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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joyride-workspace-scripting
by BetterThanTomorrowJoyride Workspace scope scripting — scripts and source files in <workspace>/.joyride/. Covers workspace activation, project-specific automation, workspace vs user scope precedence, and team sharing. Use when: creating or editing Workspace scripts/source files, setting up workspace_activate.cljs, or building project-specific automation.
joyride
by BetterThanTomorrowJoyride core — REPL evaluation, async patterns, VS Code API access, Flares, JS interop, and available libraries. Use when: working with Joyride evaluation, writing ClojureScript in any Joyride context, creating Flares/WebViews, exploring VS Code APIs via the REPL, or using joyride_evaluate_code / joyride_request_human_input tools.
joyride-user-scripting
by BetterThanTomorrowJoyride User scope scripting — scripts and source files in ~/.config/joyride/. Covers user activation scripts, global keyboard shortcuts, disposable management, and npm dependencies. Use when: creating or editing User scripts/source files, setting up user_activate.cljs, or configuring global keybindings.
joyride-update-llm-contexts
by BetterThanTomorrowOne-time migration: modernize a Joyride project's copilot-instructions.md to use bundled skills instead of duplicated API docs or fetch URLs. Use when: the instructions file tells you to check for migration, or the user asks about updating their Joyride AI context.
joyride-internals
by BetterThanTomorrowJoyride extension internals — subsystem contracts, state architecture, activation sequences, and namespace reference. Use when: modifying core extension code, debugging state issues, working with app-db or SCI context, understanding activation or script execution flow, investigating nREPL or when-context behavior, or working with the output or disposable system.
testing-joyride
by BetterThanTomorrowTest Joyride features in the examples workspace. Covers output terminal verification, who-tracking, REPL log queries, and evaluation result validation. Use when: testing Joyride features.
e2e-testing
by BetterThanTomorrowE2E test authoring for Backseat Driver. Use when: writing, modifying, or debugging e2e tests under e2e-test-ws/, adding new MCP test scenarios, investigating test failures, understanding test infrastructure, or working with files matching *_test.cljs in the e2e workspace.
backseat-driver
by BetterThanTomorrowEffective use of the Backseat Driver extension and its tools for Clojure interactive programming. Use when: working in Clojure (including all dialects and runtimes) project, be it reading, planning, developing, or evaluating code in the REPL, looking up function documentation or ClojureDocs examples, choosing REPL sessions, editing Clojure files structurally, checking REPL output, planning implementations, reviewing code, or developing solutions incrementally. Whenever you consider any of these tools: clojure_evaluate_code, clojure_load_file, clojuredocs_info, clojure_list_sessions, clojure_symbol_info, clojure_repl_output_log, clojure_edit_files, clojure_balance_brackets. Also use this skill when PLANNING or DISCUSSING Clojure development approaches — not only at the moment of REPL evaluation.
editing-clojure-files
by BetterThanTomorrowStructural editing of Clojure files using Backseat Driver tools. Use when editing or planning edits: creating/adding/inserting/replacing/deleting top-level forms, fixing bracket balance, resolving indentation issues, planning multi-edit sequences, recovering from failed edits, or working with Rich Comment Forms in Clojure files, regardless of dialect or runtime. Use whenever you consider any of these tools: clojure_edit_files, clojure_balance_brackets. Use when editing Clojure and unsure which tool to pick. Use this skill when PLANNING or DISCUSSING Clojure file edits — not only at the moment of editing.
backseat-driver-internals
by BetterThanTomorrowBackseat Driver extension internals — Ex framework contracts, app-db state architecture, MCP socket server lifecycle, enrichment system, Datascript output log, activation sequences, and namespace reference. Use when: modifying core extension code, debugging state issues, working with app-db or Ex framework, understanding activation or MCP server flow, investigating enrichment or Datascript connections, or working with tool registration.
backseat-driver-testing
by BetterThanTomorrowTesting strategies for Calva Backseat Driver MCP tools. Use when: Testing Backseat Driver, validating tool updates, testing structural editing workflows, verifying REPL evaluation with who-tracking, testing output log filtering, testing load-file tool, smoke testing after dep bumps, or debugging tool behavior. Covers all Backseat Driver tool categories: structural editing, REPL eval, load file, symbol info, bracket balancing, and output log.
babashka
by BetterThanTomorrowWrite idiomatic Babashka (bb) scripts and modules. Covers babashka.fs, babashka.process, babashka.cli, babashka.http-client, and built-in namespaces. Use when: writing bb scripts, creating or modifying a task, REPL-driven Babashka development, editing .clj files in directories with bb.edn or scripts/ folders, using Backseat Driver tools with Babashka.
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.