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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submit-credit-application
by aliyun授信申请提交辅助技能。自动盘点客户档案中的笔记与材料,对照银行贷前尽调材料标准评估完备性,检查案件冲突与授信到期状态,生成结构化评估报告并引导客户经理确认提交。当用户需要提交授信申请、发起审批流程、续贷申请、追加授信或评估材料是否齐全时使用此技能。触发词包括:"提交授信申请"、"发起审批"、"submit credit"、"续贷申请"、"追加授信"、"评估材料"、"授信申请提交"、"发起授信流程"。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泛泛咨询。
client-communication
by alexcloweBorrower education, rate explanations, closing process walkthroughs, and milestone updates
loan-officer-assistant
by itgoyo综合信贷经理助手，涵盖借款人接待、资格预审、文件收集、流水线管理、合规追踪、利率报价和过户协调，适用于住宅、商业和消费信贷。
ocean-bank-deal-docs
by jndeweyGenerate Bank commercial real estate loan documents from a Credit Approval Summary (CAS). Use this skill whenever the user asks to generate, draft, or produce loan documents, commitment letters, promissory notes, mortgages, or any Bank closing documents from a CAS. Also trigger when the user references a CAS file, asks to "run" a deal, or mentions Bank document automation. This skill edits Word templates via tracked changes — it does NOT generate documents from scratch.
agency-loan-officer-assistant
by anavvanzinComprehensive loan officer assistant for mortgage and lending professionals — covering borrower intake, pre-qualification, document collection, pipeline management, compliance tracking, rate quoting, and closing coordination across residential, commercial, and consumer lending
pre-approval-email
by AStyer8345Drafts a congratulatory pre-approval email to a borrower via Outlook (Zapier). Adam uploads a pre-approval letter PDF — Claude extracts key fields and populates the hardcoded HTML template, then creates an Outlook draft for Adam to review and send. Use this skill whenever Adam says "pre-approval email", "send pre-approval", "draft pre-approval email", "pre-approval letter email", "borrower got approved", "got pre-approved — send them the email", or uploads a pre-approval letter PDF and wants to draft the borrower email. Also trigger on phrases like "pre-approval email for [name]", "send [name] their pre-approval email", "[name] is pre-approved send the email", or "preapproval email".
identity-verification
by hpsgdVerify that a named individual is who they claim to be, or resolve ambiguity between people sharing a name. Anchors on verifiable claims and cross-references independently. Writes a conforming report (per report-conventions) to <engagement_dir>/identity-verification/<lastname>-<firstname>.md. Requires authorisation gate.
knowledge-curator
by jb-612Manage bank docs and loan tracks in the knowledge base
mortgagetasksai
by laudoluxDevAccess 80+ AI-powered skills for mortgage loan officers, processors, and underwriters. Use when: user asks about borrower intake, loan applications, pre-approval, underwriting, closing disclosures, TRID compliance, pipeline management, appraisals, or any mortgage lending administration task.
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.