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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1k-bundle-release
by OneKeyHQBundle release workflow — checkout, prepare, pr, diff-check, audit, publish, sync.
1k-feature-guides
by OneKeyHQFeature development guides for OneKey. Use when adding new chains, socket events, notifications, pages, or routes. Covers blockchain integration, WebSocket subscriptions, push notifications, and navigation patterns.
1k-perf-optimizer
by OneKeyHQSystematic performance optimization and regression debugging for OneKey mobile app (iOS). Use when: (1) Fixing performance regressions - when metrics like tokensStartMs, tokensSpanMs, or functionCallCount have regressed and need to be brought back to normal levels, (2) Improving baseline performance - when there's a need to optimize cold start time or reduce function call overhead, (3) User requests performance optimization/improvement/debugging for the app's startup or home screen refresh flow.
1k-i18n
by OneKeyHQInternationalization — translations (ETranslations, useIntl, formatMessage) and locale management. NEVER modify auto-generated translation files.
1k-implementing-figma-designs
by OneKeyHQImplements Figma designs 1:1 using OneKey component library (还原设计稿).
1k-group-think
by OneKeyHQSpawns a team of 3 AI agents with different analytical perspectives to collaboratively analyze a problem, propose solutions, and debate trade-offs. Use when facing bugs, design decisions, architecture choices, or any task that benefits from multiple viewpoints. Agents discuss with each other, then present a comparison table for the user to decide. Triggers on "group think", "multi-agent", "team analysis", "3 agents", "collaborative analysis", "debate solutions".
1k-retrospective
by OneKeyHQAnalyze accumulated bug fix cases and propose updates to the self-testing checklist. Use periodically (weekly/monthly) to evolve quality checks based on real issues.
1k-ui-verify
by OneKeyHQAI-agent-driven UI verification for OneKey. Use to actually drive the running app and confirm a visual/interactive change works — Electron desktop via Chrome DevTools Protocol (CDP) on port 9222 with playwright-core, and React Native (iOS/Android) via callstack agent-device. Triggers on "verify the UI", "drive the app", "screenshot the change", "check it on desktop/simulator", "CDP 9222", "agent-device", "UI 验证", "跑一下看看", "截图确认".
1k-dev-commands
by OneKeyHQDevelopment commands — yarn scripts for dev servers, building, linting, testing, and troubleshooting.
1k-analytics
by OneKeyHQAnalytics event tracking for OneKey. Use when adding tracking events, logging to server, user behavior tracking, or business metrics. Covers the @LogToServer decorator pattern, logger scope/scene architecture, and common pitfalls. Triggers on "埋点", "统计", "打点", "数据追踪", "日志", "analytics", "tracking event", "Mixpanel", "LogToServer", "trackEvent", "defaultLogger".
1k-app-upgrade-test
by OneKeyHQCreate test versions to verify app auto-update functionality and version migration.
1k-auditing-pre-release-security
by OneKeyHQAudits security and supply-chain risk between two git refs with Codex cross-validation. 预发布安全审计（含 Codex 交叉验证）。Use when performing pre-release security audits, supply-chain reviews, or comparing two git refs for security regressions. Triggers on “预发布审计”, “security audit”, “release audit”, “安全预审”.
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.