Total 54,336 skills, Data Processing has 2781 skills
Showing 12 of 2781 skills
Création, édition et analyse complète de tableurs avec support des formules, du formatage, de l'analyse de données et de la visualisation. Quand Claude doit travailler avec des tableurs (.xlsx, .xlsm, .csv, .tsv, etc.) pour : (1) Créer de nouveaux tableurs avec formules et formatage, (2) Lire ou analyser des données, (3) Modifier des tableurs existants en préservant les formules, (4) Analyse et visualisation de données dans les tableurs, ou (5) Recalculer des formules.
Outscraper platform help — data extraction platform that scrapes Google Maps, reviews, and 50+ sources plus an instant Business Catalog database. Use when scraping Google Maps businesses or reviews, extracting emails/contacts from websites, enriching company data, validating emails, scraping Amazon or review platforms, or working with the Outscraper API. Do NOT use for general prospect list strategy (use /sales-prospect-list), cross-platform enrichment strategy (use /sales-enrich), or email deliverability strategy (use /sales-deliverability).
Fix broken data scrapers and pipelines. Use when data acquisition fails, a scraper breaks, an API returns errors, or data format has changed. Also handles submitting upstream issues or PRs when the problem is in a dependency like soccerdata or kloppy.
Guided, interactive exploration of statistical data via SDMX providers (Eurostat, OECD, ECB, World Bank, ISTAT, and others) using the opensdmx CLI. Use this skill whenever the user asks ANY question about statistics or data that could be answered with SDMX data — even if they don't mention SDMX, Eurostat, or any provider by name. Topics include demographics, economy, employment, births, deaths, population, prices, trade, health, agriculture, GDP, inflation, unemployment, fertility rates, migration, energy, education, poverty, housing, and any other statistical topic. Also use it when the user mentions a specific dataflow ID they want to explore. Trigger this skill even for implicit questions like "how many births were there in Italy last year?" or "I need EU unemployment data by age group" — these clearly need SDMX data even if the user doesn't say so. The skill guides the user step by step: discovers relevant datasets, proposes the most meaningful candidates, explores the schema using real constraints (not codelists), explains the dataset structure, and invites the user to make informed filter choices before fetching any data.
Find, characterize, and source small molecules for chemical biology and drug discovery. Covers compound identification (PubChem, ChEMBL), structure search, binding affinity data, ADMET/drug-likeness prediction, and commercial availability (eMolecules, Enamine). Use when asked to find compounds, assess drug-likeness, search by structure, retrieve binding affinities, or source chemicals.
Build search applications and query log analytics data with OpenSearch. Use this skill when the user mentions OpenSearch, search app, index setup, search architecture, semantic search, vector search, hybrid search, BM25, dense vector, sparse vector, agentic search, RAG, embeddings, KNN, PDF ingestion, document processing, or any related search topic. Also use for log analytics and observability — when the user wants to set up log ingestion, query logs with PPL, analyze error patterns, set up index lifecycle policies, investigate traces, or check stack health. Activate even if the user says log analysis, Fluent Bit, Fluentd, Logstash, syslog, traceId, OpenTelemetry, or log analytics without mentioning OpenSearch.
Vector search best practices for Azure DocumentDB using `cosmosSearch` — choosing between DiskANN / HNSW / IVF, creating indexes, tuning `lBuild` / `lSearch` / `maxDegree`, Product Quantization (up to 16,000 dims), half-precision (fp16) indexing, and normalizing embeddings for cosine similarity. Use when building RAG / semantic-search applications, creating a vector index, tuning recall/latency, or reducing vector-index memory footprint.
Static basic information for all Longbridge-tradable securities — stocks, ETFs, options, warrants: company name, listing date, exchange, industry classification, total shares, circulating shares, market cap, IPO price, website, address. Futures / bonds / funds have limited coverage. Triggers: "基础信息", "股票信息", "上市日期", "总股本", "流通股", "IPO价格", "标的信息", "品种信息", "基礎信息", "股票資料", "上市日期", "總股本", "流通股", "IPO價格", "基本資料", "basic info", "stock info", "listing date", "shares outstanding", "IPO price", "symbol info", "static data", "security info", "exchange listing", "total shares".
Analyst EPS estimate time series for a listed stock via Longbridge — tracks how the consensus EPS forecast (high / low / mean / median / analyst count) has changed over time, and shows actual values where reported. Answers "have analysts been raising or cutting their estimates?" Complements longbridge-consensus (current snapshot) by focusing on the historical revision trajectory. Triggers: "分析师预测历史", "EPS预测趋势", "一致预期变化", "预期上调下调", "分析师预期轨迹", "分析师EPS", "预测时间序列", "分析師預測歷史", "EPS預測趨勢", "一致預期變化", "預期上調下調", "分析師EPS", "analyst estimate history", "EPS estimate trend", "consensus revision history", "estimate time series", "analyst forecast trajectory", "EPS upgrade downgrade history".
Discounted cash flow (DCF) valuation model built from Longbridge financial data — historical FCF (operating cash flow minus capex), projected FCF with growth assumptions, WACC (Beta / risk-free rate / equity risk premium), terminal value, intrinsic value vs current price, and margin of safety. Triggers: "DCF", "现金流折现", "内在价值", "自由现金流", "WACC", "折现率", "安全边际", "终值", "现金流贴现", "現金流折現", "內在價值", "自由現金流", "折現率", "安全邊際", "DCF model", "discounted cash flow", "intrinsic value", "free cash flow", "WACC", "discount rate", "margin of safety", "terminal value", "Gordon growth".
ETF analysis framework via Longbridge — product screening (AUM/expense ratio/index), tracking error, liquidity (bid-ask spread/volume), premium/discount (NAV vs market price), and A-share ETF allocation insights. Triggers: "ETF分析", "ETF选择", "ETF跟踪误差", "ETF溢价", "ETF流动性", "ETF费率", "ETF规模", "宽基ETF", "行业ETF", "指数基金", "ETF分析", "ETF選擇", "ETF追蹤誤差", "ETF溢價", "ETF流動性", "ETF費率", "ETF規模", "指數基金", "ETF analysis", "ETF selection", "tracking error", "ETF premium discount", "ETF liquidity", "expense ratio", "broad market ETF", "sector ETF", "index fund".
Comprehensive market scanner — combines real-time quotes, capital flow (large/medium/small order distribution), and candlestick data for multi-symbol technical analysis (MACD / RSI / Bollinger Bands computed from OHLCV). Supports batch multi-symbol scanning. Triggers: "行情扫描", "综合行情", "多标的扫描", "行情数据", "实时行情综合", "技术+资金综合", "行情指标", "行情監控", "行情掃描", "綜合行情", "多標的掃描", "技術+資金綜合", "market scanner", "comprehensive quote", "multi-stock scan", "real-time data", "market data query", "technical plus capital flow", "market overview", "batch quote", "technical indicators", "MACD RSI scan".