis, venue formatting, BibTeX/Hayagriva checks, grammar, logic, abstract/title, tables, pseudocode, related work, research-gap framing, adaptation, de-AI polish, translation, and submission readiness; use LaTeX skills for .tex.
drawio
by bahayonghangCreate, edit, replicate, import, and export draw.io diagrams with an offline YAML-first workflow. Use for general engineering and product diagrams: architecture, network topologies, flowcharts, UML/ER, org charts, Mermaid/CSV conversion, existing .drawio bundles, style presets, themes, and non-publication formula diagrams. For paper, thesis, journal, conference, IEEE/ACM, manuscript, camera-ready, or publication figures, prefer drawio-academic-skills; this base provides shared CLI, references, themes, schemas, styles, and optional Desktop export.
drawio-academic-skills
by bahayonghangPublication-figure overlay for draw.io. Use instead of the general drawio skill whenever a diagram is for a paper, thesis, dissertation, journal, conference, IEEE/ACM submission, manuscript, camera-ready, Word/LaTeX figure, or other publication. Applies venue, figure-type, color, caption/legend, formula, and paper-readability gates for architecture, workflow, roadmap, network-topology, and replicated paper figures. Runs offline and YAML-first through the sibling ../drawio base for CLI, references, themes, schemas, styles, and Desktop export; never requires MCP or a live backend.
roundtable
by bahayonghangMulti-perspective roundtable discussion skill for exploring a topic through a truth-seeking moderator and a small set of representative real figures. Use when the user asks for 圆桌讨论 / roundtable / 多人物观点碰撞 / 让几位思想家讨论 / 模拟多方思想交锋 / 结构化辩论探索, or wants to examine one topic through disciplined multi-party debate rather than a plain summary. Do not use it for ordinary argumentative writing, generic pros/cons lists, or a single-speaker explanation.
bidwriter
by bahayonghang智能标书编写专家，专精工程咨询、建筑设计、市政工程领域的投标文件编写。 当用户提到：标书、投标、招标、投标文件、技术标、商务标、招标响应、投标方案、 bid document、bid proposal、tender、proposal writing、编写标书、写标书、 招标文件分析、评分标准优化 时使用此技能。
code-quality-review
by bahayonghangRun a code quality review focused on maintainability, structure, abstraction quality, file growth, branching complexity, boundary cleanliness, and refactoring opportunities. Use when the user asks for code quality review, code review, maintainability review, architecture quality review, PR code quality feedback, 代码质量审查, 代码质量 review, 可维护性审查, 架构质量审查, or review comments about code structure. Do not use for pure security review, formatting-only review, performance profiling, or implementation tasks unless the user also asks for a code quality review.
implementation-notes
by bahayonghangUse when implementing a multi-step spec, PRD, design doc, GitHub issue, or approved plan where decisions, deviations, and tradeoffs accumulate during coding. Maintain a live implementation-notes.md alongside the work capturing design decisions (choices made where the spec was ambiguous), intentional deviations from the spec, alternatives considered and rejection criteria, and open questions for human review. Use proactively the moment implementation of a written spec begins, before the first edit. Triggers include 实施记录, 实现笔记, 决策日志, 边写边记, 按这个 spec 实现, 按这个计划做, implementation log, decision log, spec divergence, implement this plan. Do NOT use for single-file fixes, pure investigation, one-off scripts, work without a written spec to deviate from, or simple tasks where one commit captures the reasoning.
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.