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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palantir-core-workflow-a
by ZenProjectGitBuild 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-core-workflow-b
by ZenProjectGitWork with Palantir Foundry Ontology objects, actions, and queries via SDK. Use when querying objects, applying actions, linking objects, or building Ontology-driven applications. Trigger with phrases like "palantir ontology", "foundry objects", "palantir actions", "ontology query", "OSDK objects".
palantir-cost-tuning
by ZenProjectGitOptimize Palantir Foundry costs through compute tuning, incremental builds, and usage monitoring. Use when analyzing Foundry compute costs, reducing API usage, or implementing cost monitoring for Foundry workloads. Trigger with phrases like "palantir cost", "foundry billing", "reduce foundry costs", "foundry pricing", "foundry expensive".
palantir-data-handling
by ZenProjectGitImplement Palantir Foundry data handling with PII protection, markings, and GDPR compliance. Use when handling sensitive data in Foundry, implementing data classifications, or ensuring compliance with privacy regulations. Trigger with phrases like "palantir data", "foundry PII", "palantir GDPR", "foundry data protection", "palantir markings".
palantir-enterprise-rbac
by ZenProjectGitConfigure Palantir Foundry enterprise access control with project roles, markings, and service users. Use when implementing role-based access, configuring project permissions, or setting up service user accounts for Foundry integrations. Trigger with phrases like "palantir RBAC", "foundry roles", "palantir permissions", "foundry access control", "foundry service user".
palantir-hello-world
by ZenProjectGitCreate 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-install-auth
by ZenProjectGitInstall and configure Palantir Foundry SDK authentication with OAuth2 or token auth. Use when setting up a new Foundry integration, configuring API credentials, or initializing the foundry-platform-sdk in your project. Trigger with phrases like "install palantir", "setup palantir", "palantir auth", "configure palantir API key", "foundry SDK setup".
palantir-multi-env-setup
by ZenProjectGitConfigure Palantir Foundry across development, staging, and production environments. Use when setting up multi-environment Foundry deployments, managing per-environment credentials, or implementing environment-specific configurations. Trigger with phrases like "palantir environments", "foundry staging", "foundry dev prod", "palantir environment setup".
palantir-reference-architecture
by ZenProjectGitImplement 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 ZenProjectGitApply 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 ZenProjectGitUpgrade 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".
anth-known-pitfalls
by ZenProjectGitIdentify and avoid common Claude API anti-patterns and integration mistakes. Use when reviewing code, onboarding developers, or debugging subtle issues with Anthropic integrations. Trigger with phrases like "anthropic pitfalls", "claude anti-patterns", "claude mistakes", "anthropic common issues", "claude gotchas".
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.