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Found 217 Skills
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.
Analyze nutrition data, identify nutrition patterns, assess nutritional status, and provide personalized nutrition recommendations. Supports correlation analysis with exercise, sleep, and chronic disease data.
Best practices for Pandas data manipulation, analysis, and DataFrame operations in Python
Identify non-obvious signals, hidden patterns, and clever correlations in datasets using investigative data analysis techniques. Use when analyzing social media exports, user data, behavioral datasets, or any structured data where deeper insights are desired. Pairs with personality-profiler for enhanced signal extraction. Triggers on requests like "what patterns do you see", "find hidden signals", "correlate these datasets", "what am I missing in this data", "analyze across datasets", "find non-obvious insights", or when users want to go beyond surface-level analysis. Also use proactively when you notice interesting anomalies or correlations during any data analysis task.
Specialized utility for advanced manipulation, analysis, and creation of spreadsheet files, including (but not limited to) XLSX, XLSM, CSV formats. Core functionalities include formula deployment, complex formatting (including automatic currency formatting for financial tasks), data visualization, and mandatory post-processing recalculation.