emblem-memecoin-scout 是做什么的？
通过 EmblemAI 进行 Memecoin 发现与风险评估。支持 Solana、Base 和 Hedera 上的热门 Memecoin，包括 Pump.fun 和 LaunchLab 新代币提醒、Clanker 发现、Rug Pull 检测、持仓分析和聪明钱追踪。当用户想要寻找新的 Memecoin、检查代币是否为 Rug Pull，或发现热门低市值代币时使用。
通过 EmblemAI 进行 Memecoin 发现与风险评估。支持 Solana、Base 和 Hedera 上的热门 Memecoin，包括 Pump.fun 和 LaunchLab 新代币提醒、Clanker 发现、Rug Pull 检测、持仓分析和聪明钱追踪。
此技能利用 EmblemAI 平台为用户提供 Memecoin 发现与风险评估的一站式解决方案。它支持在 Solana、Base 和 Hedera 区块链上追踪热门 Memecoin，并能监控 Pump.fun 和 LaunchLab 等平台的新代币发行。该技能的核心功能包括 Rug Pull 检测（通过持仓分布、流动性锁定等指标）、持有者分析以及聪明钱追踪，帮助用户识别高风险代币和潜在机会。当用户希望寻找新的 Memecoin、验证代币是否安全，或者发现低市值热门代币时，可以直接使用该技能。它主要面向 AI 代理（如 Claude Code、Cursor 等），但开发者也可以将其集成到自己的工具链中。技能通过调用 EmblemAI 的 API 接口来获取实时数据，并以 JSON 或自然语言形式返回结果。
npx skills add https://github.com/emblemcompany/agent-skills/tree/main/skills/emblem-memecoin-scoutInstalled? Explore more 研究与数据分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
通过 EmblemAI 进行 Memecoin 发现与风险评估。支持 Solana、Base 和 Hedera 上的热门 Memecoin，包括 Pump.fun 和 LaunchLab 新代币提醒、Clanker 发现、Rug Pull 检测、持仓分析和聪明钱追踪。当用户想要寻找新的 Memecoin、检查代币是否为 Rug Pull，或发现热门低市值代币时使用。
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.
DeFi yield research and liquid staking via EmblemAI. Discover yield opportunities, compare protocols, check DeFi positions with Nansen, and enter liquid staking via token swaps. Use when the user wants to research yields, find staking options, or review DeFi positions.
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.
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.