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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karpathy-guidelines
by multica-aiBehavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
multica-autopilots
by multica-aiUse when creating, updating, inspecting, triggering, or debugging Multica autopilots. Covers the full chain: schedule/webhook/manual trigger, create_issue vs run_only execution, agent/squad leader admission, runs, created issues/tasks, webhook URL rotation, and side-effect boundaries.
multica-creating-agents
by multica-aiUse when creating, inspecting, or debugging a Multica agent through the `multica agent` CLI or `POST /api/agents` — what each field is, its persisted shape, whether it is metadata-only or consumed by the daemon at claim time, which inputs are validated/rejected, how custom_env secrets are gated, and how skill binding behaves. Not for assigning issues to existing agents or for runtime task prompts.
multica-mentioning
by multica-aiUse when an issue comment needs to @mention someone — link to a person, trigger another agent, hand work to a squad, or broadcast with @all. Documents the verified mention contract: how a mention link is built from a real UUID, the four mention types and exactly what each one enqueues (agent → a run for that agent, squad → a run for the squad leader, member and issue → a rendered link with NO run), comment create/edit preview and suppression, the @all broadcast and how it suppresses the assignee's auto-trigger, and the silent no-op cases (a name where a UUID belongs, a bad/unknown UUID, an already-pending task, an archived agent, a private agent you cannot access). WHETHER to mention — loop avoidance, staying silent on acknowledgements — lives in the runtime brief's Mentions section, not here. This skill is the backend contract only, traced to server/internal/util/mention.go and server/internal/handler/comment.go.
multica-projects-and-resources
by multica-aiUse when creating, inspecting, updating, or debugging Multica projects and project resources. Covers durable project context, github_repo and local_directory resources, how resources affect future agent task context, when to bind repos, and when not to mutate resources.
multica-runtimes-and-repos
by multica-aiUse when inspecting or debugging Multica runtimes, daemon task claiming, agent not running, workdir/session reuse, or repository checkout. Covers runtime online/offline state, daemon heartbeat/claim chain, task-scoped repo checkout, project repo context, local_directory caveats, and safe diagnostic commands.
multica-skill-importing
by multica-aiUse when a user provides a skill URL, slug, or clear intent to import/install a specific skill into the current Multica workspace. Teaches the workspace import API/CLI path (POST /api/skills/import), the supported URL source families, --on-conflict fail|overwrite|rename|skip behavior and structured import results, additive agent binding vs replace-all, and the reserved SKILL.md supporting-file rule. Do not use it to decide which skill the user needs, and never treat an external local installer like npx skills add as the final Multica install.
multica-squads
by multica-aiUse when creating, inspecting, updating, assigning, mentioning, or debugging Multica squads. Explains what squads are, squad/member fields, CLI commands, leader routing, issue assignment, comments, mentions, autopilot behavior, leader briefing, side effects, and product-gap handling.
multica-working-on-issues
by multica-aiUse when working on a Multica issue after the runtime has provided the trigger context — to apply the product contracts the runtime brief does not encode: how PR linking differs from close intent, how to read a linked PR's real state via the pull-requests CLI, which metadata keys are high-signal, what status changes trigger on the server, and how sub-issue create status (todo vs backlog) controls whether assigned agents start immediately.
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.