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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credit-analysis
by HKUDS固收与信用分析：信用债评级、利差分析、违约风险评估、城投债研究、可转债定价与策略。
grundbegriffe-cashflow-kreditlinien
by KlotzketteLiqui Grundbegriffe Cashflow Kreditlinien im Plugin Liquiditaetsplanung: prüft konkret Liquiditaetsplanung Grundbegriffe, Kreditlinien pruefen, Debitorenseite optimieren. Liefert priorisierten Output mit Norm-Pinpoints, Risikoampel und nächstem Schritt.
kreditlinien-pruefen
by KlotzketteKreditlinien pruefen: Kontokorrent, Investitionskredit, Avalkredit, Factoring-Linie, Lieferantenkredit. Ausnutzungsgrade, Tilgungsplaene, Sicherheiten, Covenants. Pruefraster für Bankgespraech.
liqp-warenkredit-skonto-szenarien-spezial
by KlotzketteSpezialfall Warenkredit und Skontostrategien in der Krise: Lieferantenverhandlung, Vorkasse, verlaengerter Eigentumsvorbehalt, Factoring. Pruefraster für Treasury.
mahnstufen-debitoren
by KlotzketteDebitorenseite optimieren: DSO-Kennzahl, Mahnstufen, Skontostrategie, Factoring-Optionen, Forderungsausfallversicherung. Empfehlung: 3-Wochen-Forecast getrennt nach Kategorie 'sicher / wahrscheinlich / fraglich'.
financial-analyzing
by huangjia2019Analyze financial data, calculate financial ratios, and generate analysis reports. Use when the user asks about revenue, costs, profits, margins, ROI, financial metrics, or needs financial analysis of a company or project.
financial-analyzing
by huangjia2019Analyze financial data, calculate financial ratios, and generate analysis reports. Use when the user asks about revenue, costs, profits, margins, ROI, financial metrics, or needs financial analysis of a company or project.
credit-due-diligence
by aliyun企业信贷尽职调查报告生成技能。自动获取企业工商、征信、财务、经营等多维度数据，按银行贷前尽调标准进行系统性分析，覆盖企业基本面、公司治理、关联关系、财务健康、经营真实性验证、主体资信、风险评估及授信建议，输出完整结构化尽调报告。触发词包括："尽职调查"、"尽调报告"、"due diligence"、"贷前调查"、"credit investigation"、"做个尽调"、"写尽调报告"。不适用于：贷后管理报告、风险分类调整、不良资产处置、或个人信贷尽调。
admission-rules-scan
by aliyun信贷产品准入规则扫描技能。基于客户画像与目标产品的风控规则库,逐条匹配准入条件,输出"符合/不满足/需确认"三态评估报告,标注一票否决项与差距改进建议。用于授信方案设计阶段的主动预检,帮助客户经理提前识别准入障碍。当用户需要评估客户是否符合某产品准入条件、扫描准入规则、判断客户资质,或说"这个客户能做流贷吗"、"准入扫一下"、"产品准入评估"、"admission scan"、"规则扫描"、"准入检查"时使用此技能。不适用于:贷后管理、风险分类调整、不良资产处置、授信审批决策、或无具体客户背景的一般风控咨询。
ai-risk-planning
by aliyun信贷风控任务规划技能。基于贷款申请信息、输入材料(企业基本信息/尽调报告/年报)及宏观信贷策略,以8大风险筛查维度(固定项)为基础框架,叠加行业风险规则(行业项)和动态专项分析建议(针对项),自顶向下规划风险分析任务和数据采集任务,输出结构化JSON任务规划清单。当用户提供贷款申请信息并要求规划风控审查任务、生成尽调清单、制定信贷审查方案时使用此技能。触发词包括:"风控任务规划"、"risk planning"、"尽调清单生成"、"信贷审查方案"、"风险分析任务"、"风控规划"、"审查任务规划"。不适用于:贷后管理、风险分类调整、不良资产处置、授信审批决策、或无具体贷款申请背景的一般风控咨询。
credit-case-intake-check
by aliyun授信申请案件进件合规检查技能。对客户提交的授信申请材料进行完整性校验、基本信息有效性核查、申请金额合理性审查,输出材料清单状态(齐全/缺失)、缺失项严重度分级(阻断/建议补充/信息提示),并给出补充指引。触发词包括:"进件检查"、"进件材料检查"、"case intake check"、"帮我检查一下进件材料"、"这个案子材料齐了吗"、"材料校验"、"进件合规检查"、"检查授信材料"。不适用于:贷后管理、风险分类调整、不良资产处置、授信审批决策、客户经理访前分析(请使用pre-visit-credit-analysis)、或无具体案件背景的一般合规咨询。
credit-collateral-risk-mgmt
by aliyun授信押品风险评估与管理技能。覆盖准入筛查、评估合理性审查、登记有效性核验、存续期动态监控、处置合规性检查五大环节,识别押品充足性不足、价值虚高、权属瑕疵、重复担保等风险,输出结构化押品风险报告与分级预警。触发词包括:"押品风险评估"、"押品情况查询"、"抵押率检查"、"collateral risk assessment"、"查一下押品情况"、"这个抵押物有问题吗"、"抵押率超了吗"、"押品合规检查"。不适用于:贷后风险分类调整、不良资产最终核销、押品现场勘估(需第三方评估机构)、或无具体押品背景的一般合规咨询。
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.