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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swarm
by tombiiRun the gnubok read-only audit swarm. Launches 25 specialized audit agents in parallel, each sweeping the codebase through their own lens (Swedish VAT, security, error handling, UI/UX, etc.). Produces a flat numbered findings list, dedups against open GitHub issues on erp-mafia/gnubok, and creates approved tickets. Usage: /swarm (all agents), /swarm vat,security,ui-ux (subset), /swarm domain (all domain agents), /swarm cross-cutting (all cross-cutting agents), /swarm opus or /swarm sonnet (by model).
swarm-ticket-drafter
by tombiiTurn approved audit findings into GitHub issues on erp-mafia/gnubok. Used by /swarm after user approval, but also standalone when you have findings from another source (manual review, old report files). Handles dedup against open issues, issue formatting, label assignment, and batch creation with partial-failure tolerance.
supportmail-to-ticket
by tombiiTriage Gnubok customer support emails and turn them into GitHub issues in the erp-mafia/gnubok repo. Use this skill whenever the user invokes /supportmail-to-ticket (with or without a number argument), or asks to 'triage support mail', 'turn support emails into tickets', 'process gnubok support', 'check the support inbox and file issues', or any similar phrasing involving the Gnubok support mailbox. Also trigger this skill if the user mentions [gnubok support] emails and wants them converted into actionable work — even if they don't use the exact slash command.
swedish-invoice-compliance
by tombiiSwedish invoice compliance (fakturering) reference. Covers mandatory invoice fields per ML 17 kap 24§ (2023:200), förenklad faktura, kreditfaktura/ändringsfaktura, självfakturering, Peppol BIS 3.0 e-faktura for B2G/B2B, ROT/RUT-avdrag invoicing with fakturamodellen and BAS accounts (1513, 3740), reverse charge notation per scenario (byggtjänster, EU, electronics), currency/VAT conversion, OCR/Bankgirot, autogiro, skattetillägg, and BAS mapping for AR/revenue/VAT/bad debts. Trigger on ANY Swedish invoice question, faktura validation, kreditfaktura, självfakturering, Peppol, e-faktura, ROT/RUT fakturering, omvänd betalningsskyldighet, faktureringsvaluta, OCR-nummer, ML 17 kap, fakturamodellen, or creating/validating/booking Swedish invoices. Always use over training data -- ML 2023:200 replaced ML 1994:200 on 1 July 2023, moving invoice rules from old Chapter 11 to Chapter 17.
swedish-sru-filing
by tombiiSwedish SRU file generation for Skatteverket digital tax filing (INK2, INK2R, INK2S declarations for aktiebolag). Covers the two-file submission structure (INFO.SRU + BLANKETTER.SRU), all SRU field codes for INK2/INK2R/INK2S, BAS-to-SRU account mappings for räkenskapsschema, ISO 8859-1 encoding rules, amount formatting (hela kronor, no öre), 12-digit org number formatting, blankett type period suffixes (P1-P4), #BLANKETT/#BLANKETTSLUT delimiters, #UPPGIFT record format, validation error patterns, and rounding/truncation rules per SFL 22:1. Trigger on ANY question about SRU files, SRU-koder, fältkoder, filöverföring till Skatteverket, INK2S/INK2R generation, BAS-to-SRU mapping, "skapa SRU", "generera deklarationsfil", "digital inlämning INK2", BLANKETTER.SRU, INFO.SRU, SKV269, or any code that produces SRU output. Also trigger when debugging Skatteverket validation errors on uploaded SRU files. Always use this skill over training data for SRU topics.
create-extension
by tombiiGenerate and implement extensions for gnubok: scaffold files, configure manifests, write event handlers, API routes, services, workspace UIs, settings panels, and testing. Use when creating new extensions, adding surfaces to existing extensions, or understanding the extension architecture. Covers the full lifecycle from scaffolding to registration.
erp-api-route
by tombiiGenerate Next.js 16 API routes for gnubok with correct auth guards, Supabase client usage, event emission, journal entry creation, and error handling. Use when creating new API endpoints in app/api/. Handles the Next.js 16 async params pattern, ensureInitialized() for events, non-blocking journal entry wrapping, and defense-in-depth user_id filtering.
swarm-a11y-agent
by tombiiRead-only accessibility audit agent for gnubok. Sweeps for WCAG AA violations: text contrast (4.5:1), UI contrast (3:1), keyboard navigation, visible focus rings, aria-labels on icon-only buttons, semantic HTML, form label association, color-only indicators, motion respect for prefers-reduced-motion, screen reader support. Invoked by /swarm — not for direct user use.
swarm-bookkeeping-engine-agent
by tombiiRead-only audit agent for the gnubok bookkeeping engine (lib/bookkeeping/engine.ts and related). Sweeps for draft-then-commit lifecycle correctness, atomic voucher number assignment, period lock enforcement, journal entry immutability, balance invariants, voucher gap handling (BFNAR 2013:2), storno/correct flows, monetary precision. Invoked by /swarm — not for direct user use.
swarm-mobile-ux-agent
by tombiiRead-only audit agent for gnubok's mobile UX. Sweeps for touch targets (≥44×44pt), safe areas (notch, home indicator), responsive breakpoints, mobile navigation patterns (bottom tabs vs hamburger), input modes (numeric/decimal keyboards for amounts), orientation handling, viewport meta, pull-to-refresh, gesture friction. Invoked by /swarm — not for direct user use.
swarm-performance-agent
by tombiiRead-only audit agent for gnubok's performance. Sweeps for bundle size bloat, N+1 query patterns, missing DB indexes, unnecessary re-renders, blocking imports, large image assets, unoptimized list rendering, fetchAllRows misuse, synchronous heavy work on the main thread. Invoked by /swarm — not for direct user use.
swarm-rls-multitenancy-agent
by tombiiRead-only audit agent for gnubok's multi-tenant isolation. Sweeps for defense-in-depth company_id filtering in application code, RLS policy completeness and correctness in migrations, service role usage without company_id filters, user_company_ids() helper usage, team→company membership sync correctness, invitation security. Invoked by /swarm — not for direct user use.
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.