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...
memgraph-storage-reviewer
by memgraphReview code changes in Memgraph's storage layer, including MVCC, concurrency patterns, WAL, recovery, DDL operations, index/constraint management, delta chains, and skip list operations. Invoke for pull requests or changes to src/storage/v2/.
agent-context-graph
by memgraphSet up and check Codex context capture into Memgraph.
release
by memgraphUsed to release all toolbox, integrations, agents. Use when releasing a subproject to PyPI, Docker Hub, or when the user asks to release or publish.
add-best-practice
by memgraphUse this skill when adding a new best practice example to the Memgraph best-practices repository. Walks through categorization, code documentation, dependencies, and README updates so the contribution matches the repo's conventions.
pre-release
by memgraphRun final pre-release tasks for the documentation site. Updates release note dates, direct download links to the new Memgraph version, and rebuilds the site to regenerate the sitemap. Use when preparing a documentation release, before publishing, or when asked to run "pre-release" steps.
new-release-branch
by memgraphCreate a new release branch for Memgraph documentation with the initial release-notes scaffold commit and PR. Use when the user wants to start a new release, create a release branch, or prepare release notes for a new version.
check-before-release
by memgraphRun before every release to ensure all merged memgraph PRs have a docs label assigned, have changelog entries, have docs pages where required, that all changed text is free of spelling and grammar issues, and that existing documentation cross-links the new content. Use when preparing a release branch, before merging into the main branch, or when asked to "check before release". Also use standalone when asked to audit docs labels on a milestone.
write-changelog-item
by memgraphCreate/write the changelog item / release note for a given Github PR and update the docs release PR description.
memgraph-rust-query-modules
by memgraphDevelop custom query modules in Rust for Memgraph graph database. Use when user asks to create Rust procedures, implement graph algorithms in Rust, build high-performance query modules, or work with the rsmgp-sys Rust API. Covers module structure, compilation with Cargo, graph traversal, vertex/edge operations, and deployment to Memgraph.
memgraph-indexes-and-constraints
by memgraphCreate and manage Memgraph indexes, constraints, and enums. Use when the user asks about indexing strategy, creating or dropping indexes, schema enforcement, uniqueness or existence constraints, data type constraints, enums, or ANALYZE GRAPH.
memgraph-mgconsole-cli
by memgraphGuide to using Memgraph's mgconsole CLI for connecting to Memgraph, running Cypher, configuring output, and using interactive or non-interactive modes. Use when the user asks how to install, connect, or run queries with mgconsole.
memgraph-run-mage-algorithms
by memgraphRun MAGE (Memgraph Advanced Graph Extensions) graph algorithms from Cypher, including built-in deep path traversals (BFS, DFS, WSP, ASP, KSP), PageRank, community detection, centrality, node embeddings, LLM integration, and 70+ algorithm modules. Use when the user asks to run a graph algorithm, find shortest paths, detect communities, compute centrality, generate embeddings, or call any MAGE procedure.
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.