Total 53,816 skills, Data Processing has 2767 skills
Showing 12 of 2767 skills
>
Expert in quantitative finance, algorithmic trading, and financial data analysis using Python (Pandas/NumPy), statistical modeling, and machine learning.
SQLiteData advanced patterns, @Selection column groups, single-table inheritance, recursive CTEs, database views, custom aggregates, TableAlias self-joins, JSON/string aggregation
Chief Data Officer advisory for startups: AI training data rights and consent provenance, data product strategy (warehouse vs lakehouse vs mesh, build-vs-buy), B2B customer-data-as-asset valuation and M&A readiness, data team org evolution. Use when deciding whether to train models on customer data, choosing data architecture, valuing data for fundraising or M&A, sequencing data hires, or when user mentions CDO, chief data officer, data strategy, data mesh, lakehouse, training data, data product, data monetization, or customer data asset. NOT a tactical data engineering skill — strategic decisions only.
Production-ready VCF processing, variant annotation, mutation analysis, and structural variant (SV/CNV) interpretation for bioinformatics questions. Parses VCF files (streaming, large files), classifies mutation types (missense, nonsense, synonymous, frameshift, splice, intronic, intergenic) and structural variants (deletions, duplications, inversions, translocations), applies VAF/depth/quality/consequence filters, annotates with ClinVar/dbSNP/gnomAD/CADD via ToolUniverse, interprets SV/CNV clinical significance using ClinGen dosage sensitivity scores, computes variant statistics, and generates reports. Solves questions like "What fraction of variants with VAF < 0.3 are missense?", "How many non-reference variants remain after filtering intronic/intergenic?", "What is the pathogenicity of this deletion affecting BRCA1?", or "Which dosage-sensitive genes overlap this CNV?". Use when processing VCF files, annotating variants, filtering by VAF/depth/consequence, classifying mutations, interpreting structural variants, assessing CNV pathogenicity, comparing cohorts, or answering variant analysis questions.
Converts legacy SQL to modular dbt models. Use when migrating SQL to dbt for: (1) Converting stored procedures, views, or raw SQL files to dbt models (2) Task mentions "migrate", "convert", "legacy SQL", "transform to dbt", or "modernize" (3) Breaking monolithic queries into modular layers (discovers project conventions first) (4) Porting existing data pipelines or ETL to dbt patterns Checks for existing models/sources, builds and validates layer by layer.
Magnetotelluric data processing and modelling. Read EDI files, analyze MT responses, perform inversions, and visualize resistivity models. Use when Claude needs to: (1) Read/write EDI files, (2) Process MT impedance tensors, (3) Analyze phase tensors and dimensionality, (4) Plot apparent resistivity and phase curves, (5) Create pseudosections, (6) Perform strike analysis, (7) Run 1D inversions, (8) Prepare data for 2D/3D modelling.
LP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.
This skill should be used when the user needs to query COSMIC Cancer Gene Census to check if genes are known cancer genes. Triggers include requests to annotate genes with cancer information, check if variants are in cancer genes, or retrieve cancer gene properties from COSMIC database.
Publication-ready matplotlib figures for Nature/high-impact journals and academic papers. Covers bar charts, grouped bars, heatmaps, line/trend plots, forest plots, microscopy-style image panels, schematic + quantitative composites, radar plots, and multi-panel layouts with Nature-style typography (Arial/sans-serif), restrained color systems, and SVG/PDF export conventions. Use when creating scientific figures that must match Nature publication standards. Do NOT use for interactive dashboards (Plotly, Bokeh) or Illustrator/Figma-first infographic workflows.
从智慧芽（PatSnap）获取专利权利要求数据。当用户提到专利权利要求、权利要求文本、独立权利要求、从属权利要求、权利要求数量、权利要求树、权利要求分析、权利要求范围、权利要求语言、想查看特定专利的权利要求部分、patent claims, independent claims, dependent claims, claims text, PatSnap时触发此技能。即使用户未明确提及"智慧芽"或"PatSnap"，只要其请求涉及通过专利ID或公开号获取或分析专利权利要求信息，也应触发此技能。
对产品标题进行分词分析，提取词频、场景词、人群词、材质词等属性维度。当用户想分析产品标题、提取标题高频词、进行标题分词、发现场景词或人群词、对比不同商品的标题关键词用法、基于词频优化Listing标题、识别一组ASIN中的常见属性规律、title tokenization, word frequency analysis, scene keyword extraction, audience keyword analysis, title optimization, attribute keyword extraction, keyword frequency时触发此技能。即使用户未明确说"标题分析"，只要其需求涉及将产品标题拆解为有意义的词组、统计关键词频率或按提取的属性对商品分组，也应触发此技能。