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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watcher-creator
by asheshgoplaniGuide for creating agent-deck watchers conversationally. This skill should be used when users want to set up a new watcher (webhook, ntfy, github, slack, gmail) to route events to a conductor. It walks the user through selecting an adapter type, gathering required settings, generating watcher.toml and clients.json entries, and emits the exact `agent-deck watcher create` command to run.
session-share
by asheshgoplaniShare Claude Code sessions between developers. Use when user mentions "share session", "export session", "import session", "send session to", "continue from colleague", or needs to (1) export current session to file, (2) import session from another developer, (3) hand off work context. Enables private, secure session transfer via direct file sharing.
agent-deck
by asheshgoplaniTerminal session manager for AI coding agents. Use when user mentions "agent-deck", "session", "sub-agent", "MCP attach", "git worktree", or needs to (1) create/start/stop/restart/fork sessions, (2) attach/detach MCPs, (3) manage groups/profiles, (4) get session output, (5) configure agent-deck, (6) troubleshoot issues, (7) launch sub-agents, or (8) create/manage worktree sessions. Covers CLI commands, TUI shortcuts, config.toml options, and automation.
opengraphdb
by asheshgoplaniUse when the user wants to query, traverse, build, or evolve a graph database; embedded or HTTP-served; Cypher syntax, vector similarity, full-text, RDF round-trip, time-travel, GraphRAG, or an MCP tool catalog. Trigger on phrases like "graph database", "knowledge graph", "Cypher query", "MCP graph", "GraphRAG", "vector + graph", "property graph", "RDF", "time-travel queries", "Neo4j alternative", "embedded graph", "single-file graph", "Memgraph alternative", "Kuzu alternative", "graph + vector + text", or any task framed as "agent owns the graph end-to-end". Use even when the user does not name OpenGraphDB, if the workload pattern matches load entities + relationships, then query by traversal, similarity, or time. Skip when the workload is a Neo4j cluster (causal cluster, Fabric), a time-series DB, plain key-value, or a managed vector DB where graph traversal is not part of the access pattern.
data-import
by asheshgoplaniUse when the user wants to load data into OpenGraphDB from a file or stream. Trigger on phrases like "import this CSV", "load JSON", "ingest RDF / Turtle / N-Triples", "bulk load", "import 50k rows", "ETL into the graph", or any task framed as "I have data over there and need it as nodes / edges over here". Covers format detection (CSV / JSON / JSONL / RDF), two-pass ingest (nodes first, edges second), batch sizing for the single-writer kernel, MERGE-based idempotency for re-runnable jobs, and validation against the resulting schema.
graph-explore
by asheshgoplaniUse when the user points at an unknown OpenGraphDB graph and asks "what is in here?", "show me the schema", "what entities exist", "how is this graph connected", or wants to navigate a graph they did not build. Trigger on phrases like "explore the graph", "discover schema", "find entry points", "what nodes are connected to X", "summarize this graph", "show me the most connected nodes". Covers five exploration strategies, schema navigation, entry-point selection, and how to descend from a high-level summary to a focused subgraph without overwhelming the user.
ogdb-cypher
by asheshgoplaniUse when generating, optimizing, or debugging Cypher queries against OpenGraphDB. Trigger on phrases like "write a Cypher query", "MATCH", "MERGE", "RETURN", "WHERE", "OpenGraphDB query", "openCypher", "graph query", or any task that requires producing executable Cypher against a known schema. Covers all supported clauses, OpenGraphDB-specific extensions ("AT TIME", "db.index.vector.queryNodes", "db.index.fulltext.queryNodes", "db.index.hybrid.queryNodes"), and common Cypher error patterns. The procedure namespace is "db.*" (matches Neo4j); the older "ogdb.*" form was never shipped.
opengraphdb
by asheshgoplaniUse when user wants to query, traverse, or build a graph database; embedded or HTTP-served; supports Cypher syntax, vector similarity, RAG, RDF round-trip, and MCP tool catalog. Trigger keywords - graph database, knowledge graph, Cypher query, MCP graph, GraphRAG, vector + graph, property graph, RDF, SHACL, time-travel queries, Neo4j alternative, embedded graph, single-file graph.
schema-advisor
by asheshgoplaniUse when the user describes a domain and wants a graph schema, or asks for index recommendations, RDF ontology mapping, or modeling tradeoffs. Trigger on phrases like "design a graph schema for", "what labels and edges should I use", "how should I model this in a graph", "which indexes do I need", "RDF mapping", "URI strategy", "ontology", or any request that converts a domain description into nodes, edges, and property layouts. Covers eight modeling best practices, six common anti-patterns, index selection (B-tree, vector, full-text), and RDF mapping with `_uri` preservation for round-trippable RDF.
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.