Loading...
Loading...
Found 217 Skills
This skill should be used when analyzing CSV datasets, handling missing values through intelligent imputation, and creating interactive dashboards to visualize data trends. Use this skill for tasks involving data quality assessment, automated missing value detection and filling, statistical analysis, and generating Plotly Dash dashboards for exploratory data analysis.
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for creating new spreadsheets, reading/analyzing data, modifying existing spreadsheets, or recalculating formulas.
Synthesize qualitative and quantitative user research into structured insights and opportunity areas. Use when analyzing interview notes, survey responses, support tickets, or behavioral data to identify themes, build personas, or prioritize opportunities.
Provides trading strategies for cryptocurrencies based on Binance market data, calculated technical analysis indicators, and aggregated market sentiment from crypto RSS news feeds. Use when users ask for trading advice, strategy recommendations, or analysis combining price data, TA, and sentiment for crypto assets like ETH, BTC, or altcoins.
QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
Data analysis best practices with pandas, numpy, matplotlib, seaborn, and Jupyter notebooks.
Guidelines for data analysis and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
Screen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system.
Panel data analysis with Python using linearmodels and pandas.
This skill should be used when analyzing business sales and revenue data from CSV files to identify weak areas, generate statistical insights, and provide strategic improvement recommendations. Use when the user requests a business performance report, asks to analyze sales data, wants to identify areas of weakness, or needs recommendations on business improvement strategies.
Identify stocks where blogger sentiment has changed significantly. Use when users want to find who changed their mind or detect sentiment reversals.