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...
implementing-go-semaphore-pools
by LeoDPraetorianUse when implementing bounded concurrency in Go with semaphores and worker pools - rate limiting external APIs, high-throughput parallel processing (40K+ items/hour), variable-cost task scheduling. Complements go-errgroup-concurrency. Handles "too many goroutines", "rate limit exceeded", "out of memory", semaphore.NewWeighted, errgroup.SetLimit, runtime.NumCPU patterns
capabilities-vql-development
by LeoDPraetorianUse when developing VQL artifacts - Velociraptor query language, Aegis capabilities, artifact testing
nrf-reviewer
by LeoDPraetorianUse when reviewing findings or preliminary observations for Norton Rose Fulbright (NRF) engagements - applies prophylactic legal style, defensive writing, and NRF-specific formatting requirements to markdown documents
writing-nerva-sctp-modules
by LeoDPraetorianUse when creating SCTP-based fingerprintx plugins for telecom/5G protocols - guides through plugin interface, SCTP semantics, and platform-specific patterns
mapping-to-fda-cybersecurity
by LeoDPraetorianUse when mapping security findings to FDA medical device cybersecurity guidance during capability development - provides premarket/postmarket requirements, severity classification framework, SBOM requirements, and coordinated disclosure guidance for healthcare compliance
researching-perplexity
by LeoDPraetorianUse when researching with AI-powered search - Perplexity MCP for fast synthesis with citations, alternative to traditional WebSearch+WebFetch. Requires API key setup.
docx
by LeoDPraetorianUse when working with professional .docx documents for creating new documents, modifying content, working with tracked changes, adding comments, or analyzing document structure - provides comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction
implementing-branded-types-typescript
by LeoDPraetorianUse when implementing domain models with multiple ID types that should not be mixed (Asset vs Risk vs User IDs) - teaches branded/nominal types pattern to prevent primitive obsession and mixing incompatible string/number types at compile time - community pattern NOT in official TypeScript docs with production-proven ROI (Revolut reduced incidents 45 percent) - includes type-level and runtime validation patterns with Chariot entity examples
orchestrating-capability-development
by LeoDPraetorianUse when developing security capabilities (VQL, Nuclei, Janus, Fingerprintx, Scanner integrations) - coordinates architecture, implementation, review, and testing phases with capability-specific quality checks
reviewing-capability-implementations
by LeoDPraetorianUse when executing code reviews for security capability implementations (VQL/Nuclei/Janus/fingerprintx) - provides 5-step review process validating plan adherence, detection quality, and verification commands with severity classification
gateway-capabilities
by LeoDPraetorianUse when developing capabilities - routes to VQL, Nuclei templates, scanner integration, and security tool skills via progressive loading.
windows-internals-part2
by LeoDPraetorianUse when investigating Windows system mechanisms, virtualization technologies, management/diagnostics, caching, file systems, or startup/shutdown processes - authoritative reference from Windows Internals 7th Edition Part 2 by Russinovich, Ionescu, Solomon, and Allievi
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.