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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ci-perf-analysis
by OpenXiangShan用于从 GitHub Actions 的 gem5 性能 CI 中定位 summary、score.txt 和归档目录，并结合本地 gem5_data_proc 对 spec06/spec17 结果做 weighted score、benchmark 子项对比和通用 stats 归因。适用于用户给出 run URL/run id、commit、workflow run，或要求分析 CI 跑分变化来源时。
branch-predictability-triage
by OpenXiangShan用于分析分支 PC 对应的 ELF / 函数 / 源码语义，并判断该分支更像是语义上天然难预测，还是更像预测器没有学好。适用于 SPEC06 checkpoint、benchmark ELF、topMispredictsByBranch.csv、单个 branch PC 归因。
tage-trace-workflow
by OpenXiangShan用于编译 XiangShan RTL trace 版 emu、运行 RTL/gem5 的 TAGE trace、并对 gem5 stats/bp.db 与 RTL CondTrace 做共口径聚合比较。适用于 coremark、SPEC checkpoint slice、sjeng_22213 等单切片对拍与首轮分叉定位。
run-cpt-regression
by OpenXiangShan仅负责批量运行 gem5 checkpoint（1次或2次）。不做任何分析。
rtl-tage-alignment
by OpenXiangShan用于比较 XiangShan RTL 与 gem5 BTBTAGE/TAGE 在 allocation、useful、reset 和 update path 上的语义差异，并结合 CI、原始 stats 和 benchmark 结果定位性能变化来源。适用于用户要求检查“和 RTL 是否对齐”、分析 TAGE 相关性能变化、设计最小 A/B 验证时。
cpu-performance-modeling
by OpenXiangShan由 CPU 性能模拟器建模经验蒸馏而成的行为级建模规范。强调高性能实现、参数化资源模型、细粒度/粗粒度取舍、控制流抽象、复杂度约束和 stats 驱动验证。适用于新增或修改性能模型、把 RTL 机制抽象成模拟器模型、设计参数化微结构模型、决定哪些行为需要细粒度建模、评审 AI 生成的 CPU 性能模拟代码时。
mgsc-table-probe
by OpenXiangShan分析香山 MGSC/SC 在前端微测试上的效果。适用于以下场景：(1) 用 off/l_only/g_only/i_only/full 等 A/B profile 批量运行 mgsc_test；(2) 比较不同 profile 下的 topMispredictsByBranch.csv 和 stats.txt；(3) 使用 bp.db 里的 MGSCTRACE 将每个分支的收益/损失归因到具体 SC 表；(4) 决定如何为 Global 或 IMLI 表设计新的测试。
gem5-manual-perf-trigger
by OpenXiangShan用于在本地通过 `gh` 远程触发 OpenXiangShan/GEM5 的 `manual-perf.yml`，并在触发后检查 run 是否正常创建。适用于用户明确要求触发 `manual-perf`、组装 `gh workflow run` 命令、校验 workflow 输入项、或确认触发后的 Actions run 状态。
frontend-pmu-analysis
by OpenXiangShan仅做 BPU 计数器提取与批量汇总（机器可读 JSON/CSV）。配置文件只需要写原始 stats 计数器名。
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.