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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stripe-best-practices
by oxy-hqGuides Stripe integration decisions — API selection (Checkout Sessions vs PaymentIntents), Connect platform setup (Accounts v2, controller properties), billing/subscriptions, Treasury financial accounts, integration surfaces (Checkout, Payment Element), migrating from deprecated Stripe APIs, and security best practices (API key management, restricted keys, webhooks, OAuth). Use when building, modifying, or reviewing any Stripe integration — including accepting payments, building marketplaces, integrating Stripe, processing payments, setting up subscriptions, creating connected accounts, or implementing secure key handling.
shadcn
by oxy-hqManages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI. Provides project context, component docs, and usage examples. Applies when working with shadcn/ui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for "shadcn init", "create an app with --preset", or "switch to --preset".
oxy-task-spec-default
by oxy-hqUse when adding or modifying code in `crates/app/src/server/` or HTTP handlers that involves long-running compute, periodic schedules, multi-step pipelines, or any work that must survive instance death. Triggers include "spawn a background task", "schedule periodically", "run async after returning", "long-running operation", "fire and forget", "tokio::spawn", "background job", "queue this", or PRs that add new work to HTTP handlers. Also triggers when designing features that touch LLM APIs, git clones, embedding builds, or any operation taking >5 seconds.
oxy-scaling-design
by oxy-hqUse when the user asks about Oxy's multi-instance scaling, worker fleet, workspace ownership leases, horizontal scaling design, or any topic from the scaling design doc. Triggers include "scale Oxy", "scale oxygen", "multi-instance", "worker fleet", "lease table", "horizontal scaling", "Phase 1/2/3/4/5/6/7 of scaling", "gix migration", "smart cloning", "durable execution", "shard workspaces", "Envoy ring hash", "internal jobs admin", "workspace ownership".
agentic-browser-test
by oxy-hqUse when an Oxy dev or coding agent working in oxy-hq/oxygen-internal needs to create, update, maintain, or run/debug an agentic browser flow under web-app/tests/agentic/. Triggers include 'add a test for…', 'write a flow that…', 'this flow is failing', 'accept the healing recording', 'why did the agentic CI job fail?', 'regression test for the bug I just fixed'. Produces or repairs .flow.test.yml files and routes the dev through triage / healing / cache hygiene. Skip for unit tests (Vitest/cargo nextest), Oxy agent eval tests (.agent.test.yml / .aw.test.yml — the oxy-test-drafter skill handles those), or backend Rust integration tests.
oxy-compile-boundary
by oxy-hqUse when adding a new YAML entity type to Oxy (e.g. a new file extension under `crates/oxy-compile/src/walker.rs` like `.foo.yml`), when introducing a new runtime read site that walks the workspace filesystem, when wiring a new handler that calls `ConfigManager::resolve_*` or `fs::read_to_string(workspace_path...)`, or any time someone proposes a feature that "just reads from the workspace dir." Also triggers on phrases like "new file extension", "add a YAML config", "load this from disk", "scan the workspace for", "read the YAML file" — the compile boundary expects every NEW workspace artifact to be a row in Postgres, not a per-request FS read.
validate
by oxy-hqValidate .view.yml semantic layer files using airlayer. Use when the user creates or modifies view files and wants to check for errors.
query
by oxy-hqRun a semantic query against the database via airlayer. Use when the user wants to query data through the semantic layer, test view definitions, or debug query results.
profile
by oxy-hqProfile dimensions in the semantic layer to discover data values, ranges, and cardinality. Use when the user wants to understand what data is in a dimension, find valid filter values, or validate view definitions against actual data.
migrate-from-cube
by oxy-hqMigrate a Cube.js semantic layer to airlayer .view.yml files. Use when the user has existing Cube.js schema files (.js or .yml) they want to convert to airlayer format.
bootstrap
by oxy-hqBootstrap a semantic layer from a database. Use when the user wants to create .view.yml files from their warehouse schema, or when starting a new airlayer project from scratch.
query
by oxy-hqCompile a semantic query to SQL using airlayer. Use when the user wants to generate SQL from .view.yml schemas, test a query against their semantic layer, or translate dimensions/measures/filters into dialect-specific SQL.
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.