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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external-gitcode-ascend-modelscope-cli
by ascend-ai-codingModelScope CLI 模型与数据集下载工具。当用户需要从 ModelScope 下载模型或数据集、批量下载模型、校验文件完整性、统计模型参数量、或进行网络诊断时使用。
npu-torchair-infer
by ascend-ai-codingMigrate any HuggingFace model to Ascend NPU torchair graph mode (torch.compile) and benchmark it for accuracy and performance against NPU eager and CPU eager. Use when running, compiling, or benchmarking HF models (vision, text, image-text encoders such as SigLIP2, DINOv3, ViT, CLIP, Qwen-VL, SAM) on Ascend 910B/CANN with torch_npu and torchair; when a torch.compile graph-mode run on NPU fails (Dynamo TorchRuntimeError, unsupported op, interpolate/contiguous errors); or when comparing torchair vs npu_eager vs cpu with cosine similarity, max abs diff, and p50/p95/p99 latency.
pytorch-profiling-collection
by ascend-ai-coding使用 torch_npu.profiler 在非 MindSpeed-LLM 与非 MindSpeed-MM 的训练/推理脚本中采集 Ascend NPU Profiling 数据。覆盖 level0/level1/level2 采集级别、训练循环/单次推理/指定代码段三种方式，支持 .py 和 .sh 脚本路径。当用户需要性能采集、性能分析、查看算子耗时、定位训练瓶颈时使用。触发关键词：profiling、性能采集、性能分析、算子耗时、瓶颈定位、torch_npu.profiler。
external-cannbot-model-model-infer-precision-debug
by ascend-ai-coding基于 PyTorch 框架的昇腾 NPU 模型推理精度问题诊断技能。当前主要覆盖 KVCache / FlashAttention 相关精度问题，包括 Prefill/Decode 对齐、cache 更新错误、slot/block mapping 错误、attention 路径切换后的精度异常等。触发场景：优化改造后精度验证未通过、模型输出与基线存在显著偏差、Prefill 和 Decode 精度表现不一致、出现 NaN/Inf、量化模式下精度异常放大等。
external-cannbot-model-model-infer-kvcache
by ascend-ai-coding基于 PyTorch 框架的昇腾 NPU 模型推理 KVCache 优化技能。分析和优化 LLM 文本推理模型中的 KVCache 实现，包括连续缓存、分页注意力（Paged Attention）配合 FA 融合算子、MLA 压缩缓存。触发场景包括：KVCache 管理实现、paged attention、KV 压缩、FA 融合算子、OOM/性能问题、block_table/slot_mapping 构造。基于本仓库已有模型的 KVCache 实现经验，按模型类型和场景推荐最佳方案。
rl-msprobe
by ascend-ai-coding自动化 verl msprobe 精度数据采集；开始前检查/预装 msprobe（pip install mindstudio-probe）。自动识别三种模式：(1) 训练采集——global_profiler + precision_debugger stages；(2) 推理采集——vLLM/SGLang rollout dump；(3) 训推一致性——engine patch + PROMPTS_ONLY。触发词：verl dump、msprobe、mindstudio-probe、训练采集、推理采集、训推一致性、PrecisionDebugger。
external-cannbot-ops-ascendc-regbase-best-practice
by ascend-ai-coding当需要为 DAV_3510 RegBase 算子确认 API 约束、实现结构、排查常见陷阱或选择真实参考算子时使用。
external-gitcode-ascend-ssh-connection
by ascend-ai-codingSSH远程开发套件，连接管理、命令执行、文件传输、部署、隧道、调试
external-gitcode-ascend-msverl-daily-regression-triage
by ascend-ai-codingTriage a daily msverl regression run by reading the baseline comparison log, stopping on success, extracting the most relevant training failure evidence from the daily training log when needed, collecting recent commits from verl main and MindSpeed master, and ranking the most likely culprit commits with concise fix-direction guidance.
external-gitcode-ascend-verl-async-dapo
by ascend-ai-codingVerl 单异步 DAPO 训练配置生成器。触发场景：(1) 启动单异步 DAPO 训练 (2) 生成训练脚本 (3) 配置特性参数 (4) 训练前检查。**特性策略**：用户未指定时默认开启性能特性（flash_attn/dynamic_batch/remove_padding/gradient_checkpointing），显存特性（offload/recompute）默认关闭。OOM 时自动追加显存特性重试。**训练监控**：启动后输出 SwanLab 链接供用户自行查看，仅在错误时通知用户。**依赖 skill**：SwanLab 配置通过 swanlab-setup skill 提供。
external-gitcode-ascend-verl-feature-deploy
by ascend-ai-codingVerl 分布式训练服务一键拉起与配置。触发场景：(1) 用户要启动 Verl 训练任务或部署 RLHF/DAPO 训练环境 (2) 在 NPU 集群上拉起 Verl 训练容器 (3) 配置 Ray 集群和 SwanLab 监控 (4) 根据 7 位二进制掩码灵活配置加速特性。支持 Qwen3-8B 等 Megatron 模型的 DAPO 训练全流程。
external-cannbot-graph-torch-npugraph-ex-knowledge
by ascend-ai-codingnpugraph_ex（aclgraph）模式使用指南。采用 Capture & Replay 方式将算子任务下沉至 Device 执行，减少 Host 调度开销，适用于固定 shape 在线推理低延迟场景。涵盖模式配置、FX Pass、编译缓存、多流并行、内存复用、静态 Kernel 编译、限核、性能优化、调试定位、自定义算子入图等。关键词：npugraph_ex、aclgraph、backend="npugraph_ex"、capture、replay、reduce-overhead、config.aclgraph_config。
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.