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...
activity-reconstruction
by dreadnodeReconstruct what happened on an iOS device and when — application usage, backgrounds/foregrounds, device lock state, location, cellular context — using knowledgeC.db, routined, PowerLog, CellularUsage, Safari, and related artifacts. Builds evidence-backed timelines.
apache-confusion-attacks
by dreadnodeExploit Apache httpd semantic parsing ambiguities for ACL bypass, SSRF, source disclosure, and RCE. Use when Apache httpd detected (Server header, .htaccess, mod_rewrite).
h2-connect-internal-scan
by dreadnodeInternal port scanning and SSRF via HTTP/2 CONNECT method -- enumerates internal services, detects open ports, and bypasses firewall restrictions. Use when target supports HTTP/2 and proxies may forward CONNECT requests to internal hosts.
h2-waf-bypass
by dreadnodeBypass WAF body/path inspection via HTTP/2 binary framing — delayed DATA frames blind out-of-process WAFs, body size truncation evades ext_authz limits, Extended CONNECT converts methods past ACLs. Includes black-box proxy+WAF fingerprinting. Use when WAF blocks payloads over HTTP/1.1 but target supports HTTP/2, or when standard 403-bypass and parser-differential techniques fail.
h2c-websocket-smuggling
by dreadnodeBypass reverse proxy ACLs via H2C upgrade or WebSocket tunnel. Use when proxy blocks internal paths but forwards Upgrade headers, or when standard CL.TE/TE.CL smuggling fails.
jxscout-static-analysis
by dreadnodeQuery and manage jxscout static analysis matches -- list match kinds, get matches with filters, mark matches as seen/unseen. Use when triaging security findings, investigating code patterns, exploring the attack surface, reviewing scan results, or tracking vulnerability triage progress across match results.
jxscout-bookmarks
by dreadnodeCreate jxscout bookmarks and bookmark groups via the CLI to document interesting code during security research. Use when analyzing JS/HTML files, reviewing findings, documenting client-side flows, or when the user asks to bookmark security-relevant code patterns, gadgets, or sinks.
jxscout-custom-analyzers
by dreadnodeCreate custom jxscout analyzers (regex, derived, or script-based) and retrigger analysis. Use when the user wants to find specific code patterns across all project files, add new match kinds, or extend jxscout's static analysis capabilities.
jxscout-findings
by dreadnodeCreate, retrieve, and list jxscout findings to document security-relevant discoveries. Use when you've identified a vulnerability, interesting gadget, security-relevant primitive, or anything a bug bounty hunter would want to track. Also use when the user wants to review, list, filter, or summarize existing findings.
jxscout-relationships
by dreadnodeQuery jxscout for asset relationships -- which JS files and iframes a page loads, lazy-loaded chunks, reversed source maps, and how assets relate to each other. Use when mapping the attack surface of a specific page or understanding how assets are connected.
jxscout-repeater
by dreadnodeSend and iterate on raw HTTP requests using jxscout's repeater. Use when the user asks to analyze, test, try out, send, resend, or replay an HTTP request; when testing an endpoint or API call for security issues; when modifying parameters/headers/body to test for vulnerabilities; or when the user mentions repeater, .req/.res, or raw HTTP testing.
jxscout-security-research
by dreadnodeCoordinates end-to-end jxscout vulnerability assessment workflows including target enumeration, static analysis triage, and finding documentation -- routes to specific jxscout-* skills for each phase. Use when starting a new target assessment, planning attack surface mapping, performing security testing, finding vulnerabilities, or understanding how jxscout tools fit together.
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.