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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build-paths-advanced
by oracle-devrelScaffold an agent system where Oracle AI DB is the *only* state store, composed from the build-paths/skills/ building-block library. Stack — langchain-oracledb + oracle-database-mcp-server + in-DB ONNX embeddings + OCI GenAI Grok 4 + Open WebUI. Three projects — production-feeling NL2SQL+RAG hybrid analyst, self-improving research agent, conversational schema designer. For users who want a real DB-as-only-store agent demo.
build-paths-intermediate
by oracle-devrelScaffold a Grok-4 tool-calling agent over an Oracle schema using langchain-oracledb + oracle-database-mcp-server + in-DB ONNX embeddings (registered MiniLM model, no external embedding API) + Open WebUI. For users who've built RAG before and want to rebuild it on the production-feeling Oracle stack.
build-paths
by oracle-devrelTop-level router for the build-paths skill set. Asks the user one question (which path?), then dispatches to beginner/, intermediate/, or advanced/. Use when the user wants to scaffold an Oracle-AI-DB project but hasn't picked a difficulty yet.
build-paths-beginner
by oracle-devrelScaffold a small RAG chatbot on Oracle 26ai Free + langchain-oracledb + OCI Generative AI Grok 4 + sentence-transformers MiniLM-L6-v2 (Python-side embeddings, same model intermediate/advanced register inside Oracle) + Open WebUI. Three flavors that share one skeleton — PDF / Markdown / Web. For users new to Oracle who want a polished demo running in an afternoon.
oracle-mcp-server-helper
by oracle-devrelWire the oracle-database-mcp-server into a Python project so an LLM agent can call list_tables / describe_table / run_sql / vector_search at inference time. Handles install, stdio launch, and LangChain tool conversion via langchain-mcp-adapters. Use whenever a project needs a Grok 4 / GPT-class agent that talks to a live Oracle schema.
langchain-oracledb-helper
by oracle-devrelScaffold a langchain-oracledb store layer — multi-collection OracleVS wrapper, metadata-as-string monkeypatch, embedder-dim assertion, OracleChatHistory subclass (langchain-oracledb does not ship one). Use when a project needs Oracle as its LangChain vector store and chat-history backend.
oracle-aidb-docker-setup
by oracle-devrelBring up Oracle 26ai Free in Docker, create a project-scoped app user with USERS tablespace + ONNX-friendly grants, and write a `.env` ready for the rest of the build-paths skills. Idempotent — safe to re-invoke. Use when any project needs a fresh local Oracle 26ai instance.
soccer-workshop-setup
by oracle-devrelBootstrap the soccer analytics agent workshop. Starts the Oracle AI Database Free container, applies schema, loads the FIFA dataset, optionally trains models, populates LangChain OracleVS hybrid retrieval plus semantic memory, applies LangGraph OracleDB observability, and verifies OCI GenAI access. Use when starting the workshop or resetting a stale environment.
soccer-agent-toolbelt
by oracle-devrelSoccer analytics agent toolbelt. Gives Claude Code direct access to the Oracle-backed match data, ML predictions, and three-tier memory. Use when answering questions about football matches, building on the soccer agent, or exploring the World Cup dataset.
autonomous-ops-watch
by oracle-devrelinspect sql-observable operational evidence for one connected oracle autonomous ai lakehouse. use when users ask for maintenance notifications, patch identifiers or patch details, client errors, lockdown profile errors, operator access audit evidence, unified audit highlights, active or blocking sessions, invalid objects, compile errors, table or partition access statistics, or an operations summary based only on sql evidence. this skill is read-only, sql/mcp-first, and does not use oci apis, oci events, console, cli, lifecycle actions, resource principal views, or external schedulers.
autonomous-data-loader
by oracle-devrelgenerate and safely execute oracle autonomous ai lakehouse data loading and oci object storage lakehouse access workflows using dbms_cloud. use when the user wants to list oci object storage files, choose files or prefixes to load, create conservative csv staging tables, generate or run copy_data or copy_collection, tune dbms_cloud format options, load json documents into soda collections, create external tables to query apache iceberg data stored in oci object storage using direct metadata.json or hadoop catalog patterns, monitor user_load_operations or dba_load_operations, inspect logfile_table or badfile_table, troubleshoot rejected rows, reconcile loads, or profile staged data after loading. this skill is mcp-first with generate-only fallback and is scoped to oci object storage and dbms_cloud-based workflows.
adb-mcp-server-setup
by oracle-devrelCreate or update a Codex MCP server entry for Oracle Autonomous Database, refresh ADB bearer token, and optionally bootstrap default DB tools (LIST_SCHEMAS, LIST_OBJECTS, GET_OBJECT_DETAILS, EXECUTE_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.