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.
Querying local SQLite index...
folyamatos-ellenorzes
by SzotaszTeljes ellenőrzés
channel-plugin-duplicate-socket
by SzotaszMarveen flottában új channel plugin (slack-channel, telegram, stb.) telepítésekor a user-szintű ~/.claude/settings.json enabledPlugins minden agent-nek loadolja a plugin server-t, és ha az egy Socket Mode connection-t nyit (Slack), akkor TÖBB agent egyszerre nyit ugyanazon a workspace-en kapcsolatot. Akkor használd, ha egy agent Socket Mode-os channel plugin-t használ és inbound event-ek "fele eltűnik".
dream-engine
by SzotaszÉjszakai analízis-loop az aznapi memóriákról, naplóról és kanban-állapotról. Generál 4 priorizált akció-javaslatot reggelre.
memoria-heartbeat
by Szotasz30 percenként átnézi a beszélgetést, menti a fontosat, és skill-eket generál ha volt komplex munka
reggeli-napindito
by SzotaszReggeli összefoglaló: email, naptár, AI hírek, plus Dream Engine top-of-message
bumblebee-hygiene-scan
by SzotaszWeekly supply-chain hygiene scan (Perplexity Bumblebee). Monday 09:00. Inventories installed packages, MCP configs, and extensions, then matches against known supply-chain threat catalogs. Telegram alert ONLY if findings > 0.
kanban-audit
by Szotasz4 óránkénti kanban-tábla audit. Tisztítás (7+ napos done archiválás) + beakadt task-ok számon kérése (előző audit óta nem mozdult in_progress -> ping az assignee-nek).
ai-fleet-project-execution
by SzotaszAI fleet project execution (orchestrator=PM, marketing agent, backend dev agent, video agent). Fast-iteration architecture pivots and inter-agent task delegation across multi-hour sessions. Use when a user assigns a multi-agent project with "take it as a team" instruction.
fleet-helper
by SzotaszShared, dependency-free Python helpers for the agent fleet - dashboard API (memory, messages, kanban), Telegram MarkdownV2 escaping, and rule-based Mail.app triage. Use to do deterministic work (fetch/filter/SQL/format/escape) in Python instead of burning model tokens doing it in the LLM turn. The dashboard token is read from store/.dashboard-token at call time, never hardcoded.
github-pr-rebase-merge
by SzotaszMerge a stack of GitHub PRs sequentially when they share files and will cause cascading conflicts. Triggers when user says "merge the PRs sorban" or similar, and the PRs come from external forks (cannot push back to PR branch).
handoff
by SzotaszGenerate a HANDOFF.md context transfer document for session continuity. Use when switching sessions, handing off to another agent, or preserving complex task context before a context window reset. Trigger on "/handoff" command or "handoff:" prefix in inter-agent messages.
retrospective
by SzotaszAnalyze the current session for improvement opportunities in skills, memory, and workflow. Spawns a sub-agent for unbiased analysis. Use when a session involved complex problem-solving, error recovery, user corrections, or multi-step workflows. Trigger on "/retrospective" command or at session end after significant work.
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.