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...
address-pr-review
by novuhqCritically triage pull request review comments against PR requirements and codebase reality, implement only valid fixes, and reply only when declining a suggestion. Use when the user asks to address PR comments, review feedback, CodeRabbit/Bugbot threads, or `/address-pr-review`.
env-setup
by novuhqCreate or update Novu environment variables in the user's project safely (never expose the secret key to the client). Complements the official Novu skills by covering project-level env configuration.
figma-use
by novuhq**MANDATORY prerequisite** — you MUST invoke this skill BEFORE every `use_figma` tool call. NEVER call `use_figma` directly without loading this skill first. Skipping it causes common, hard-to-debug failures. Trigger whenever the user wants to perform a write action or a unique read action that requires JavaScript execution in the Figma file context — e.g. create/edit/delete nodes, set up variables or tokens, build components and variants, modify auto-layout or fills, bind variables to properties, or inspect file structure programmatically.
novu-prepare-pr
by novuhqPost-implementation PR prep for Novu feature branches — quality passes, commit/PR hygiene, CI triage, and review feedback. Use after feature work is done, when the user asks to prepare a PR, ship a branch, fix CI, address review comments, or babysit a pull request before merge.
run-api-e2e-tests
by novuhqRun e2e tests for the API service. Use when the user wants to run API E2E tests.
nv-worktree-cleanup
by novuhqAudits git worktrees and removes the ones that are safe to delete — merged into the base branch (including squash-merges via `gh`), missing on disk, or explicitly confirmed by the user — then deletes the associated local branches and runs `git worktree prune`. Use when the user asks to clean up worktrees, prune worktrees, list stale worktrees, remove merged worktrees or branches, or reclaim disk space from old checkouts.
nv-worktree-create
by novuhqCreate a sibling git worktree and a new branch with the same name, copy local `.env*` files, initialize the enterprise submodule, wire enterprise symlinks, and move the agent into the worktree. Use when the user asks for a worktree, parallel branch checkout, or `/worktree` with a branch name.
nv-onboard-dcr-mcp
by novuhqOnboard a new DCR OAuth MCP catalog entry with provider-doc vetting and curl probes. Use when adding or changing `mode: dcr` entries in MCP_SERVERS. Abort if the provider requires whitelist or manual approval.
nv-worktree-commands
by novuhqGit worktree command reference: path naming, create/remove/prune, `.env*` copy, package-manager install, and cleanup. Use when implementing or debugging worktree setup — not as the primary user-facing workflow (see nv-worktree-create).
ink-tui-wizard
by novuhqBuild terminal user interfaces (TUIs) using Ink (React for CLIs) and @inkjs/ui with a reactive, session-driven wizard pattern. Use when creating interactive CLI installation wizards, setup flows, or multi-step terminal applications in Node.js/TypeScript. Covers reactive screen resolution, declarative flow pipelines, overlay interrupts, session state management, Ink components, Flexbox terminal layout, and graceful degradation across terminal environments.
onboard-dcr-mcp
by novuhqOnboard a new DCR OAuth MCP catalog entry with provider-doc vetting and curl probes. Use when adding or changing `mode: dcr` entries in MCP_SERVERS. Abort if the provider requires whitelist or manual approval.
novu-dashboard-workflows
by novuhqAuthor step content for Novu workflows defined in the Dashboard or generated/edited via the Novu MCP. Use when filling in step controls (subject, body, editorType, headers, body, conditions) for email, in-app, sms, push, chat, delay, digest, throttle, or HTTP Request steps.
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.