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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tilelang-build
by tile-aiRepository-specific build, rebuild, install, and test instructions for tilelang. Use when working in the tilelang repository and the correct commands are needed for building from source, reinstalling after changes, or running project tests.
tilelang-tvm-ir
by tile-aiUse when editing TileLang C++ passes or TVM TIRX code that handles ObjectRef/NodeRef types such as For, Buffer, Var, SBlock, Stmt, PrimExpr, or their *Node raw node counterparts; especially when choosing function parameters, optional values, identity maps/sets, or equality checks.
tilelang-pass-generate
by tile-ai根据 pass-design.md 与 workflow 分析结果生成 TileLang-Ascend Pass 的最终实现代码（不含 UT/ST）。先输出实现骨架文档（pass-impl-skeleton.md）确认框架设计，再生成 C++ 实现、Python 封装、Pipeline 接入，并完成最小冒烟验证。测试生成由后续独立 skill 负责。触发关键词：实现 Pass、生成 Pass 代码、Pass 编码、根据设计文档实现 Pass、写 Pass 代码、落地 Pass、新增 Pass 实现。
tilelang-pass-design
by tile-ai根据 Pass 需求生成 TileLang-Ascend Pass 设计文档（pass-design.md）。涵盖 Pass 定位分析（Phase 1/2归属、Pipeline位置、依赖关系）、IR 变换设计、C++ 实现方案、测试方案、风险分析等。触发关键词：设计 Pass、Pass 设计文档、写 Pass、实现 Pass、添加 Pass、新建 Pass、Pass 设计、开发 Pass。
tilelang-pass-analyzer
by tile-aiTileLang Pass 功能分析与对比工具。触发条件：(1) 用户询问特定 pass 的功能、作用、原理、实现细节，关键词包括"XXpass是干什么的"、"介绍XXpass"、"XXpass的功能"、"XXpass怎么实现的"、"分析XXpass"；(2) 对比两个 pass 的差异、联系；(3) 查询某类 pass 列表；(4) 模糊匹配 pass 名称。支持精确匹配和模糊匹配，返回由浅入深的 Markdown 分析报告，包含伪代码、IR 变换示例、代码位置等。
tilelang-submodule-pull
by tile-aiAutomatically pull tilelang repository and its third-party code. Provides scheduled pull script supporting git pull --recurse-submodules and git submodule update --init --recursive with automatic error detection and retry. Triggers when user mentions "重新拉取三方库", "自动重拉", "重新拉取子模块", "auto retry pull", "pull submodules", "update third-party libs", "retry pulling third-party libs", "auto pull code" or similar keywords.
tilelang-error-fixer
by tile-aiTileLang-Ascend 错误诊断、调试与修复技能。融合了 GDB 故障定位、IR Dump 分析、目标代码倒推（AOT）的诊断能力。提供从环境检查、错误定位、AOT验证到 C++ Pass 修复的端到端工作流。
tilelang-perf-optimization
by tile-aiTileLang 算子性能调优与潜在性能劣化模式检查。提供性能数据采集、瓶颈诊断、优化实施、效果验证能力；也用于生成或评审算子时对照常见性能劣化模式示例检查当前 kernel 代码。触发：算子精度通过后需要优化性能、性能不及预期时。
tilelang-pass-workflow-analyzer
by tile-aiTileLang Ascend Pass 工作流分析。用于理解 Pass 之间的关系、执行顺序、数据依赖，以及帮助定位新 Pass 应该在哪里添加。触发时机：用户询问 Pass pipeline、Pass 执行顺序、Pass 之间的关系、如何添加新 Pass、Pass 依赖关系、"Pass 工作流"、"Pass 顺序"、"在哪里加 Pass" 等关键词时。
tilelang-github-operations
by tile-aiGitHub 操作指南集合。支持的操作：(1) PR 工作流：提交代码、创建 Pull Request；(2) GitHub CLI 配置：安装、认证、Token 管理。触发关键词：PR、pull request、push、commit、gh 命令、GitHub CLI、提交代码、创建 PR 等。
tilelang-a5-sim-convert
by tile-ai将 tilelang example 脚本转换为可在 A5 camodel 仿真器上直接运行的版本。输入脚本路径，输出一个新的 *_sim.py 文件，不覆盖原始文件。触发：仿真运行、camodel、A5 仿真、sim 模式、转换脚本为仿真、不需要 NPU 跑 kernel、simulate A5。
tilelang-ascend-tile-api
by tile-aiTileLang-Ascend 新增 Ascend 专属 T.tile.xxx 小 API 的端到端开发流程。用户要求新增、封装、暴露、实现或测试 ascend_tile.py 中的 T.tile API / Ascend tile primitive 时必须使用本 skill，尤其适用于需要同时打通 Python 前端、C++ lowering/codegen、Ascend C helper、文档和 CI 测试的任务。
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.