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...
storybook-config
by flight505Generate and configure Storybook 10 for any framework with automatic detection, SOTA best practices, and platform-specific optimizations (Web, Tauri, Electron)
ai-startup-advisor
by flight505Strategic advisor for evaluating software/AI product ideas and generating defensible startup strategies. Use this skill whenever the user wants to evaluate a product idea, brainstorm startup directions, assess defensibility against foundation model companies, choose between build-vs-buy for AI components, or needs a strategic positioning check for a development project. Trigger on phrases like "is this idea defensible", "what should I build", "will OpenAI/Anthropic eat this", "startup idea", "product strategy", "moat", "competitive positioning", "should I build X", "vertical AI opportunity", "business model for AI", or any discussion about whether a software product is worth building given the current AI landscape. Also trigger when the user is evaluating technical architecture decisions through a strategic lens — e.g. "should I use a fine-tuned model or API calls" where the answer has competitive implications.
palantir-core-workflow-a
by flight505Build Palantir Foundry data pipelines using Python transforms. Use when creating ETL pipelines, writing @transform decorators, or building dataset-to-dataset processing in Foundry. Trigger with phrases like "palantir pipeline", "foundry transform", "palantir ETL", "palantir data pipeline", "foundry python transform".
palantir-hello-world
by flight505Create a minimal working Palantir Foundry example querying Ontology objects. Use when starting a new Foundry integration, testing your setup, or learning basic Foundry API and Ontology patterns. Trigger with phrases like "palantir hello world", "palantir example", "palantir quick start", "foundry first query".
palantir-migration-deep-dive
by flight505Execute major Palantir Foundry migration strategies including data migration, API version upgrades, and platform transitions. Use when migrating data into Foundry, upgrading between API versions, or re-platforming existing integrations. Trigger with phrases like "migrate to palantir", "foundry migration", "palantir data migration", "foundry replatform".
palantir-observability
by flight505Set up observability for Palantir Foundry integrations with metrics, logging, and alerts. Use when implementing monitoring for Foundry API calls, setting up dashboards, or configuring alerting for Foundry integration health. Trigger with phrases like "palantir monitoring", "foundry metrics", "palantir observability", "monitor foundry", "foundry alerts".
palantir-performance-tuning
by flight505Optimize Palantir Foundry API performance with caching, batching, and pagination. Use when experiencing slow API responses, optimizing transform builds, or improving request throughput for Foundry integrations. Trigger with phrases like "palantir performance", "optimize foundry", "foundry slow", "palantir caching", "foundry batch".
palantir-prod-checklist
by flight505Execute Palantir Foundry production deployment checklist and rollback procedures. Use when deploying Foundry integrations to production, preparing for launch, or implementing go-live procedures. Trigger with phrases like "palantir production", "deploy foundry", "palantir go-live", "foundry launch checklist".
palantir-rate-limits
by flight505Implement Palantir Foundry API rate limiting, backoff, and request queuing. Use when handling 429 errors, implementing retry logic, or optimizing API request throughput for Foundry. Trigger with phrases like "palantir rate limit", "foundry throttling", "palantir 429", "foundry retry", "palantir backoff".
palantir-reference-architecture
by flight505Implement Palantir Foundry reference architecture with best-practice project layout. Use when designing new Foundry integrations, planning data pipeline architecture, or establishing patterns for Ontology-driven applications. Trigger with phrases like "palantir architecture", "foundry best practices", "foundry project structure", "how to organize palantir".
palantir-sdk-patterns
by flight505Apply production-ready Palantir Foundry SDK patterns for Python and TypeScript. Use when implementing Foundry integrations, refactoring SDK usage, or establishing team coding standards for Foundry API calls. Trigger with phrases like "palantir SDK patterns", "foundry best practices", "palantir code patterns", "idiomatic foundry SDK".
palantir-upgrade-migration
by flight505Upgrade Palantir Foundry SDK versions and handle breaking changes. Use when upgrading foundry-platform-sdk, migrating between API versions, or detecting deprecations in Foundry integrations. Trigger with phrases like "upgrade palantir", "palantir migration", "foundry breaking changes", "update foundry SDK".
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.