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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didit-face-search
by didit-protocolIntegrate Didit Face Search standalone API to perform 1:N facial search against all previously verified sessions. Use when the user wants to detect duplicate accounts, search for matching faces, check if a face already exists in the system, prevent duplicate registrations, search against blocklist, or implement facial deduplication using Didit. Returns ranked matches with similarity percentages.
didit-liveness-detection
by didit-protocolDetects liveness from a single selfie image via the Didit standalone API. Use when checking if a person is physically present, detecting spoofing or presentation attacks, implementing anti-spoofing measures, or performing passive liveness verification. Returns liveness score, face quality, and luminance metrics. 99.9% accuracy.
didit-database-validation
by didit-protocolIntegrate Didit Database Validation API to verify personal data against government databases. Use when the user wants to validate identity against government records, verify national ID numbers, check CPF/CURP/DNI/cedula numbers, perform identity database lookups, validate identity documents against official sources, or implement database verification for Latin American or Spanish identity documents using Didit. Supports 18 countries with 1x1 and 2x2 matching methods.
didit-email-verification
by didit-protocolIntegrate Didit Email Verification standalone API to verify email addresses via OTP. Use when the user wants to verify emails, send email OTP codes, check email verification codes, detect breached or disposable emails, check if an email is undeliverable, or implement email-based identity verification using Didit. Supports fraud signals (IP, device, user agent), configurable code length, alphanumeric codes, and policy-based auto-decline for risky emails.
didit-face-match
by didit-protocolIntegrate Didit Face Match standalone API to compare two facial images. Use when the user wants to compare faces, verify face identity, implement biometric comparison, facial recognition, or selfie-to-document matching using Didit. Returns a similarity score (0-100) with configurable decline threshold. Supports image rotation and multi-face detection.
didit-id-document-verification
by didit-protocolVerifies identity documents via the Didit standalone API. Use when verifying a passport, ID card, driver's license, or residence permit, performing OCR extraction, MRZ parsing, document authenticity checks, or KYC document validation. Supports 4000+ document types across 220+ countries.
didit-phone-verification
by didit-protocolIntegrate Didit Phone Verification standalone API to verify phone numbers via OTP. Use when the user wants to verify phones, send SMS or WhatsApp or Telegram codes, check phone verification codes, detect disposable or VoIP numbers, or implement phone-based identity verification using Didit. Supports multiple delivery channels (SMS, WhatsApp, Telegram, voice), fraud signals, and policy-based auto-decline.
didit-proof-of-address
by didit-protocolIntegrate Didit Proof of Address standalone API to verify address documents. Use when the user wants to verify a proof of address, validate utility bills, bank statements, government documents, extract address from documents, verify residential address, implement address verification, or perform PoA checks using Didit. Supports OCR extraction, geocoding, name matching, and multi-page documents.
didit-verification-management
by didit-protocolFull Didit identity verification platform management — account creation, API keys, sessions, workflows, questionnaires, users, billing, blocklist, and webhooks. Use when someone needs to create a Didit account, get API keys, set up verification workflows, create or retrieve verification sessions, approve or decline sessions, manage users, check credit balance, top up credits, configure blocklists, configure webhooks programmatically, handle webhook signatures, or perform any platform administration. 45+ endpoints across 9 categories.
didit-kyc-onboarding
by didit-protocolEnd-to-end KYC (Know Your Customer) identity verification for onboarding real users. Use when someone needs to perform KYC, onboard users with identity verification, verify a person's identity with ID scan and selfie, run a full KYC flow, create a verification session for a user, set up ID + liveness + face match verification, or implement user onboarding with document and biometric checks. Creates a KYC workflow, generates a verification URL, and retrieves the decision.
didit-aml-screening
by didit-protocolIntegrate Didit AML Screening standalone API to screen individuals or companies against global watchlists. Use when the user wants to perform AML checks, screen against sanctions lists, check PEP status, detect adverse media, implement KYC/AML compliance, screen against OFAC/UN/EU watchlists, calculate risk scores, or perform anti-money laundering screening using Didit. Supports 1300+ databases, fuzzy name matching, configurable scoring weights, and continuous monitoring.
didit-biometric-age-estimation
by didit-protocolEstimates a person's age from a facial image via the Didit standalone API. Use when implementing age gating, checking if someone is over 18 or 21, performing age verification for compliance, or detecting underage users. Includes passive liveness check. Supports configurable thresholds, adaptive fallback to ID verification, and per-country restrictions.
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.