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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chrome-devtools
by kettleofketchupBrowser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.
brand
by kettleofketchupDraftForge brand/theming review. Flags raw <button> over PrimaryButton/SecondaryButton, hardcoded violet/slate over brand tokens, inline styles, missing UserAvatar/EntityBreadcrumb.
calico
by kettleofketchupCalico CNI via Tigera Operator for Kubernetes. Use for IPPools, NetworkPolicy/GlobalNetworkPolicy, BGP peering, FelixConfiguration/BPF, K3s/RKE2/Rancher, airgap, or pod networking troubleshooting.
k3s
by kettleofketchupSelf-hosted K3s lightweight Kubernetes distribution. This skill should be used when installing or configuring K3s clusters, setting up server/agent nodes, configuring HA with embedded etcd, disabling default components (traefik, servicelb, flannel), replacing Flannel with custom CNI (Calico/tigera-operator, Cilium), configuring private registries (registries.yaml), deploying in airgapped/air-gapped environments (image pre-loading, INSTALL_K3S_SKIP_DOWNLOAD), managing K3s server flags (--cluster-cidr, --service-cidr, --flannel-backend=none, --disable-network-policy), writing /etc/rancher/k3s/config.yaml, troubleshooting containerd/kubelet issues, or integrating K3s with NixOS modules. Covers single-node and multi-node topologies.
calico
by kettleofketchupCalico CNI and network policy engine via Tigera Operator for Kubernetes. Use when deploying Calico with Tigera Operator Helm chart, configuring IPPools (VXLAN/IPIP/BGP encapsulation), writing Calico NetworkPolicy or GlobalNetworkPolicy resources, setting up BGP peering (BGPPeer, BGPConfiguration, route reflectors), tuning FelixConfiguration (BPF dataplane, iptables, logging, flow logs), upgrading Calico versions, enabling or disabling Calico on K3s/RKE2/Rancher clusters, configuring airgapped Calico deployments, or troubleshooting pod networking issues (node NotReady, DNS failures, cross-node connectivity, VXLAN/IPIP tunnel problems).
k3s
by kettleofketchupSelf-hosted K3s Kubernetes. Use for install/config, HA etcd, disabling traefik/flannel, custom CNI (Calico/Cilium), registries.yaml, airgap/INSTALL_K3S_SKIP_DOWNLOAD, server flags, NixOS modules.
zsh-completions
by kettleofketchupWrite zsh completion scripts with descriptions, colors, and dynamic completions. This skill should be used when creating _command completion files, using _arguments/_describe/_values functions, configuring ZLS_COLORS for colored completions, building subcommand trees (like git/kubectl), generating dynamic completions from external commands, understanding compinit/compdef/fpath, configuring fzf-tab previews and grouping, writing fzf.zsh preview scripts, or debugging completion issues. Output completions to ~/.config/zsh/completions/.
mkdocs-documentation
by kettleofketchupMkDocs Material documentation management. This skill should be used when writing, formatting, or validating documentation in docs/. Covers admonitions, Mermaid diagrams, code blocks with annotations, content tabs, navigation setup, include-markdown for reusable content, _includes pattern for shared diagrams, and mkdocs testing. Always check project-specific docs at docs/dev/ai/skills/ and docs/dev/ai/agents/ for project-specific Claude skill and Claude agent documentation when available.
vsphere
by kettleofketchupVMware vSphere infrastructure management with govc CLI and Ansible. This skill should be used when running govc commands (VM lifecycle, snapshots, datastore operations, OVA/ISO deployment, inventory navigation), writing Ansible playbooks with community.vmware or vmware.vmware collections, managing ESXi hosts via esxcli/SSH, working with vSphere REST API via curl, configuring vCenter authentication, understanding vSphere inventory hierarchy (Datacenter/Cluster/Host/VM), or automating VM provisioning and infrastructure operations.
hyprland
by kettleofketchupHyprland Wayland compositor on NixOS. This skill should be used when configuring programs.hyprland, GPU rendering (Intel/AMD/NVIDIA), Wails/webkit2gtk fixes, xdg portals, home-manager, or wallpapers.
hyprland
by kettleofketchupHyprland Wayland compositor on NixOS. This skill should be used when configuring programs.hyprland, GPU rendering (Intel/AMD/NVIDIA), Wails/webkit2gtk fixes, xdg portals, home-manager, or wallpapers.
openwebui
by kettleofketchupOpen WebUI self-hosted AI chat interface. This skill should be used when deploying (Docker/Helm/pip), configuring auth/SSO/RBAC, RAG/vector DBs, web search, plugins/MCP, scaling, or reverse proxy.
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.