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...
qingteng-use
by AgentFlocks用于处理青藤云安全平台相关任务,适合通过API或者结合浏览器进行以下任务:主机资产盘点、进程与账号排查、端口和服务查询、网站与数据库资产分析、可疑操作检测、暴力破解分析、异常登录排查、WebShell 与后门调查、蜜罐结果分析、补丁与漏洞风险检查、弱密码排查、基线任务查看、合规检查、授权管理、系统审计和快速风险体检场景。只要用户提到青藤、青藤云安全、青藤主机安全的相关操作时,必须先加载本 skill。本 skill 是 青藤 平台操作的唯一决策入口:在未阅读本 skill 并完成模式判断前,不要直接调用任何 `qingteng_*` tool。
implementing-zero-knowledge-proof-for-authentication
by AgentFlocksZero-Knowledge Proofs (ZKPs) allow a prover to demonstrate knowledge of a secret (such as a password or private key) without revealing the secret itself. This skill implements the Schnorr identificati
implementing-bgp-security-with-rpki
by AgentFlocksImplement BGP route origin validation using RPKI with Route Origin Authorizations, RPKI-to-Router protocol, and ROV policies on Cisco and Juniper routers to prevent route hijacking.
performing-web-cache-poisoning-attack
by AgentFlocksExploiting web cache mechanisms to serve malicious content to other users by poisoning cached responses through unkeyed headers and parameters during authorized security tests.
performing-windows-artifact-analysis-with-eric-zimmerman-tools
by AgentFlocksPerform comprehensive Windows forensic artifact analysis using Eric Zimmerman's open-source EZ Tools suite including KAPE, MFTECmd, PECmd, LECmd, JLECmd, and Timeline Explorer for parsing registry hives, prefetch files, event logs, and file system metadata.
analyzing-outlook-pst-for-email-forensics
by AgentFlocksAnalyze Microsoft Outlook PST and OST files for email forensic evidence including message content, headers, attachments, deleted items, and metadata using libpff, pst-utils, and forensic email analysis tools for legal investigations and incident response.
implementing-hashicorp-vault-dynamic-secrets
by AgentFlocksImplements HashiCorp Vault dynamic secrets engines for database credentials, AWS IAM keys, and PKI certificates with automatic generation, lease management, and credential rotation to eliminate static secrets in application configurations. Activates for requests involving Vault secrets engine configuration, dynamic database credentials, ephemeral cloud credentials, or automated secret rotation.
implementing-zero-trust-with-hashicorp-boundary
by AgentFlocksImplement HashiCorp Boundary for identity-aware zero trust infrastructure access management with dynamic credential brokering, session recording, and Vault integration.
processing-stix-taxii-feeds
by AgentFlocksProcesses STIX 2.1 threat intelligence bundles delivered via TAXII 2.1 servers, normalizing objects into platform-native schemas and routing them to appropriate consuming systems. Use when onboarding new TAXII collection endpoints, automating bi-directional intelligence sharing with ISACs, or building pipeline validation for malformed STIX bundles. Activates for requests involving OASIS STIX, TAXII server configuration, MISP TAXII, or Cortex XSOAR feed integrations.
performing-physical-intrusion-assessment
by AgentFlocksConduct authorized physical penetration testing using tailgating, badge cloning, lock bypassing, and rogue device deployment to evaluate facility security controls.
performing-kubernetes-penetration-testing
by AgentFlocksKubernetes penetration testing systematically evaluates cluster security by simulating attacker techniques against the API server, kubelet, etcd, pods, RBAC, network policies, and secrets. Using tools
implementing-network-policies-for-kubernetes
by AgentFlocksKubernetes NetworkPolicies provide pod-level network segmentation by defining ingress and egress rules that control traffic flow between pods, namespaces, and external endpoints. Combined with CNI plu
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.