Browse Skills

Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.

Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.

Profile and explore a dataset to understand its structure, quality, and patterns. Helps when encountering new data—checks null rates, column distributions, and identifies issues like duplicates or outliers.

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types.

Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use for analyzing RNA-seq, WGS/WES, or ATAC-seq data from local FASTQs or public GEO/SRA datasets.

Provides deep learning-based single-cell analysis workflows using scvi-tools for data integration, batch correction, multi-modal analysis, spatial transcriptomics, and RNA velocity.

Performs quality control on single-cell RNA-seq data using scverse best practices with MAD-based filtering and comprehensive visualizations. Use for QC analysis, filtering low-quality cells, or assessing data quality in .h5ad or .h5 files.

Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries.

Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.

Translates natural language data requests into optimized SQL queries tailored to your database dialect. Ideal for building complex queries with CTEs, joins, and aggregations, and for performance optimization on large partitioned tables.

Reconcile accounts by comparing GL balances to subledgers, bank statements, or third-party data. Use when performing bank reconciliations, GL-to-subledger recs, intercompany reconciliations, or identifying and categorizing reconciling items.

Context Mode Insight opens the hosted analytics dashboard in your default browser so engineering teams can view per-engineer productive rate, retry waste, blocker detection, and role-narrowed views. Use it when you need to access the Context Mode analytics layer quickly.