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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video-review
by htekdevReview and inspect video files using Gemini AI vision. Use this skill when asked to inspect, review, analyze, describe, or understand what's happening in a video file. Supports custom prompts for targeted analysis.
late-api
by htekdevManage Late.co social media scheduling API — list, reschedule, bulk delete, and sync scheduled posts. Use this skill when asked to manage scheduled posts, clean up the Late queue, reschedule posts, inspect Late API state, or troubleshoot Late API issues.
npm-publish
by htekdevPublish packages to npm registry. Use this skill when asked to publish, release, or deploy a package to npm. Handles granular access token creation, authentication, and publishing with 2FA bypass.
release
by htekdevCreate a new version release for vidpipe. Use this skill when asked to release, version, tag, or create a new version. Handles version bump, changelog generation, GitHub release, and npm publishing.
emergency-protocol
by htekdevFamily emergency response procedures — parent notification, medical info relay, child safety escalation, and urgent contact protocols. Use when user says "emergency", "urgent medical", "notify other parent", "emergency contact", "911", "hospital", "safety alert", "allergies", "medications list", or any family emergency situation.
shopping-trip-closeout
by htekdevPost-shopping-trip workflow — check off purchased items, log expenses to budget tracker, and sync inventory. Use when user says "got groceries", "back from shopping", "bought the items", "went to store", "shopping done", "picked up groceries", "HEB run done", "Costco trip", or any indication a shopping trip was completed.
work-hours-filtering
by htekdevWork-hours task filtering and availability detection — suppresses physical tasks during meetings, serves only digital tasks during work blocks, and determines free gaps for task delivery. Use when checking availability, filtering tasks by work status, deciding what to serve during meetings, or any agent says "is he in meetings", "work hours", "suppress chores", "can I nudge now", "is he available", "free block", "meeting check".
finance-task-lifecycle
by htekdevFinance task lifecycle management — auto-pay bill cleanup, payment-logged cluster clearing, and bill reminder task creation/cancellation rules. Use when agent says "bill paid", "auto-pay", "payment logged", "clear reminders", "cancel bill task", "finance cleanup", "bill already paid", "duplicate payment task", or handles any bill-payment task lifecycle.
correction-persistence
by htekdev3-layer lesson persistence pattern — when {{PARENT_1}} or {{PARENT_2}} corrects behavior, persist the lesson across store_memory + standing-orders.md + copilot-instructions.md. Use when user says "persist lesson", "save correction", "never repeat", "remember this rule", "update instructions", "learned behavior", "permanent fix", or any behavioral correction that must be persisted.
grams-only
by htekdevAll food measurements must use grams exclusively. {{PARENT_1}} uses a kitchen scale — never use tablespoons, ounces, cups, or volumetric units. Use when providing recipes, meal suggestions, ingredient quantities, or any food-related measurements.
engagement-cadence
by htekdevEngagement-respecting message cadence — anti-nag protocol, response tracking, escalating back-off, fatigue detection, and per-recipient adaptation. Use when agent says "check engagement", "skip cycle", "unanswered messages", "cadence check", "should I message", "engagement fatigue", "back-off", "nudge frequency", "message spacing", or any periodic messaging decision.
task-management
by htekdevCentralized task management procedures for all agents — creating, tracking, updating, and completing tasks via the action-tracker extension. Use when managing tasks, creating action items, checking task status, cleaning up stale tasks, or any agent says "create task", "manage tasks", "task status", "clean up tasks", "update task", "complete task", "task lifecycle", "task ownership".
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.