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Found 326 Skills
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
Detect and classify candlestick patterns from ingested OHLCV data
Guide pharmacogenomics (PGx) research -- drug-gene interaction lookup, CPIC guideline retrieval, variant-drug annotation, allele function status, FDA biomarker labeling, and clinical dosing recommendations. Covers the full CPIC-to-PharmGKB-to-clinical-recommendation workflow. Use when users ask about pharmacogenomics, drug-gene interactions, CPIC guidelines, genotype-guided dosing, PGx biomarkers, CYP enzyme phenotypes, or star allele interpretation.
Create, visualize, and analyze lithological and stratigraphic logs for well data. Use when Claude needs to: (1) Create lithology columns from depth intervals, (2) Parse geological descriptions into structured logs, (3) Visualize stratigraphic columns with patterns and colors, (4) Perform well-to-well correlations, (5) Extract statistics like net-to-gross ratios, (6) Define rock type lexicons and legends, (7) Export lithology data to CSV/LAS/JSON.
Quick BI-SmartQ skill with multiple data analysis capabilities: 1. **File Q&A**: Upload Excel/CSV files for intelligent analysis via Quick BI API 2. **Dataset Q&A**: Natural language queries on Quick BI platform datasets, with automatic intelligent table selection and matching 3. **Document Parsing**: Parse PDF/Word/Excel/CSV/images, extract text, and support extracting key fields to generate structured Excel 4. **Dashboard Skill Generation**: Auto-convert QuickBI dashboards into data query skills 5. **Data Insight**: Deep data insight analysis on Quick BI datasets 6. **Data Report**: Auto-generate professional data reports based on analysis results Use when users mention data analysis, smart Q&A, querying data, file analysis, document parsing, dashboard skills, data insight, or data reports.
Especialista em pesquisa, análise de dados e gestão do conhecimento no SynkOS. Use esta skill quando o usuário pedir para pesquisar um tema, analisar dados ou evidências, consolidar conhecimento disperso, criar ou atualizar páginas de wiki, documentar achados de discovery, extrair padrões de múltiplas fontes, ou responder perguntas como "o que já sabemos sobre X?", "quais são os riscos de Y?", "pesquise Z e documente". Ative também para auditar a saúde do wiki, promover padrões para memória persistente, ou quando o contexto precisar ser levantado antes de uma decisão técnica ou de produto.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
Retrieve detailed revenue breakdown by product segment for public companies. Use when analyzing product mix, revenue concentration, segment contribution, or business line performance.
Analyze Management Discussion and Analysis (MD&A) sections from SEC filings using Octagon MCP. Use when extracting strategic initiatives, financial performance commentary, macroeconomic challenges, and forward-looking statements from 10-K and 10-Q filings.
Retrieve detailed revenue breakdown by geographic segment for public companies. Use when analyzing regional exposure, geographic concentration, international expansion, or currency risk assessment.
Retrieve historical market capitalization data for any stock using Octagon MCP. Use when tracking market cap changes over time, analyzing valuation trends, identifying peak and trough valuations, and comparing historical size classifications.
A Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Great for exploring relationships between variables and visualizing distributions. Use for statistical data visualization, exploratory data analysis (EDA), relationship plots, distribution plots, categorical comparisons, regression visualization, heatmaps, cluster maps, and creating publication-quality statistical graphics from Pandas DataFrames.