Skill UI

Guides creation of competitor comparison or alternative landing pages for SEO and sales enablement, detailing honesty-first messaging, multiple page formats, and centralized competitor data so evaluators can choose confidently.

This advanced workflow enables users to perform deep analyses on large-scale people intelligence datasets. Users can build custom queries with multi-dimensional filters, run comparisons across different candidate pools (e.g., Q1 vs Q4), and aggregate skill density by geographical region. It is essential for talent acquisition teams needing to identify market trends and optimize hiring strategies beyond standard search functions.

Blueprint for building SEO-optimized pages at scale using data-driven templates, focused on unique value, clean architecture, indexing, and quality governance. Ideal for teams creating directory, comparison, or location landing pages.

This comprehensive guide provides a phased strategy for migrating error tracking services from legacy tools like Rollbar, Bugsnag, or New Relic to Sentry. It covers crucial steps including concept mapping between diverse providers, implementing safe parallel reporting (dual-write), validating data parity via API comparison, and performing the final cutover to ensure continuous monitoring and zero downtime.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

This tool analyzes ML experiment results from various paths (JSON/CSV). It computes comprehensive statistics, builds detailed comparison tables based on model types and hyperparameters, and generates actionable insights. Use it when you need to interpret raw experimental data, compare different model runs, or determine the next steps for research.

Generate publication-quality figures and comparison tables from experiment results for academic papers. Automatically selects plot types from data and produces vector PDFs at 300 DPI.

This skill provides institutional-depth equity analysis by combining public SEC EDGAR filings and real-time market data. It can generate structured, verdict-style memos, score stocks across four key pillars (Momentum, Stability, Financial Health, Upside), and perform side-by-side comparisons of two tickers, enabling data-driven investment decision support without requiring proprietary terminal access.

This skill guides developers in writing robust and defensive Go code. It addresses common pitfalls that lead to runtime panics and silent data corruption, such as nil map writes, slice backing array aliasing, silent numeric truncation (int64 to int32), and inaccurate float comparisons. Use this when designing highly reliable Go systems that must withstand typical programmer errors and maintain data integrity.

This skill automatically generates an interactive SQL Notebook designed to validate changes in dbt models or snapshots. It processes changes identified from a GitHub PR or local repository, generating comparison queries that compare 'before' (production) and 'after' (development) data states. It is essential for embedding data observability and quality checks into CI/CD pipelines.

Programmatically query the CZ CELLxGENE Census for versioned single-cell and spatial transcriptomics data, including population-scale cell metadata, gene expression slices, summary counts, source H5AD URIs, embeddings, and reference atlas comparisons.

A comprehensive Python command-line toolkit for processing and visualizing high-throughput sequencing data including ChIP-seq, RNA-seq, and ATAC-seq. Performs quality control, normalization, sample comparison, and generates publication-quality genomic visualizations with bigWig conversion and heatmap profiles.