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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neo4j-cypher-guide
by opsmillComprehensive guide for writing modern Neo4j Cypher read queries. Essential for text2cypher MCP tools and LLMs generating Cypher queries. Covers removed/deprecated syntax, modern replacements, CALL subqueries for reads, COLLECT patterns, sorting best practices, and Quantified Path Patterns (QPP) for efficient graph traversal.
migrate-feature-page
by opsmillMigrate Infrahub docs feature pages from the legacy topic+guide pair into a cleaner structure (single merged page, hub+spokes, or tutorial extraction). Supports both single-feature migrations (one feature like Profiles or Webhooks) and section-wide migrations (a whole section like Branches & Change Control with multiple features migrated together) when the team has agreed on a section-wide restructure plan. Trigger when the user names a specific feature or section to migrate (e.g. "let's migrate profiles", "start the Resource Manager migration", "do the entire Branches & Change Control section"). Each migration is its own branch off `demo/groups-diataxis-example`, its own PR back to that branch.
speckit-git-feature
by opsmillValidate Jira/JPD ticket reference and create a feature branch
infrahub-managing-transforms
by opsmillCreates Infrahub transforms that convert data into JSON, text, CSV, or device configs using Python or Jinja2 templates, with YAML-driven tests. TRIGGER when: building config generation, data export, format conversion, Jinja2 templates, artifact pipelines, writing or running tests for a transform. DO NOT TRIGGER when: designing schemas, writing validation checks, creating generators, querying live data.
infrahub-auditing-repo
by opsmillAudits an Infrahub repository against best practices and rules, producing a structured compliance report. TRIGGER when: reviewing repo for compliance, onboarding to existing project, pre-deployment validation, catching issues. DO NOT TRIGGER when: creating schemas, writing checks/generators, querying live data, populating objects.
infrahub-common
by opsmillShared references and cross-cutting rules for all Infrahub skills — GraphQL syntax, .infrahub.yml format, and common patterns. DO NOT TRIGGER directly — loaded automatically by other Infrahub skills when they need shared references.
infrahub-managing-checks
by opsmillCreates Infrahub check definitions — Python validation logic, GraphQL queries, and YAML-driven tests for proposed change pipelines. TRIGGER when: writing validation checks, creating Python checks, building data quality guards for proposed changes, writing or running tests for a check. DO NOT TRIGGER when: designing schemas, querying live data, building transforms or generators.
infrahub-managing-generators
by opsmillCreates Infrahub Generators — design-driven automation that builds infrastructure objects from templates and topology definitions. TRIGGER when: building design-to-implementation workflows, auto-creating objects from templates, topology-driven generation. DO NOT TRIGGER when: designing schemas, writing data transforms, querying live data, populating static data files.
infrahub-managing-menus
by opsmillCreates Infrahub custom navigation menus for the web UI sidebar, organizing node types into logical groups. TRIGGER when: designing sidebar menus, grouping node types in UI, customizing Infrahub web interface navigation. DO NOT TRIGGER when: designing schemas, writing checks or transforms, populating data objects.
infrahub-managing-objects
by opsmillCreates and manages Infrahub object data YAML files for populating infrastructure instances — devices, locations, organizations, and modules. TRIGGER when: creating device instances, populating data files, defining locations or organizations, adding infrastructure objects. DO NOT TRIGGER when: designing schemas, writing Python checks/generators, querying live data.
infrahub-managing-schemas
by opsmillCreates, validates, and modifies Infrahub schema YAML files — nodes, generics, attributes, relationships, and extensions. TRIGGER when: designing data models, adding schema nodes, validating schema definitions, planning schema migrations, modeling file objects / attachments / uploads (storing PDFs, diagrams, images, certificates, documents as Infrahub objects). DO NOT TRIGGER when: populating data objects, writing checks/generators/transforms, querying live data.
infrahub-analyzing-data
by opsmillAnalyzes and correlates live Infrahub data via the MCP server — answers operational questions, detects drift, and investigates impact. TRIGGER when: querying infrastructure data, checking compliance, investigating change impact, producing ad-hoc reports. DO NOT TRIGGER when: writing automated checks, building transforms, designing schemas, populating data files. ALWAYS pass the user's question verbatim as args — this skill runs in a forked context and cannot see the parent conversation. Invoking without args will fail.
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.