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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columba-python-migration
by torlando-techThis skill should be used when working on the Strangler Fig migration of reticulum_wrapper.py, creating or modifying rns_api.py, working on RnsApiClient or ChaquopyRnsApiClient in Kotlin, extracting business logic from Python to Kotlin, modifying PythonWrapperManager, working on health monitoring, telemetry collection, RMSP, delivery state machines, message reception, link speed probing, identity file management, event/callback extraction, or any task that touches the Python-to-Kotlin migration boundary. It provides the full migration plan, phase dependencies, thin API surface, and anti-patterns to avoid.
transport-routing
by torlando-techReticulum's transport layer and multi-hop routing system. Use when working with transport nodes, routing, multi-hop forwarding, path resolution, hop counts, PATHFINDER constants, or path requests.
resources
by torlando-techReticulum's resource transfer system for large data over links. Use when working with resource transfers, windowing, hashmaps, segments, compression, file transfers, or resource states.
reticulum-utilities
by torlando-techComprehensive guide to Reticulum's command-line utilities for network management, diagnostics, identity operations, file transfer, and remote execution
lxmf-protocol
by torlando-techDeep knowledge of LXMF (Lightweight Extensible Message Format) protocol and Reticulum integration. Use when working with LXMF messaging, LXMessage creation, LXMRouter configuration, propagation nodes, delivery methods (OPPORTUNISTIC, DIRECT, PROPAGATED), stamps, tickets, or RNS destinations and links.
micron-syntax
by torlando-techThis skill should be used when the user asks about "micron markup syntax", "micron formatting", "backtick commands in NomadNet", ".mu file format", "NomadNet terminal formatting", "micron colors", "micron links", "micron fields", "micron parser implementation", or needs to understand Micron markup tags for NomadNet pages.
nomadnet-pages
by torlando-techThis skill should be used when the user asks about "NomadNet page development", "creating .mu pages", "hosting files on NomadNet nodes", "dynamic NomadNet pages", "NomadNet page authentication", "node page serving", ".allowed files", or needs to create, configure, or troubleshoot NomadNet node pages.
announce-mechanism
by torlando-techDetailed knowledge of Reticulum's announce mechanism for automatic path discovery. Use when working with announce propagation, path discovery, announce bandwidth, announce forwarding, path requests, or announce structure.
cryptography-identity
by torlando-techReticulum's cryptographic primitives, identity structure, encryption tokens, and key derivation. Use when working with Ed25519, X25519, identity hashes, HKDF, token format, encryption, signatures, ratchets, or key derivation.
destinations
by torlando-techReticulum's addressing system and destination types. Use when working with destination types (SINGLE, GROUP, PLAIN, LINK), destination hashes, aspect naming, or addressing.
interfaces
by torlando-techReticulum's hardware abstraction layer and interface types. Use when working with RNode, AutoInterface, TCP, UDP, I2P, Serial, KISS, BLE interfaces, interface modes, IFAC, bandwidth allocation, or MTU.
links
by torlando-techReticulum's link system — encrypted channels with forward secrecy. Use when working with link establishment, link requests, ECDH, ephemeral keys, forward secrecy, channels, keep-alive, or link timeouts.
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.