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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fleet-management
by michaelalberRolling deployment strategies, multi-device coordination, and rollback triggers for edge device fleets. Use when managing fleet-wide deployments, configuring rollout strategies, building device registries, or implementing rollback automation.
react-component-scaffolder
by michaelalberScaffolds a single React / TypeScript component or route with a typed props interface, a co-located React Testing Library test, an accessibility baseline, and an optional Storybook story. React analog of fastapi-scaffolder — the component/route is the front-end "unit". Use when creating a new React component, adding a route/page, generating a typed presentational or container component, or standing up a component with its test and story. Triggers on phrases like "scaffold react component", "create react component", "new react component", "add react route", "react page component", "react component with test", "react storybook component".
dotnet-vertical-slice
by michaelalberScaffold vertical slice architecture with CQRS + FreeMediator, including optional Telerik Blazor UI generation. Use when creating feature-based .NET projects with command/query separation and pipeline behaviors. Triggers on "scaffold feature", "create slice", "new feature", "generate cqrs", "add command", "add query", "create handler", "vertical slice". Do NOT use when the project uses layer-based (N-tier) architecture — this skill enforces feature folder structure and will conflict with existing layer conventions.
dotnet-security-review
by michaelalberOWASP-based security review of .NET / .NET Framework applications with Telerik UI specialization. Detects the framework and entry points, scans against the OWASP Top 10 (2025) mapped to .NET patterns (deserialization, injection, auth, Telerik, crypto, secrets), and produces a manager-friendly executive summary plus a graded technical findings table. Use to audit .NET code for vulnerabilities. Triggers on "dotnet security review", "OWASP audit", "check for vulnerabilities", "Telerik security", ".net security posture", "security scan", "vulnerability assessment". For federal / gov / DOE / NIST / FIPS / CUI context, run security-review-federal after this base review. Do NOT use to grade architecture/structure — use dotnet-architecture-checklist.
security-review-federal
by michaelalberFederal / government security overlay applied ON TOP OF a base language security review (dotnet/python/php/rust/react). Language-agnostic: adds NIST SP 800-53 control mapping, FIPS 140-2/3 cryptographic compliance (with a per-language crypto table), CUI handling, EO 14028 supply-chain requirements, and DOE Order 205.1B, and emits POA&M-ready findings with FIPS 199 impact levels. Use for federal/DOE/DOD/national-laboratory systems. Triggers on "federal security review", "NIST compliance", "NIST 800-53", "FISMA", "CUI", "FIPS audit", "DOE security", "POA&M", "ATO review". Do NOT use alone — run the matching <lang>-security-review FIRST; this overlay maps and extends it.
4d-schema-migration
by michaelalberParses 4D application exports and generates SQL Server DDL, EF Core entities, and Blazor UI guidance. Use when migrating from 4th Dimension (4D) platform to .NET/SQL Server. Triggers on phrases like "migrate 4D", "4D to SQL Server", "4D database conversion", "4D schema", "4th dimension", "convert 4D", "4D application", "4D database", "4D to .NET", "4D to EF Core", "4D forms to Blazor".
nuget-package-scaffold
by michaelalberNuGet package creation with CI/CD pipeline setup and test harness. Use when creating new NuGet packages, configuring package metadata, or setting up publish workflows. Do NOT use when the library is internal-only and not intended for NuGet publication; Do NOT use when the target is an application project.
research-synthesis
by michaelalberMulti-source cross-referencing, source credibility scoring, and structured briefing formats. Use when synthesizing research findings from multiple sources into evidence-based deliverables with confidence levels and citations.
anomaly-detection
by michaelalberStatistical anomaly detection for sensor data streams. Use when implementing outlier detection, drift monitoring, anomaly classification, and alert/recalibration decision trees for time-series sensor data.
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.