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Found 278 Skills
Install or update DuckDB extensions. Each argument is either a plain extension name (installs from core) or name@repo (e.g. magic@community). Pass --update to update extensions instead of installing.
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.
Calculate derived football metrics and models. Use when the user wants to compute xG, xGOT, PPDA, passing networks, expected threat, possession value, pressing intensity, or any derived football statistic from raw data.
TransForm integration. Manage data, records, and automate workflows. Use when the user wants to interact with TransForm data.
Diagnose, compare, and optimize Apache Spark applications and SQL queries using Spark History Server data. Use this skill whenever the user wants to understand why a Spark app is slow, compare two benchmark runs or TPC-DS results, find performance bottlenecks (skew, GC pressure, shuffle spill, straggler tasks), get tuning recommendations, or optimize Spark/Gluten configurations. Also trigger when the user mentions 'diagnose', 'compare runs', 'why is this query slow', 'tune my Spark job', 'benchmark comparison', 'performance regression', or asks about executor skew, shuffle overhead, AQE effectiveness, or Gluten offloading issues.
Aylien News API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Aylien News API data.
Python data processing with pandas, openpyxl, and lxml. Covers DataFrame operations, Excel I/O, XML parsing, bulk data transformation, and large-file handling. Use when processing tabular data, spreadsheets, or XML in Python. USE WHEN: user mentions "pandas", "DataFrame", "openpyxl", "read_excel", "lxml", "XPath", "CSV processing", "Excel parsing", "bulk data", "large file", "data transformation", "UTF-16", "codecs" DO NOT USE FOR: SQL databases (use sql-expert), NumPy-only math, ML/training
Finage integration. Manage data, records, and automate workflows. Use when the user wants to interact with Finage data.
Local execution tools for Xiaohongshu/Rednote hosted collection workflows, including actor runs, dataset normalization, account and post ranking, comment clustering, product-pool ranking, and topic-map building.
Regulatory variant interpretation -- GWAS association lookup, eQTL analysis, chromatin state annotation, regulatory element overlap, and trait ontology resolution. Connects GWAS Catalog, GTEx, ENCODE, RegulomeDB, OpenTargets, OLS ontology, and Ensembl regulatory features. Use when users ask about non-coding variants, GWAS hits, eQTLs, regulatory elements, enhancer/promoter variants, or trait-associated SNPs.
Elliott Wave Theory Signal Engine — Detects swing points via Zigzag, matches 5-wave impulse structures (1-2-3-4-5) and 3-wave corrective structures (A-B-C), validates with Fibonacci relationships, and generates wave positions, target prices, and risk levels. Triggers: "艾略特波浪", "波浪理论", "推动浪", "调整浪", "斐波那契", "1浪", "3浪", "5浪", "abc浪", "艾略特", "波浪計數", "推動浪", "調整浪", "斐波那契", "Elliott wave", "wave theory", "impulse wave", "corrective wave", "fibonacci retracement", "wave count", "wave 3", "wave 5".
Sector screening and ranking — filter and rank A-share / HK / US industry sectors by valuation (PE/PB), capital inflow, price performance (1d/5d/20d), and turnover rate. Outputs a sector leaderboard. Triggers: "板块筛选", "行业筛选", "强势板块", "弱势板块", "板块排行", "行业排名", "资金流入板块", "涨幅最大板块", "板塊篩選", "行業篩選", "強勢板塊", "弱勢板塊", "板塊排行", "行業排名", "sector screener", "sector filter", "sector ranking", "top sectors", "hot sectors", "capital inflow sectors", "sector scan", "industry ranking", "sector performance", "best sectors today".