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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slurm-hold
by Gonglitian在 slurm 集群上拉一个长占用 placeholder job + tmux session + SSH alias，之后用 srun --overlap 复用节点跑实际训练。模板自 hpcc raise 7-day hold 实战。用户说 'hold 节点' / 'slurm 占位' / '长占用' / '抢节点' 时调用。
sync-to-remote
by Gonglitian把本地 project 推到远程 compute host（hpcc / bcc / tasl-labserver）+ 远端 bootstrap auto-production framework + 验证 stubs import 通。给 driver 用，跟 /cross-host-sync (run sync) 互补——后者管 run state，本 skill 管 code state。Use when user says "sync to remote", "部署到 hpcc", "推到集群", "rsync project", "远程跑", "hpcc 上验证", "deploy 上去".
ucr-hpcc-cluster
by GonglitianHelp users work with the UCR HPCC (High Performance Computing Center) cluster. Provides commands for connecting, submitting jobs, managing software, and handling data storage.
cross-host-sync
by Gonglitian把 4 台机器（hpcc / bcc / tasl-7 / tasl-labserver）上正在跑的 run + ckpt 路径 + conda env + dataset 路径同步到 Notion 数据库。一行命令双向同步。用户说 'sync 状态' / '同步到 notion' / 'cross host' / '哪台机器跑啥' 时调用。
driver-findings
by GonglitianDriver Claude（通常通过 /remote-drive 控制 sub-agent CC）跑实战发现新 bug / 配置问题 / 架构选择需重新拍板时，把 findings 标准化写到 .driver_findings_<round>_<context>.md，让 sub-agent 读了去 patch。比 send-keys 长 message 可靠 100 倍。Use when user says "driver findings", "findings file", "round N 反馈", "远程发现", "hpcc 验证发现", "driver patch", "sub-agent 修 bug".
review-ral
by GonglitianIEEE RA-L 论文审稿助手。输入一篇待审稿 PDF，自动完成：初读论文提取关键信息 → 多源文献检索 (Semantic Scholar + WebSearch/arXiv + vec-db) → 并行 agent 深读相关论文 → 带着领域知识精读 待审稿论文 → 输出完整的 RA-L 审稿意见（含评分、推荐、双语评审意见）。审稿风格追求独到犀利， 不千篇一律。Use PROACTIVELY whenever the user asks to review a paper for RA-L, IEEE Robotics and Automation Letters, or says "审稿", "review this paper", "帮我审稿", "写review", "RA-L review", "审一下这篇", "peer review", or provides a PDF and mentions reviewing. Also trigger when user mentions PaperCept, reviewer form, or review deadline.
experiment-plan
by GonglitianTurn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.
ghostty-cjk-input-debug
by GonglitianDiagnose and fix Chinese / Japanese / Korean (CJK) input method (IME) issues in Ghostty terminal on Linux — specifically the snap-distributed Ghostty + fcitx5 + GTK4 + X11 / Wayland combination. Trigger this skill whenever the user reports: 'ghostty 输入不了中文 / 中文打不了 / 输入法不工作 / fcitx 在 ghostty 里失灵', 'ghostty 装好了但输入法没反应', 'snap 应用 + 输入法 / IME 问题', 'GTK4 + XIM 不工作', or wants to mirror a working ghostty + fcitx5 setup from another machine. Also trigger for adjacent symptoms like 'gnome-terminal 能用中文但 ghostty 不行' or 'ghostty 输入候选窗不弹'. The skill provides a 5-pitfall checklist (GTK4-XIM removal, snap classic GTK module loading, --gtk-single-instance zombie, desktop-entry priority shadowing, env propagation paths) and a /proc-based diagnostic protocol that pinpoints the exact failure layer in under 60 seconds. Prefer this skill over generic 'install fcitx5' guidance whenever the user already has fcitx5 working in other apps (e.g., gnome-terminal, browser) but ghostty is the holdout — that pattern is the high-value case
auto-version
by Gonglitian决策为 REFINE 或 PIVOT 时，自动 snapshot stage-N_v{K}/ 目录（保留旧版本），新 run 用 3-part prompt: CONTINUATION (上次进度) + PRIOR PLAN (上次方案) + DELTA (这次改动)。Use when user says "auto version", "snapshot", "备份旧版本", "v3 v4 v5", "version run", "保留旧 run".
sleep-research
by Gonglitian夜间 autonomous 模式入口。把 stop-hook + heartbeat + 完成报告格式 + cost cap 打包成一行。已 ad-hoc 用了 200+ 次，固化为命名 skill。Use when user says "sleep research", "夜间", "睡觉了你干", "通宵跑", "autonomous overnight", "我去睡觉", "overnight", "明早". The user wants to walk away and have the agent push forward until a named goal.
read-paper
by GonglitianDeep-read an academic paper (PDF or arXiv ID) and produce a structured markdown note with figure analysis. Use this skill whenever the user wants to read, summarize, or take notes on a research paper — including phrases like '读一下这篇论文', 'summarize this paper', 'read paper 2401.xxxxx', '论文笔记', 'paper notes', or any mention of reading/analyzing an arXiv paper or PDF. Also trigger when the user provides a paper ID or PDF path and expects a detailed summary. This skill handles the full pipeline: text segmentation, figure extraction via VLM, cross-analysis, and final note assembly.
rebuttal
by GonglitianReviewer 评论流水线：分类 → 找 supporting evidence → 写 rebuttal → cross-model review → submit。3 道 safety gate：no fabrication / no overpromise / full coverage。Use when user says "rebuttal", "reviews", "reviewer 来了", "反驳", "答辩", "OpenReview comment".
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.