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...
demo-cnpg-local-web-ng
by carverautoRun ServiceRadar web-ng locally against the live Kubernetes `demo` CNPG database for dashboard, SRQL, services, and UI testing. Use when a task asks to test with live demo data, run local Phoenix against demo CNPG, validate dashboard authoring with Playwright, or avoid fragile kubectl port-forward database access.
demo-local-rollout
by carverautoBuild, sign, and roll ServiceRadar changes into the Kubernetes `demo` namespace using locally built images and immutable `sha-...` tags. Use when the user asks to deploy, refresh, roll, patch, or test code in `demo` before a release. Covers changed-image detection, copying unchanged images forward, OpenBao cosign signing, Argo patching, and rollout verification. Do not use for release cuts, Docker Compose refreshes, or non-demo namespaces unless the user explicitly redirects you.
demo-web-ng-fastpath
by carverautoRefresh the Kubernetes `demo` namespace with a web-ng-only change using the ServiceRadar fast path. Use when the diff only touches `elixir/web-ng/**` and the user wants a faster local demo rollout without rebuilding the full image graph. Covers scope verification, copying unchanged images forward, rebuilding the production `serviceradar-web-ng` release locally, pushing with `crane`, signing with the OpenBao release key, patching Argo, and verifying the rollout. Do not use when non-web-ng services changed or when cutting a release.
fieldsurvey-local-web-ng
by carverautoRun ServiceRadar web-ng locally against the Kubernetes demo namespace FieldSurvey data, including CNPG NodePort access, NATS Object Store artifact access, authenticated browser checks, and Playwright screenshots for dashboard and FieldSurvey review iteration.
playwright-cli
by carverautoAutomate browser interactions, inspect web pages, debug UI behavior, capture snapshots/screenshots, manage browser sessions, generate or run Playwright tests, mock requests, trace sessions, record videos, and work with browser storage using the playwright-cli command. Use when Codex needs browser automation through playwright-cli or npx playwright-cli.
release-cut-and-demo-roll
by carverautoCut a ServiceRadar release and roll the Kubernetes `demo` namespace to the resulting published semver image tag through ArgoCD Image Updater. Use when the user asks to update `VERSION` and `CHANGELOG`, run `scripts/cut-release.sh`, push the release refs, wait for published release artifacts, and then refresh `demo` to that released version. Do not use for pre-release local testing with unpublished images; use `$demo-local-rollout` or `$demo-web-ng-fastpath` for that.
srql-fixtures-db-tests
by carverautoRun ServiceRadar Elixir database tests against the Kubernetes CNPG instance in the `srql-fixtures` namespace. Use when local localhost PostgreSQL is unavailable, when tests need current branch migrations, or when a user asks to use the srql-fixtures database for DB-backed validation. Covers scratch database creation, TLS-required NodePort connections, migrations, focused test commands, cleanup, and secret hygiene.
web-ng-docker-loop
by carverautoRun ServiceRadar `elixir/web-ng` locally against the Docker Compose CNPG database with copied mTLS certs and Docker secrets, then verify dashboard UI changes with Playwright. Use when iterating on web-ng, dashboard package, custom React dashboard, UAL WiFi map, SRQL topbar, or browser screenshot behavior without rebuilding the web-ng release image.
daisyui-colors
by carverautoMANDATORY color usage rules for daisyUI 5
daisyui-config
by carverautoConfiguration options for daisyUI 5
daisyui-install
by carverautoInstallation notes for daisyUI 5
daisyui
by carverautoOfficial daisyUI component library skill. The mandatory UI library for Tailwind CSS. TRIGGER when generating any HTML or JSX code even if the user does not explicitly ask for this skill.
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.