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Found 271 Skills
Invoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
Generate a pre-earnings briefing for any stock using Yahoo Finance data. Use this skill whenever the user wants to prepare for an upcoming earnings report, understand what analysts expect, review a company's beat/miss track record, or get a quick overview before an earnings call. Triggers include: "earnings preview for AAPL", "what to expect from TSLA earnings", "MSFT reports next week", "earnings preview", "pre-earnings analysis", "what are analysts expecting for NVDA", "earnings estimates for", "will GOOGL beat earnings", "earnings beat/miss history", "upcoming earnings", "before earnings", "earnings setup", "consensus estimates", "earnings whisper", "EPS expectations", "what's the street expecting", "earnings season preview", any mention of preparing for or previewing an earnings report, or any request to understand expectations ahead of a company's earnings date. Always use this skill when the user mentions a ticker in context of upcoming earnings, even if they don't say "preview" explicitly.
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.
Exécute des requêtes SQL en lecture seule sur plusieurs bases de données PostgreSQL. À utiliser pour : (1) interroger des bases PostgreSQL, (2) explorer les schémas/tables, (3) exécuter des requêtes SELECT pour l'analyse de données, (4) vérifier le contenu des bases. Supporte plusieurs connexions avec descriptions pour une sélection automatique intelligente. Bloque toutes les opérations d'écriture (INSERT, UPDATE, DELETE, DROP, etc.) par sécurité.
Structured equity research database for 1,735 Taiwan-listed companies with wikilink knowledge graph, supply chain mapping, and financial data tools.
Amazon Athena integration. Manage data, records, and automate workflows. Use when the user wants to interact with Amazon Athena data.
Explore, interpret, and draw conclusions from football data. Use when the user wants to analyse match events, compare teams or players, understand tactical patterns, build visualisations, or needs guidance on what questions to ask of their data. Adapts to the user's experience level.
Marketing Miner integration. Manage data, records, and automate workflows. Use when the user wants to interact with Marketing Miner data.
Use this skill whenever the user wants to work with survey data using the `survy` Python library. Triggers include: loading or reading survey CSV/Excel/JSON/SPSS files, handling multiselect (multi-choice) questions, computing frequency tables or crosstabs, exporting survey data to SPSS (.sav) or other formats, updating variable labels or value indices, transforming survey data between wide/compact formats, filtering respondents, replacing values, adding/dropping/sorting variables, or any task involving survy's API (read_csv, read_excel, read_json, read_polars, read_spss, crosstab, survey["Q1"], to_spss, to_csv, to_excel, to_json, etc.). Also trigger when the user says things like "analyze my survey", "process questionnaire data", "build a survey analysis script", or "help me with survy". Always read this skill before writing any survy code — it contains the correct API, patterns, and gotchas.
Conduct compensation benchmarking analysis to position salaries against market data. Use this skill when the user needs to assess pay competitiveness, build salary bands, or analyze pay equity — even if they say 'are we paying market rate', 'salary benchmarking', or 'compensation analysis'.
Explains what blockchain intelligence is, standard tool categories (explorers, dashboards, tracers, visualizers), and traditional vs crypto payment rails context. Use when the user asks what blockchain intelligence means, how to read on-chain data at a high level, SWIFT vs settlement, stablecoin rails, or which classes of tools exist for chain analysis.
Use Dune MCP through UXC for blockchain table discovery, SQL query creation/execution, execution result retrieval, and visualization with help-first schema inspection, explicit auth binding, and guarded credit-consuming operations.