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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triton-ascend-case-vector-elemwise-bench-atlas-a3
by xchang1121Atlas A3 上 Triton vector 一元/二元 `tl.*` ：fp32/fp16/bf16 三种 dtype 下各算子端到端 time (ms)；并给出"语义等价、精度对齐"前提下应替换的 triton API 与推荐写法。例如 fp32 上 `tl.exp2(x)` 比 `tl.exp(x*LN2)` 性能表现得要差，可以选择使用 `tl.exp(x*LN2)`。
triton-ascend-a5-api
by xchang1121Atlas A5 (Ascend950) 专属 Cube/Vector 协同编程接口。涵盖 Buffer Language (bl.alloc/to_buffer/to_tensor/subview) 和 Ascend Language (al.scope/fixpipe/sync_block_set/sync_block_wait/sub_vec_id/copy) 的完整用法与同步操作。适用于需要在 A5 硬件上进行 Cube 计算后交给 Vector 做后处理（如 bias/relu/softmax）的高性能内核编写场景。
triton-ascend-case-vector-elemwise-bench-atlas-a5
by xchang1121Atlas A5 上 Triton vector 一元/二元 `tl.*`：fp32/fp16/bf16 三种 dtype 下各算子端到端 time (ms)；并给出"语义等价、精度对齐"前提下应替换的 triton API 与推荐写法。
triton-ascend-a5-attention
by xchang1121适用于 A5（Ascend950）注意力(attention)机制算子的串行优化指南。当算子的核心计算是 Transformer 风格的注意力运算，且目标硬件为 Ascend950 时应选择此指南。涵盖 Cube/Vector 操作、al.fixpipe/bl.alloc 数据流、串行同步机制（sync_block_set/wait）、Flash Attention 四阶段分解、P 矩阵 ND→NZ 格式转换等 A5 专用技巧。本文档给出的是 Cube/Vector 串行交替执行版本，不适用于不含注意力结构的普通矩阵乘法或归约运算。
triton-ascend-a5-matmul-vector
by xchang1121适用于 A5（Ascend950）Cube/Vector 协同编程的 MatMul + Vector 后处理融合优化指南。当算子的核心计算是矩阵乘法后接逐元素操作（如 bias 加法、ReLU 激活、残差加、量化等）时应选择此指南。本指南采用两段式调度：一个 cube scope 整段循环 + 一个 vector scope 整段循环 + 单 buffer + 一对显式同步事件。覆盖 Cube/Vector 数据流、ROW_SPLIT 拆分、sub_vec_id 索引、显式 sync_block_set/wait 配对、plain matmul kernel 推荐写法、关键约束速查等。不适用于纯 Vector 逐元素运算、也不适用于无后处理的纯 MatMul。
triton-ascend-case-vector-mask-i32
by xchang1121比较两侧本身多为 `tl.int32`（offset / attn_arg 等），`arith.cmpi` 产出的是 `i1`；多段结果在 **`i1` 张量上做 `&`/`|`** 时， lowering 会在每条逻辑附近插入 **`extui`/`trunci`** 与 `select` 对齐；**每段比较后立刻 `.to(tl.int32)`**，让整条 mask 在 **`i32` 0/1 上 `&`/`|`**，后端更易连续处理 **`vand.i32`/`vor.i32`**。
triton-ascend-case-matmul-large-k
by xchang1121矩阵乘法矩阵乘法 A[M, K] @ B[K, N] = C[M, N]中，大K维度矩阵乘法(K>>M,N)优化：针对M/N较小但K极大(如M=N=256,K=131072)的场景，Split-K切分K维度并行化、Workspace+Reduce替代全局同步，实现显著性能提升
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.