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...
hydrex
by BankrBotInteract with Hydrex liquidity pools on Base. Use when the user wants to lock HYDX for voting power, check voting power for gauge voting, vote on liquidity pool strategies, view pool information, check voting weights, participate in Hydrex governance, deposit single-sided liquidity into auto-managed vaults to earn Hydrex yields, claim oHYDX rewards from incentive campaigns, or exercise oHYDX into veHYDX. Uses Bankr for transaction execution.
helixa
by BankrBotHelixa — Onchain identity, reputation, and Cred Scores for AI agents on Base. Use when an agent wants to mint an identity NFT, check its Cred Score, verify social accounts, update traits/narrative, query agent reputation data, check staking info, or search the agent directory. Supports SIWA (Sign-In With Agent) auth and x402 micropayments. Also use when asked about Helixa, AgentDNA, ERC-8004, Cred Scores, $CRED token, or agent identity.
zerion
by BankrBotInterpreted crypto wallet data for AI agents. Use when an agent needs portfolio values, token positions, DeFi positions, NFT holdings, transaction history, PnL data, token prices, charts, gas prices, swap quotes, or DApp information across 41+ chains. Zerion transforms raw blockchain data into agent-ready JSON with USD values, protocol labels, and enriched metadata. Supports x402 pay-per-request ($0.01 USDC on Base) and API key access. Triggers on mentions of portfolio, wallet analysis, positions, transactions, PnL, profit/loss, DeFi, token balances, NFTs, swap quotes, gas prices, or Zerion.
zyfai
by BankrBotEarn yield on any Ethereum wallet on Base, Arbitrum, and Plasma. Use when a user wants passive DeFi yield on their funds. Deploys a non-custodial deterministic subaccount (Safe) linked to their EOA, enables automated yield optimization, and lets them deposit/withdraw anytime.
qrcoin
by BankrBotInteract with QR Coin auctions on Base. Use when the user wants to participate in qrcoin.fun QR code auctions — check auction status, view current bids, create new bids, or contribute to existing bids. QR Coin lets you bid to display URLs on QR codes; the highest bidder's URL gets encoded.
onchainkit
by BankrBotBuild onchain applications with React components and TypeScript utilities from Coinbase's OnchainKit. Use when users want to create crypto wallets, swap tokens, mint NFTs, build payments, display blockchain identities, or develop any onchain app functionality. Supports wallet connection, transaction building, token operations, identity management, and complete onchain app development workflows.
agenticbets
by BankrBotPlace prediction bets on token prices on Base via AgenticBets. Use when the user wants to bet UP or DOWN on whether a token price will go up or down, check prediction market odds, view open betting rounds, or claim winnings from settled rounds. Supports all tokens with active markets on AgenticBets (AGBETS, CLAWD, MOLT, WCHAN, and more). Uses Bankr Submit API to execute bet and claim transactions on Base.
aeon-deep-research
by BankrBotExhaustive multi-source research on a topic with attributed claims, a mandatory adversarial counterpoint, and an open-questions list. Analyst-grade — claims are tagged with source class (primary / expert / secondary / market signal) and confidence, contradicting sources are named rather than averaged. Use when the cost of being wrong exceeds an hour of research. Triggers: "deep research X", "DD on Y", "build me a memo on Z", "contrarian take on X".
bankr-shopify
by BankrBotShopify Admin & Storefront GraphQL APIs via curl, with Bankr-native bridges. Manage products, orders, customers, inventory, metafields, webhooks, and bulk ops, then wire merchant data to onchain primitives — store a Bankr-resolvable handle (ENS, Twitter, Farcaster, wallet) on each customer as a metafield, expose Shopify draft orders behind x402 endpoints Bankr settles in USDC, and turn ORDERS_PAID webhooks into Bankr agent jobs for loyalty drops or royalty splits. Triggers: "Shopify products", "Shopify orders", "create draft order", "loyalty drop on order", "x402 checkout", "tokengate Shopify". Core Shopify content adapted with attribution from NousResearch/hermes-agent (MIT).
aeon-hacker-news-digest
by BankrBotTop Hacker News stories filtered by interest tags, with comment-mined insights (top, dissenting, expert/builder) and themed clustering. Comments often beat the post — this skill extracts the highest-signal threads rather than just listing front-page links. Triggers: "top HN today", "hacker news digest", "what's on hn", "best comments today".
trustlayer-sybil-scanner
by BankrBotFeedback forensics for ERC-8004 agents. Detects Sybil rings, fake reviews, rating manipulation, and reputation laundering across 20 chains. No API key needed.
aeon-huggingface-trending
by BankrBotTrending Hugging Face models, datasets, and spaces — filtered by license sanity, dedup vs same-week quantizations, with a "why notable" line per pick (architecture shift, size step, license change, notable author). Surfaces what's actually shifting rather than just popular. Triggers: "trending on HF", "what models are hot", "huggingface trending", "new spaces today", "best new datasets".
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.