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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iris-openclaw-product-photo
by database-zumaGenerate prompt spec untuk product photography Zuma — sandal, footwear, apparel. Produces structured JSON/text prompt yang bisa dipakai di Midjourney, DALL-E, Flux, atau brief ke fotografer. Use when: user minta foto produk untuk Shopee/Tokopedia listing, Instagram feed, brand catalog, atau white background e-commerce shots.
zuma-token-usage-report
by database-zumaCross-cutting skill for reporting token usage and model info after completing any task. Load alongside any other skill. Agent appends a standard footer showing model, tokens consumed, and estimated cost after producing output.
bst-data-entry
by database-zumaSkill untuk memasukkan data dari foto/gambar Bukti Serah Terima (BST) ke sheet DATA ENTRY di file Excel Update PO Zuma. TRIGGER UTAMA: user mengupload gambar/foto dokumen BST — dokumen kertas bertulisan tangan dengan header 'BUKTI SERAH TERIMA', logo PT. Halimjaya Sakti, tabel berisi kode artikel, warna, jumlah satuan, dan nomor PO. Jika user upload gambar yang terlihat seperti dokumen BST (form serah terima barang, ada kolom Uraian Barang, Jumlah Satuan, Keterangan berisi nomor PO), LANGSUNG gunakan skill ini tanpa bertanya. Trigger juga jika user menyebut 'BST', 'Bukti Serah Terima', 'langsir', 'input BST', 'pake skill bst', 'pake skill bst data entry', atau 'terjemahkan BST ke Excel'. Skill ini membaca tulisan tangan dari foto, mencocokkan kode artikel dengan fuzzy matching, dan menulis data ke format yang benar di sheet DATA ENTRY. File Excel sumbernya: https://docs.google.com/spreadsheets/d/1hJP-hQ79vxO6cj6O0R-VeiAFh5iyO0F-_N3Mpa8TawQ/edit?gid=948056441#gid=948056441
dn-to-po
by database-zumaAuto-detect Delivery Note (DN) files (PDF or Excel) and convert to Invoice + PO format. When user sends DN file with indicators (DELIVERY NOTE, DN/DDD/ pattern, or Pengiriman Pesanan sheet), immediately ask "Untuk MBB atau UBB?" then convert and deliver 2 files (Invoice for DDD + PO for MBB/UBB). Supports both PDF and Excel formats.
markitdown
by database-zumaConvert any file (PDF, Word, Excel, PPT, image, audio, HTML, CSV, JSON, XML, YouTube URL) to Markdown for LLM processing. Microsoft open-source tool, installed at ~/.local/bin/markitdown. Use as PRE-PROCESSING step when user sends/uploads a file that needs to be read, analyzed, or passed to another skill.
zuma-branch
by database-zumaZuma Indonesia offline retail store network and branch management. Covers 6 branches (Jatim, Jakarta, Sumatra, Sulawesi, Batam, Bali), store categories (RETAIL, NON-RETAIL, EVENT), store formats (mall units and high-street Ruko), stock management, and active events (WILBEX, IMBEX). Use when discussing retail stores, branch operations, store inventory, or retail management.
visualized-planogram-zuma
by database-zumaTool untuk membuat VISUALISASI planogram (bird's-eye floor plan) toko Zuma dari output XLSX planogram Step 1. Menghasilkan gambar layout fisik toko dengan penempatan artikel per hook, color-coded per series/tier. Gunakan setelah planogram Step 1 selesai menghasilkan XLSX. Use when asked to visualize planogram, create store layout image, generate floor plan, or render planogram visually.
planogram-delegate
by database-zumaMANDATORY for ALL planogram requests. Iris runs ONE command only. trigger_planogram_agent.sh handles everything — inbox, state clear, API trigger.
planogram-zuma
by database-zumaTool untuk membuat rekomendasi planogram (display toko) Zuma berdasarkan data sales, denah toko, SPG insight, dan storage capacity. Gunakan ketika user meminta analisa display, rekomendasi planogram, optimasi layout toko, atau alokasi storage.
zuma-sku-context
by database-zumaZuma Indonesia's product SKU categorization, assortments, tiering system, and SKU naming conventions. Use when working with product data, sales analysis, or e-commerce projects.
paperclip-delegate
by database-zumaGeneral-purpose Paperclip delegation skill. Check agent status, delegate tasks to any Paperclip agent (CEO, RO-Agent, future agents). Iris only writes inbox + triggers routine. Never executes tasks herself.
statistical-analysis
by database-zumaStatistical analysis skill for Zuma business data. Compute descriptive stats (mean, median, std dev, percentile), trend analysis, correlation, and basic regression on sales, stock, and performance data. Upgrades Argus/Metis report quality from plain aggregations to statistical insights. Use when user wants 'analisis mendalam', 'trend analysis', 'outlier detection', 'performa toko vs rata-rata', atau butuh angka statistik yang credible.
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.