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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debt-survival
by LeoYeAISurvival protocol for when income drops and debts pile up. Covers payment priority hierarchy, creditor negotiation, collector defense using FDCPA rights, validation letters, statute of limitations, settlement strategies, and what actually happens if you stop paying.
usecase-transaction-scan
by CoWork-OSScan recent messages/emails for card transactions and flag suspicious charges.
delinquency-and-collections
by mariourquiaCanonical delinquency-to-resolution playbook. Classifies every case by aging stage, proposes the next-step action per the overlay's playbook, generates draft resident communications with legal-review banners where jurisdictions treat them as notices, opens approval_requests for every gated transition (legal notice, eviction filing, non-standard payment plan, write-off above threshold), and records fair-housing guardrails at each decision point. Runs weekly at the property and on-demand when a single case transitions aging buckets.
invoice-chaser
by Demerzels-labAutomated invoice follow-up sequences that escalate from friendly to firm.
invoice-collector
by Demerzels-labCollect invoices/receipts from Gmail and send a summary email with attachments.
tactical-empathy
by ajbmachonUSE WHEN negotiate, negotiation, salary, deal, difficult conversation, confrontation, give feedback, persuade, convince, influence, get buy-in, tactical empathy, voss, never split the difference, prepare for conversation, practice negotiation, roleplay negotiation, spar. Negotiation and communication expert using Chris Voss methodology. Two modes: Analyze (produces negotiation dossier) and Spar (roleplay counterpart with inline coaching).
oc-norman-overdue-reminders
by luokai0Find overdue invoices and send payment reminders (Zahlungserinnerungen / Mahnungen) to clients.
draft-payment-reminder
by jawwad-aliDraft a payment reminder for overdue invoices with human approval. Creates a Plan file in Plans/ with proposed message for user to approve before sending via WhatsApp.
gws-hebrew-email-automation
by skills-ilGmail automation for Israeli freelancers using the Google Workspace CLI (gws). Use when user asks to draft Hebrew client emails, send payment reminders in Shekels, triage inbox with Hebrew labels, set up Gmail filters for Israeli services, or save drafts for later send that respect Israeli business hours. Key capabilities include bilingual email drafting via gws gmail +send, payment reminder sequences with ILS amounts, Hebrew-aware inbox labeling, and draft-then-send workflows for Shabbat-aware delivery. Do NOT use for non-Gmail email providers, Microsoft Outlook automation, or CRM-level contact management.
invoice-chaser
by GeorgeDoors888Automated invoice follow-up sequences that escalate from friendly to firm. Track unpaid invoices, send timed reminder emails with escalating tone, log payment interactions, and generate AR aging reports. Your agent handles the awkward conversations so you don't have to — preserving cash flow and client relationships while you focus on actual work. Configure invoice tracking, email templates per stage (friendly → firm → final notice), timing rules, and let your agent chase payments 24/7. Use when adding invoices, running payment chases, checking status, or generating accounts receivable reports.
revisar-cliente
by ubntomarConsulta el estado completo de un afiliado (datos, facturas abiertas, saldo, ultimo pago, estado de mora) por cedula, id o nombre. Solo lectura. Usar cuando el usuario diga "revisar cliente", "ver afiliado", "consultar moroso", "estado de cliente" o pregunte por un cliente especifico.
collections-letter
by WinbdaWrite collections letters with escalating urgency. TRIGGERS - Use when user needs help with collections-letter related tasks.
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.