Data Science

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

Fast in-process analytical database for SQL queries on DataFrames, CSV, Parquet, JSON files, and more. Use when user wants to perform SQL analytics on data files or Python DataFrames (pandas, Polars), run complex aggregations, joins, or window functions, or query external data sources without loading into memory. Best for analytical workloads, OLAP queries, and data exploration.

This skill activates when you're debugging TypeScript/JavaScript build errors, compilation failures, or type mismatches. Provides patterns for common errors, root cause analysis techniques, and fixes for type-related issues across frameworks.

Build data visualizations with visx (React + D3). Use for charts, graphs, and interactive data exploration.

Generate fractal horn geometries using space-filling curves and Mandelbrot expansion.Use when designing horn topology, creating geometry variations, or exploring fractalapproaches for acoustic optimization. Produces STL meshes and fractal analysis data.

Deep methodology knowledge for pairwise meta-analysis including fixed vs random effects, heterogeneity assessment, publication bias, and sensitivity analysis. Use when conducting or reviewing pairwise MA.

Analyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, generating a personalized growth report with actionable recommendations.

Phase 1 of Ontology Builder Pipeline. Ingests and catalogs all input materials from _input/ folder. Use when starting ontology building process or when processing new input documents for domain analysis.

Deep Python code review of changed files using git diff analysis. Focuses on production quality, security vulnerabilities, performance bottlenecks, architectural issues, and subtle bugs in code changes. Analyzes correctness, efficiency, scalability, and production readiness of modifications. Use for pull request reviews, commit reviews, security audits of changes, and pre-deployment validation. Supports Django, Flask, FastAPI, pandas, and ML frameworks.

Construct ASCII tree visualizations from parent/child work item data. Use when the user asks to "show hierarchy", "show tree", "display parent child", "visualize structure", "show feature tree", or wants to see work item relationships as a tree diagram. This skill teaches how to build trees from flat data WITHOUT code - use LLM reasoning only.

Instructions for using GPU-loaded models in your notebook environment. Use when working with models that are pre-loaded on GPU.

Database optimization, security audit, and performance analysis for MyDetailArea Supabase/PostgreSQL. Provides safe query optimization, RLS policy review, index recommendations, and migration strategies with extreme caution and rollback plans. Use when optimizing database performance, auditing security, or creating safe migrations. CRITICAL - All recommendations require validation and testing before production.

Conducts web research and synthesizes findings into structured analysis

Comprehensive React Native screen analysis tool that systematically extracts every feature, component, interaction, and detail from existing screens. Use BEFORE recreating ANY screen to ensure 100% feature parity. Triggers when user says "analyze screen", "analyze [filename]", "extract features from", or before screen recreation.

This skill should be used when users request comprehensive analysis of technology ecosystems, comparing multiple libraries/frameworks/tools with quantitative metrics from GitHub and web research. Trigger words include "ecosystem analysis", "compare libraries", "analyze React/Vue/Python ecosystem", "trending libraries", "technology stack comparison", or requests to evaluate multiple technical tools with data-driven insights.

Generates professional markdown comparison report with tables, executive summary, and verdict by use case. Use when user asks to 'generate report', 'create comparison report', 'synthesize comparison', 'write comparison', or when orchestrator has completed all data collection. Creates structured report with specs tables, pros/cons, pricing analysis, and actionable recommendations.

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: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

R 4.4+ best practices with testthat 3.2, lintr 3.2, and data analysis patterns.

Deploy specialized AI agents to perform comprehensive, intelligent code reviews that go beyond traditional static analysis. Use for automated multi-agent review, security vulnerability analysis, performance bottleneck detection, and architecture pattern validation.

Apply Charlie Munger's latticework of mental models to any problem. Use when user requests decision analysis, says "help me think", "apply mental model", mentions model names (inversion, bottlenecks, second-order thinking), or needs structured thinking frameworks.