emblem-defi-yield은(는) 무엇을 하나요?
EmblemAI를 통한 DeFi 수익률 조사 및 유동성 스테이킹. 수익 기회 발견, 프로토콜 비교, Nansen을 통한 DeFi 포지션 확인, 토큰 스왑을 통한 유동성 스테이킹 진입을 지원합니다. 사용자가 수익률을 조사하거나, 스테이킹 옵션을 찾거나, DeFi 포지션을 검토하려 할 때 사용하세요.
EmblemAI를 통한 DeFi 수익률 조사 및 유동성 스테이킹. 수익 기회 발견, 프로토콜 비교, Nansen을 통한 DeFi 포지션 확인, 토큰 스왑을 통한 유동성 스테이킹 진입을 지원합니다. 사용자가 수익률을 조사하거나, 스테이킹 옵션을 찾거나, DeFi 포지션을 검토하려 할 때 사용하세요.
이 스킬은 사용자가 EmblemAI 플랫폼을 활용하여 DeFi(탈중앙화 금융) 수익률 조사 및 유동성 스테이킹 작업을 수행할 수 있도록 지원합니다. 주요 기능으로는 다양한 DeFi 프로토콜의 수익 기회를 발견하고 비교하며, Nansen 데이터를 통해 보유한 DeFi 포지션을 확인하는 것이 포함됩니다. 또한 토큰 스왑을 통해 유동성 스테이킹에 진입할 수 있습니다. 이 스킬은 사용자가 투자 수익률을 극대화하거나, 보유 자산의 스테이킹 옵션을 탐색하거나, 기존 DeFi 포지션을 종합적으로 검토해야 할 때 이상적입니다.
npx skills add https://github.com/emblemcompany/agent-skills/tree/main/skills/emblem-defi-yieldInstalled? Explore more 연구 & 데이터 분석 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
EmblemAI를 통한 DeFi 수익률 조사 및 유동성 스테이킹. 수익 기회 발견, 프로토콜 비교, Nansen을 통한 DeFi 포지션 확인, 토큰 스왑을 통한 유동성 스테이킹 진입을 지원합니다. 사용자가 수익률을 조사하거나, 스테이킹 옵션을 찾거나, DeFi 포지션을 검토하려 할 때 사용하세요.
This repo contains 8 individual skills — each has its own dedicated page.
One-shot user management for apps, multi-chain wallet authentication, an AI-powered assistant, and AI app introspection. Use when the user wants to let website users sign in with wallets, email/password, or social login and give each user a wallet-enabled account, then embed EmblemAI chat surfaces, connect plugins, or add Reflexive observability. Provides React components, TypeScript SDKs, session-based authentication, and pointers to the React and agent-wallet skills for specialized workflows.
Connect to EmblemVault and manage wallet-aware workflows via EmblemAI with review-first, operator-controlled actions. Supports Solana, Ethereum, Base, BSC, Polygon, Hedera, and Bitcoin. Also use when the user needs Emblem's auth model explained: one browser auth flow can log a user in with wallets, email/password, or social sign-in, while agent mode can auto-provision a profile-scoped wallet with no manual setup.
Curated prompt and usage examples for research, portfolio review, quote requests, approval-gated drafts, NFT discovery, prediction-market analysis, and assistant workflows. Emphasis is review-first, trust-boundary-aware use of external data, and explicit confirmation before any value-moving action. Use when the user wants example prompts, phrasing guidance, or sample requests for end-user EmblemAI tasks.
React integration skill for EmblemAI's one-shot user management. Use when the user wants to add website authentication plus wallet-enabled users to a React app with EmblemAuthProvider, ConnectButton, social/email/wallet login, chat components, React auth hooks, React composition patterns, or Migrate.fun React hooks.
Crypto market intelligence via EmblemAI. Trending tokens, on-chain analytics, derivatives data, and smart money tracking from CoinGecko, CoinGlass, Birdeye, and Nansen. Use when the user wants market data, trending tokens, derivatives analytics, or on-chain intelligence.
Memecoin discovery and risk assessment via EmblemAI. Trending memecoins on Solana, Base, and Hedera. Pump.fun and LaunchLab new token alerts, Clanker discovery, rug-pull detection, holder analysis, and smart money tracking. Use when the user wants to find new memecoins, check if a token is a rug pull, or scout trending low-cap tokens.
Track crypto portfolio across 7 blockchains via EmblemAI. Aggregated balances with USD values, conditional trade P&L, and DeFi position tracking via Nansen. Use when the user wants to check their portfolio, see balances across chains, or review trade positions.
Execute token swaps across 6 blockchains via EmblemAI. Automatic route optimization and cross-chain bridging via ChangeNow. Use when the user wants to swap tokens, exchange crypto, convert between currencies, or bridge assets cross-chain.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.