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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cpu-optimization-x64
by mindspore-aix64 CPU 架构性能优化技巧、SIMD/AVX 向量化、数值稳定性和调试策略
triton-ascend-performance-improvement
by mindspore-aiTriton Ascend 性能优化实战经验。从批量自适应搜索中提炼的通用优化模式，覆盖 tile 调优方法论、内存加载优化、reduction 优化、隐式广播、多 Pass 合并、数据访问重构等。
pypto-optimization
by mindspore-aiPyPTO 性能优化规则与调参顺序。适用于需要优化 tile/loop/归约性能、比较不同 tile 方案、解释同一算子不同 tile 性能差异（尤其 softmax/logsoftmax/reduction/norm/loss）的场景
pypto-case-loss-crossentropy
by mindspore-ai模式 D 示例：Loss — CrossEntropyLoss，展示多输入 kernel、两段 tile、softmax+gather+sum、标量输出
coder-agent
by mindspore-ai代码生成Agent，负责将设计方案转换为可执行代码
triton-ascend-case-reduction-weighted-swiglu
by mindspore-ai3D融合算子（Weighted SwiGLU Backward）优化：Reshape降维将前两维合并简化并行策略，行二次切分避免超UB，在优先占满UB前提下为reduce轴分配较大切分尺寸，grid数较大时可能性能更优，适用于3D张量逐元素+reduce融合的场景
skills-creator
by mindspore-ai用于从对话历史中提取可复用知识并生成新的 OpenCode skill。当用户要求： (1) "凝练当前上下文" 或 "总结上下文为skills"; (2) "任务成功" 后希望保存经验; (3) 查看 "available skills" 并希望创建新技能时使用。将有价值的发现转化为可重复使用的技能。
kernel-workflow
by mindspore-aiAI Kernel 算子生成与优化工作流程。当用户需要生成、验证或优化 kernel 算子时使用此 Skill。支持 Triton、CUDA C、C++、TileLang等多种后端 DSL。
kernel-designer
by mindspore-ai算子算法草图设计 Skill — 负责根据任务需求设计高质量的算法草图（sketch），提供伪代码形式的算法方案、优化建议和实现策略。 支持多种 DSL：triton_cuda、triton_ascend、cpp、cuda_c、tilelang_cuda、pypto。 支持 Hint 模式（参数空间配置）。
kernel-generator
by mindspore-ai算子内核代码生成 Skill — 负责算子实现的全部智力工作：方案讨论、代码生成、基于反馈修改。 支持多种 DSL：triton_cuda、triton_ascend、cpp、cuda_c、tilelang_cuda、pypto。
kernel-verifier
by mindspore-ai算子代码验证 Skill — 静态代码检查 + 精度对比验证。 包含两阶段验证：先做零成本静态检查（语法、编译、import、DSL 合规性）， 通过后再对比框架实现与生成实现的输出一致性。 支持多框架（torch / mindspore）、多后端（cuda / ascend / cpu）。
kernel-agent-overview
by mindspore-aiKernelAgent 工作流程与用户交互指南
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.