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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nuitka-4-0-8
by tangledgroupPython compiler that translates Python code to C and native executables. Use when compiling Python applications for distribution, creating extension modules, optimizing performance, or building standalone binaries across Windows, Linux, macOS, and FreeBSD.
s-expression
by tangledgroupS-expressions (symbolic expressions) are a minimal notation for nested tree-structured data using atoms and lists. Invented for Lisp, they represent both code and data with the same syntax (homoiconicity). Covers function definitions, lambda forms, calling conventions, and result binding across Lisp-family languages. Use when working with Lisp-family languages, designing domain-specific languages, building parsers, representing abstract syntax trees, or serializing hierarchical data in a portable format.
s-expression-alternatives
by tangledgroupAlternative syntaxes for Lisp-family languages that reduce or eliminate parentheses while preserving homoiconicity and extensibility. Covers sweet-expressions (curly-infix, neoteric, indentation-based), i-expressions (SRFI-49 indentation-sensitive syntax), o-expressions (operator-based AST with currying juxtaposition), and Liso (Racket implementation of o-expressions). Use when designing alternative Lisp syntaxes, building reader macros for readable code, evaluating tradeoffs between parentheses and other grouping mechanisms, or implementing custom s-expression variants.
s-expression-interpreter
by tangledgroupBuild and understand s-expression interpreters in Python and C. Covers lexer-free tokenization, recursive descent parsing, eval-apply cycles, SymbolTable scoping, lval heap allocation with mpc, sfsexp library integration, and language semantics across Scheme (R4RS/R7RS), Common Lisp, and Clojure. Use when building a Lisp/Scheme interpreter from scratch, implementing s-expression data structures in C, adding function definitions and lexical scoping to an evaluator, or understanding how homoiconic languages process code-as-data through read-eval-print loops.
agentmemory-0-9-4
by tangledgroupPersistent memory engine for AI coding agents with cross-session context capture, hybrid search (BM25 + vector + knowledge graph), and multi-agent coordination via MCP server. Works with Claude Code, Cursor, Gemini CLI, and any MCP client without external databases. Use when building AI coding agent workflows requiring persistent memory or semantic recall.
agentmemory-0-8-9
by tangledgroupPersistent memory engine for AI coding agents providing automatic cross-session context capture, hybrid search (BM25 + vector + knowledge graph), and multi-agent coordination via MCP server with 43 tools. Works with Claude Code, Cursor, Gemini CLI, OpenCode, Hermes, OpenClaw, and any MCP client without external database dependencies. Use when building AI coding agent workflows requiring persistent memory, semantic recall across sessions, or multi-agent coordination with shared context.
agentmemory-0-8-10
by tangledgroupPersistent memory engine for AI coding agents with cross-session context capture, hybrid search (BM25 + vector + knowledge graph), and multi-agent coordination via MCP server. Works with Claude Code, Cursor, Gemini CLI, and any MCP client without external databases. Use when building AI coding agent workflows requiring persistent memory or semantic recall.
podman-py-5-8-0
by tangledgroupPython client library for Podman container engine providing programmatic access to containers, images, pods, networks, volumes, manifests, secrets, and quadlets via RESTful API. Use when building Python applications that require container orchestration, automation scripts, CI/CD integration, or container management without Docker dependency.
podman-5-8-1
by tangledgroupDaemonless container engine with Docker-compatible CLI for managing containers, pods, images, volumes, and networks. Supports rootless operation, Kubernetes integration, systemd management, and remote access. Use when building, running, or managing containers without a daemon, implementing rootless workflows, orchestrating pods, or migrating from Docker.
podman-5-8-2
by tangledgroupDaemonless container engine with Docker-compatible CLI for managing containers, pods, images, volumes, and networks. Supports rootless operation, Kubernetes integration, systemd management, and remote access. Use when building, running, or managing containers without a daemon, implementing rootless workflows, orchestrating pods, or migrating from Docker.
podman-compose-1-5-0
by tangledgroupOrchestrates multi-container applications using Compose specification files with Podman backend. Use when deploying containerized stacks, managing services defined in compose.yaml files, or migrating from docker-compose to a daemonless rootless workflow.
rustfs-1-0-0-alpha-93
by tangledgroupHigh-performance distributed object storage system with S3-compatible API, OpenStack Swift support, and comprehensive observability features built in Rust. Use when deploying S3-compatible storage backends, configuring distributed clusters, integrating with Kubernetes via Helm, setting up TLS/mTLS, or building data lake solutions requiring high-throughput storage.
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.