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...
jtagprobe
by BrownFineSecurityProbe IoT/embedded targets for exposed SWD/JTAG debug interfaces using a SEGGER J-Link. Detects whether debug is OPEN, LOCKED (readout-protected), or DEAD (fused off). Use when assessing whether a target's on-chip debug port can be reached, identifying the silicon vendor from DPIDR/IDCODE, and confirming halt+memory access for full debugger control.
jadx
by BrownFineSecurityAndroid APK decompiler that converts DEX bytecode to readable Java source code. Use when you need to decompile APK files, analyze app logic, search for vulnerabilities, find hardcoded credentials, or understand app behavior through readable source code.
netflows
by BrownFineSecurityNetwork flow extractor that analyzes pcap/pcapng files to identify outbound connections with automatic DNS hostname resolution. Use when you need to enumerate network destinations, identify what hosts a device communicates with, or map IP addresses to hostnames from packet captures.
wsdiscovery
by BrownFineSecurityWS-Discovery protocol scanner for discovering and enumerating ONVIF cameras and IoT devices on the network. Use when you need to discover ONVIF devices, cameras, or WS-Discovery enabled equipment on a network.
apktool
by BrownFineSecurityAndroid APK unpacking and resource extraction tool for reverse engineering. Use when you need to decode APK files, extract resources, examine AndroidManifest.xml, analyze smali code, or repackage modified APKs.
chipsec
by BrownFineSecurityStatic analysis of UEFI/BIOS firmware dumps using Intel's chipsec framework. Decode firmware structure, detect known malware and rootkits (LoJax, ThinkPwn, HackingTeam, MosaicRegressor), generate EFI executable inventories with hashes, extract NVRAM variables, and parse SPI flash descriptors. Use when analyzing firmware .bin/.rom/.fd/.cap files offline without requiring hardware access.
ffind
by BrownFineSecurityAdvanced file finder with type detection and filesystem extraction for analyzing firmware and extracting embedded filesystems. Use when you need to analyze firmware files, identify file types, or extract ext2/3/4 or F2FS filesystems.
iotnet
by BrownFineSecurityIoT network traffic analyzer for detecting IoT protocols and identifying security vulnerabilities in network communications. Use when you need to analyze network traffic, identify IoT protocols, or assess network security of IoT devices.
logicmso
by BrownFineSecurityAnalyze digital and analog captures from Saleae Logic MSO devices. Decode protocols like UART, SPI, I2C from exported binary files. Use when analyzing logic analyzer captures for CTF challenges, hardware reverse engineering, or protocol decoding.
nmap
by BrownFineSecurityProfessional network reconnaissance and port scanning using nmap. Supports various scan types (quick, full, UDP, stealth), service detection, vulnerability scanning, and NSE scripts. Use when you need to enumerate network services, detect versions, or perform network reconnaissance.
onvifscan
by BrownFineSecurityONVIF device security scanner for testing authentication and brute-forcing credentials. Use when you need to assess security of IP cameras or ONVIF-enabled devices.
picocom
by BrownFineSecurityUse picocom to interact with IoT device UART consoles for pentesting operations including device enumeration, vulnerability discovery, bootloader manipulation, and gaining root shells. Use when the user needs to interact with embedded devices, IoT hardware, or serial consoles.
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.