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...
omni
by irahardiantoToken-efficient communication protocol. Activate ONLY when: (1) user explicitly requests it (e.g., "use omni", "be concise", "compress output"), (2) dispatched as a sub-agent in /workflow-team pipelines where token budget matters, or (3) agent-to-agent communication via /omni headless modifier. Never activate by default in normal conversations — users expect natural language responses unless they opt in. Compresses prose form while preserving 100% technical accuracy. Code blocks, tool calls, file paths, and data are NEVER compressed.
mobile-design
by irahardiantoGenerates distinctive, production-grade mobile interfaces for Flutter and React Native. Prioritizes platform-native patterns, adaptive layouts, and fluid motion. Use when building mobile apps, screens, widgets, or when the user requests to style or create visually striking mobile UI.
incident-response
by irahardiantoStructured incident workflow: severity classification, triage, diagnosis, mitigation, postmortem, and prevention. Template-driven with blameless review.
adr
by irahardiantoArchitecture Decision Record skill for documenting significant architectural decisions with context, options, and consequences. Use during the Research phase when choosing between approaches, or whenever the user asks to document an architectural decision.
debugging-protocol
by irahardiantoComprehensive protocol for validating root causes of software issues. Use when you need to systematically debug a complex bug, flaky test, or unknown system behavior by forming hypotheses and validating them with specific tasks.
embedded-systems
by irahardiantoResource-constrained development, real-time patterns, interrupt handling, memory management, RTOS patterns, and hardware abstraction layers.
frontend-design
by irahardiantoGenerates distinctive, production-grade frontend interfaces and artifacts (React, Vue, HTML/CSS). Prioritizes bold aesthetics, unique typography, and motion to avoid generic designs. Use when building websites, landing pages, dashboards, posters, or when the user requests to style, beautify, or create visually striking UI.
guardrails
by irahardiantoPre-flight checklist and post-implementation self-review protocol. Use before generating any code (pre-flight) and after writing code but before verification (self-review) to catch issues early.
ml-engineering
by irahardiantoML pipeline design, feature engineering, model training/serving, experiment tracking, model validation, and MLOps principles.
parallel-dispatch-merge
by irahardiantoSafe, sequential merge protocol for integrating N parallel worktree branches back into main. Defines merge ordering, quality gates between merges, conflict classification, and the updated worktree naming convention for multi-instance dispatches.
payment-integration
by irahardiantoPCI DSS compliance, payment gateway integration, tokenization, webhook reliability, idempotency, fraud prevention, and subscription billing.
perf-optimization
by irahardiantoProfile-driven performance optimization protocol. Use when profiling data (CPU, heap, trace) is available or when the user requests performance analysis. Covers methodology, pattern catalog, safety invariants, and when-to-stop heuristics. Language-specific tooling is in languages/*.md.
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.