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.
Querying local SQLite index...
agent-trajectory-analysis
by letsgetaiAnalyze Agent experiment trajectories from JSON/JSONL session logs. When given a case file or directory, record experiment background, Agent behavior, step-by-step trajectory, compare against expected results with verifiable evidence locations, identify problems, and suggest improvements.
experiment-hygiene
by letsgetai实验前的纪律检查清单。在运行 AI agent 实验、基准测试、仿真优化前自动提醒关键纪律：commit 代码、冻结修改、记录 baseline、版本对应。触发词："跑实验"、"benchmark"、"开始测试"、"run experiment"。
psychological-counselor
by letsgetai覆盖所有心理场景的标准化心理疏导技能。遵循"感知-拆解-疏导-修复-巩固"五步法，适用于自我心理调节、他人情绪疏导、突发心理应激、长期心理困扰等所有情况。帮使用者理清思路、精准应对心理问题，实现心理状态回归平衡。不替代专业心理治疗，仅作日常心理支持。
philosophy-guide
by letsgetai当用户提出哲学问题时，与用户进行深度探讨，阐释相关哲学思想，并规划可验证的实践路径。适用于人生困惑、道德抉择、存在意义、价值判断等哲学话题的探讨与实践指导。
start-with-1pager
by letsgetaiUser's personal methodology combining pyramid thinking (SCQA/MECE) with MVP execution. Guides starting a new task by writing a focused 1-pager within 1 hour, detects perfectionism signals like "我先想想 / 等我准备好 / let me think more / I need to prepare first", and reviews expression output for conclusion-first / SCQA opening / MECE grouping. Use only when explicitly invoked by name. Typical scenarios are starting a new project, research, blog post, presentation, decision, or asking for structure review of writing/slides/talks.
benchmark-optimization-loop
by letsgetai当用户要求优化性能、尝试多种变体、递归优化、对比延迟/吞吐量/成本，或通过反复测量选出最佳实现时使用。
eval-harness
by letsgetai实验评估清单——跑实验之前先定义"什么叫过"，跑完之后对照打分。适用于仿真优化、多模型对比、benchmark 评测。
agentic-engineering
by letsgetaiAI 工程师操作手册——评估先行、任务拆解、按复杂度选模型、追踪成本。
mle-workflow
by letsgetai生产级 ML 工程全流程——数据契约、可复现训练、模型评估、部署、监控、回滚。适用于需要超越 notebook 一次性脚本的 ML 系统。
update-experiment-docs
by letsgetaiUpdate research and experiment documentation for algorithm work. Covers four stages: pre-research planning (1-pager with SCQA), post-research findings synthesis, pre-experiment setup (environment config recording), and post-experiment results documentation. Detects perfectionism signals and enforces MVP documentation. Use when starting research, after reading papers, before running experiments, or after experiments complete.
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.