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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decision-journal
by Xipher-LabsCapture significant decisions in a structured format — decision journal, log this decision, important decision, decision review. Stores entries at ~/.config/walter-os/state/decisions/. Surfaces past-due revisit prompts via weekly-review-coach.
vercel-agent-skills-bridge
by Xipher-LabsHow Walter-OS imports Vercel's official agent-skills (vercel-labs/agent-skills) where they make sense. NOT a wholesale fork — selective integration, since Walter-OS already has overlapping skills. Use this skill when the user mentions "vercel agent-skills", "react best practices skill", "deploy-to-vercel skill", or you need to evaluate adding a Vercel skill to Walter-OS.
infisical-agent
by Xipher-LabsHow to consume Infisical secrets from every Walter-OS surface — operator shells, walter-host Docker services, Vercel deploys, Railway services, GitHub Actions, n8n workflows, Cursor, Claude Code. The unifying principle: NEVER paste secrets into config files; always pull from Infisical at runtime via CLI / SDK / native integration. Use this skill when the user asks "how do I use this secret in <X>", "Infisical setup for Vercel", "secrets in GitHub Actions", "n8n credentials".
secrets-yubikey-unlock
by Xipher-LabsLegacy-named Walter-OS guide for storing Infisical Machine Identity credentials in an OS credential store. Covers macOS Keychain, Linux Secret Service, pass+GPG, and optional hardware security keys. Use when the user asks how to auth Infisical from CLI, configure secrets bootstrap, set up keychain/keyring-backed secrets, or remove plaintext tokens from shell dotfiles.
daily-supply-chain-audit
by Xipher-LabsRun a comprehensive daily security audit of all installed MCP servers, Claude Code skills, agent configs, and AI CLI tooling. Detects supply chain attacks, tool-name shadowing, malicious skills, configuration drift, missing CVE patches, and untrusted package versions. Use this skill EVERY MORNING before starting work, on demand when installing a new MCP/skill, or after pulling Walter-OS updates. ALSO trigger when the user asks "is my agent setup safe", "audit my MCPs", "check for vulnerabilities", "any new CVEs", or mentions concerns about supply chain, tool poisoning, prompt injection, or malicious skills.
frontend-quality
by Xipher-LabsEnforce frontend quality bar — accessibility (WCAG 2.2 AA), performance (Core Web Vitals: LCP/INP/CLS), semantic HTML, image optimization, bundle size, mobile-first, loading/empty/error states. Use this skill on any PR that touches React/Next.js/Astro/Svelte/Vue components, CSS/Tailwind, layout files, or anything in `app/`, `components/`, `pages/`, `src/`. Critical for [Project A] (Argentine mobile users on slow 3G/4G) and [Company] docs site (developer audience expects fast). Auto-triggers on `*.tsx`, `*.jsx`, `*.svelte`, `*.vue`, `*.astro`, `*.css`.
knowledge-extraction
by Xipher-LabsExtract structured knowledge from books, papers, articles — key claims, frameworks, Anki cards, spaced repetition. Two phases: Phase A extracts, Phase B converts to Anki/Mochi cards. State at ~/.config/walter-os/state/knowledge/. Keywords: extract knowledge, summarize paper, book notes, Anki cards, learning from.
brand-creation
by Xipher-LabsCreate a complete brand identity for a project — name, logo, color palette, typography, and voice/tone — using nanobanana for visual generation. Use this skill when starting a new project ([Project A], [Project B], hackathons), when the user asks to "create a brand", "design a logo", "make an identity", "moodboard for X", or wants to formalize the look and feel of a product. Produces deliverables to assets/brand/<project>/ ready for use in landing pages, decks, and social.
cold-outreach-sequencer
by Xipher-LabsDraft a personalized 5-touch cold outreach sequence for email, LinkedIn, or Twitter/X DM. Each touch includes personalization variables, subject line A/B options, and reply-trigger templates. Triggers on: cold outreach, email sequence, LinkedIn outreach, DM sequence, founder outreach.
wiki-query
by Xipher-LabsAnswer operator questions by reading the LLM-maintained wiki at $WALTER_OS_HOME/wiki/ before falling back to web search or model knowledge. Read $WALTER_OS_HOME/wiki/index.md first, grep for relevant terms, load matching pages, answer with `[[wikilink]]` citations. If the wiki doesn't have the answer, derive it via web/MCP search and propose a new wiki page (concepts/ or decisions/) so future queries don't re-derive. Triggered any time the operator asks a substantive question — the agent should self-decide to apply this skill, not wait for an explicit "/query".
hcloud-cli
by Xipher-LabsProvision, resize, and manage Hetzner Cloud VMs, networks, volumes, load balancers, firewalls, snapshots, and DNS via the official `hcloud` CLI. Use this skill whenever the user asks to "provision a VM", "resize Hetzner server", "create snapshot", "list VMs", "rotate Hetzner token", "set Hetzner firewall rule", or any Hetzner Cloud infrastructure task. Replaces low-trust community Hetzner MCP. SPENDS MONEY — confirmation required before any state-changing action.
project-induction
by Xipher-LabsRun a guided induction interview with the operator to establish a new project's context, constraints, and success criteria. Generates a bootstrap spec, a project-level AGENTS.md, and a Plane epic with initial tasks. Use this skill when starting a new project or when the operator invokes 'walter-os new project <type> <name>'.
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.