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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alicloud-ram-ops
by buhaiqingUse when the user wants to manage who can access their Alibaba Cloud account and what they can do — including access keys, MFA, console login, password policies, roles (cross-account or service-linked), custom/system policies, STS temporary credentials, and least-privilege audits. Trigger on: RAM, IAM, access control, permissions, authorization, security audit, key rotation, compliance, 子账号, 授权, 权限管理, 密钥管理, AK轮换, 安全审计, 角色扮演, 合规检查, 密码策略, 登录设置. Also trigger when the user asks to create or manage users/roles/keys, grant permissions, rotate keys, check access, set up MFA, audit security, assign or switch roles, or configure password rules — even without saying "RAM". Do NOT use for billing, CloudSSO, or non-RAM resource management (ECS, RDS, OSS, etc.).
jdcloud-alert-intelligence
by buhaiqing京东云告警智能助手。Use when 京东云账户/项目/资源 已产生告警后,需要 分级、聚合、抑制、降噪、值班疲劳统计 或 生成可读性高的告警分析报告。 Trigger keywords: 告警降噪, 告警分析, 告警分级, 告警聚合, 告警抑制, on-call 疲劳, alert fatigue, alert aggregation, alert grouping, 京东云告警, 告警历史分析, 告警模板。 仅适用于已有告警的二次处理(只读);不创建告警规则(用 jdcloud-cloudmonitor-ops), 不修产品配置(用各 jdcloud-*-ops)。
jdcloud-arch-advisor
by buhaiqingArchitecture Review & Design Advisory Engine. Use when user asks for architecture review, WAF assessment, architecture recommendation, or reverse-engineering existing JD Cloud infrastructure into architectural description. NOT for individual resource operations, billing, or health checks.
jdcloud-audit-ops
by buhaiqingUse when managing JD Cloud Audit Log resources — query operation events, describe event details, list operation trails, analyze user activity history, and manage event tracking. Works with "审计日志", "操作审计", "云审计", "Audit Log", or "操作记录" without saying "audit". Not for CloudTrail configurations from other clouds, VPC flow logs, or general monitoring.
jdcloud-clb-ops
by buhaiqingUse this skill to manage JD Cloud Load Balancer (CLB): deploy, configure, troubleshoot, or monitor via API/SDK or `jdc` CLI. Trigger for CLB, 负载均衡, Load Balancer, SLB, or tasks involving load balancer instances, listeners, backend servers, target groups, health checks, or traffic distribution — even without explicit "CLB" mention.
jdcloud-cloudmonitor-ops
by buhaiqingUse when you need to query monitoring metrics, set up alarm thresholds, check resource health, view alarm history, or configure custom monitoring on JD Cloud CloudMonitor. This DevOps runbook handles metric queries, threshold alerts, health checks, and incident response. Applicable when user mentions CloudMonitor, 云监控, monitoring, 告警, 指标查询, 资源告警, 监控面板, health check, 阈值告警, or metric-related tasks. Use even when user describes cloud resource status issues without explicitly mentioning "monitoring" or "alarm."
jdcloud-eip-ops
by buhaiqingUse this skill to manage JD Cloud Elastic IP (EIP): deploy, configure, troubleshoot, or monitor via API/SDK or `jdc` CLI. Trigger for EIP, 弹性公网IP, Elastic IP, or tasks involving EIP instances, bandwidth, associations, or billing — even without explicit "EIP" mention.
jdcloud-elasticsearch-ops
by buhaiqingUse this skill for JD Cloud Elasticsearch (云搜索Elasticsearch) management — create, configure, manage Elasticsearch clusters; monitor cluster health and performance; analyze index metrics; troubleshoot cluster issues; perform snapshot and restore operations. Apply when the user mentions Elasticsearch, 云搜索, ES集群, 搜索引擎, or asks about full-text search, log analytics, or Elasticsearch clusters on JD Cloud, even without explicit "Elasticsearch" mentions.
jdcloud-iam-ops
by buhaiqingUse this skill for JD Cloud identity and access management — create sub-users, groups, roles, and policies; manage access keys; grant or revoke permissions; set up cross-account access. Apply when the user mentions IAM, 访问控制, 子用户, 子账号, 角色, 策略, 授权, 权限, or asks about account security, access control, or permission management on JD Cloud, even without explicit "IAM" mentions.
jdcloud-kms-ops
by buhaiqingUse this skill for JD Cloud key management and encryption — create and manage encryption keys; encrypt/decrypt sensitive data; store and retrieve secrets; rotate keys; schedule key deletion. Apply when the user mentions KMS, 密钥管理, 加密, 解密, 密钥, or asks about data encryption, key management, secrets storage, or protecting sensitive data on JD Cloud, even without explicit "KMS" mentions.
jdcloud-kubernetes-ops
by buhaiqingUse this skill to manage JD Cloud JCS for Kubernetes: deploy, configure, troubleshoot, or monitor via API/SDK or `jdc` CLI. Trigger for Kubernetes, 容器服务, Kubernetes集群, K8s, or tasks involving cluster lifecycle, node groups, kubeconfig credentials, or workload orchestration — even without explicit "Kubernetes" or "JCS" mention.
jdcloud-mongodb-ops
by buhaiqingUse this skill for JD Cloud MongoDB database management — create, configure, and manage MongoDB instances; backup and restore data; analyze performance; troubleshoot connection or latency issues. Apply when the user mentions MongoDB, 云数据库 MongoDB, 文档数据库, 数据库, or asks about database, MongoDB instances on JD Cloud, even without explicit "MongoDB" mentions.
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.