研究与知识

研究、检索、摘要和知识工作

1098 个 Skills 可用

显示 24 个结果

gmgn-market

Get crypto and meme token price charts (K-line, candlestick, OHLCV), trending meme coin rankings by volume, newly launched tokens on launchpads (pump.fun, fourmeme, letsbonk, Raydium, etc.), and the hot-search ranking (most-searched tokens) via GMGN API on Solana, BSC, Base, or Ethereum. Use when user asks for price chart, trending tokens, what's pumping, hot coins, most searched tokens, new launches, token signals, or wants to discover early-stage opportunities.

gmgnai

获取

gmgn-token

377research-knowledge

Research any crypto or meme token by address — real-time price, market cap, liquidity, holder list, trader list, top Smart Money and KOL positions, security audit (honeypot, rug pull risk, dev wallet, renounced status), social links (Twitter/X, website) via GMGN API on Solana, BSC, Base, or Ethereum. Use when user asks about a token's price, safety, holders, traders, smart money exposure, or wants due diligence before buying.

gmgnai

获取

gmgn-portfolio

377research-knowledge

Analyze any crypto wallet by address — holdings, realized/unrealized P&L, win rate, trading history, performance stats, specific token balance, and tokens created by a developer wallet (with ATH market cap and DEX graduation status) via GMGN API on Solana, BSC, Base, or Ethereum. Use when user asks about a wallet's holdings, P&L, win rate, what tokens a dev has launched, the highest ATH token a dev ever created, or wants a wallet report to decide whether to copy-trade or follow.

gmgnai

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gmgn-swap

377research-knowledge

[FINANCIAL EXECUTION] Buy and sell meme coins and crypto tokens on Solana, BSC, Base, or Ethereum — single swap, multi-wallet batch trading, limit orders, stop loss, take profit, trailing stop loss, trailing take profit via GMGN API. Requires explicit user confirmation. Use when user asks to buy, sell, or swap a token, trade from multiple wallets, set a limit order, stop loss, take profit, or check order status.

gmgnai

获取

gmgn-track

377research-knowledge

通过 GMGN API 获取来自聪明钱钱包、KOL 影响者钱包以及个人关注钱包的实时加密货币买卖活动——包括 Alpha 信号、巨鲸追踪、Meme 币跟单思路，支持 Solana、BSC、Base 或 Ethereum。同时可查询某个钱包在 GMGN 上关注（收藏）的代币。当用户询问聪明钱或 KOL 正在买入什么、需要巨鲸提醒、链上 Alpha、跟单信号，或想查看某个钱包的关注代币时使用。（如需查询特定钱包地址的投资组合，请使用 gmgn-portfolio。）

gmgnai

获取

skill-creator

365research-knowledge

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

brianlovin

获取

playwriter

365research-knowledge

Control the user's currently open Chrome tab through the Playwriter CLI (no new browser launch). Use when you need to inspect live UI state, run scripted browser actions, capture console output, or reproduce frontend issues directly in the user's tab.

brianlovin

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firebase-basics

357research-knowledge

提供使用 Firebase CLI 进行 Firebase 基础设置、身份验证和项目管理工作流程。用于检查 Firebase CLI 版本（必须使用 'npx -y firebase-tools@latest --version'）、初始化 Firebase 环境、身份验证、设置活动项目，或配置 google-services.json 或 GoogleService-Info.plist 文件。

firebase

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wechatpay-payment-integration

346research-knowledge

微信支付（WeChat Pay）相关问题的统一入口，处理与微信支付接入、产品、开发、运营、品牌经营相关的咨询，提供产品选型、官方示例代码、接入质量评估、答疑与排障。Use when user mentions "微信支付", "微信收款", "WeChat Pay", "JSAPI", "APP支付", "H5支付", "Native支付", "扫码支付", "付款码", "小程序支付", "合单支付", "医保支付", "微信支付分", "分账", "转账", "委托代扣", "周期扣款", "代金券", "商家券", "特约商户进件", "服务商", "sub_mchid", "APIv2", "APIv3", "回调", "签名", "证书", "错误码", "OpenID", "品牌经营", "品牌经营平台", "品牌门店", "商家名片", "名片会员", "商品券", "摇一摇有优惠", "摇优惠", "品牌入驻", or asks to "推荐支付方式/产品选型", "要接口或示例代码", "做接入代码质量审查/上线前检查", "解释字段含义或接口规则", "排查报错/查单/支付问题".

wechatpay-apiv3

获取

rag

329research-knowledge

Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.

giuseppe-trisciuoglio

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memory-md-management

329research-knowledge

Provides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates with user approval.

giuseppe-trisciuoglio

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langchain4j-rag-implementation-patterns

322research-knowledge

Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.

giuseppe-trisciuoglio

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langchain4j-spring-boot-integration

322research-knowledge

Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.

giuseppe-trisciuoglio

获取

langchain4j-vector-stores-configuration

322research-knowledge

Provides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.

giuseppe-trisciuoglio

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qdrant

322research-knowledge

Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.

giuseppe-trisciuoglio

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chunking-strategy

321research-knowledge

Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.

giuseppe-trisciuoglio

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