Data Science

GitHub Actions workflow automation for intelligent CI/CD pipelines with adaptive optimization. Use for workflow creation, pipeline optimization, security scanning, failure analysis, and automated deployment strategies.

Creates slash commands for Claude Code following Sando's Golden Rule - only create commands that add intelligent value over bash (analysis, insights, multi-source aggregation, troubleshooting). Use when user requests new slash command or workflow automation that justifies token cost.

This skill should be used when you need AI debate with multiple perspectives, analyzing trade-offs, evaluating pros and cons, deep code analysis, or multi-model reasoning. Ideal for subjective decisions, architectural trade-offs, and complex problems requiring synthesis of different viewpoints. Complements opus-subagent for structured multi-step workflows.

Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.

Auto-generate renderers, calculate statistics, and create data-driven visualizations. Use for class breaks, unique values, heatmaps, dot density, relationship maps, and getting histogram/summary statistics.

Systematic vulnerability finding, threat modeling, and attack surface analysis. Use for comprehensive security assessment planning.

Security audits, vulnerability analysis, and security best practices enforcement

Medical PowerPoint presentation generation with clinical data visualization. Creates professional presentations from FHIR resources, lab results, and clinical data. Use when generating patient summaries, care plans, medical reports, or clinical presentations with proper healthcare formatting.

Creates interactive HTML visualizations of project structure, dependencies, and statistics. Use when the user asks to visualize the project, create a project overview, or generate an HTML dashboard of the codebase.

Expert system for identifying deprecated patterns, suggesting refactoring to modern standards (Python 3.12+, ES2024+), checking test coverage, and leveraging AI-powered tools. Proactively applied when users request refactoring, updates, or analysis of legacy codebases.

Analyze and explain the intent behind AI tool calls. Use when you need to understand what an action will do, verify your interpretation is correct, or explain your reasoning to the user.

Analysis of crypto-to-crypto vs crypto-to-fiat (USD) trading on Alpaca for fee optimization and risk considerations

Examines git diffs between commits or branches with intelligent analysis. Provides unified diff format with comprehensive summaries including file statistics, rename detection, and merge commit handling. Outputs formatted diffs to /claudedocs for documentation and review purposes.

Research genealogical connections to Aboriginal peoples in specified regions using web search, historical documents, and database analysis. Process images from folders, discover relationship paths, and track research findings.

Master MongoDB indexing and query optimization. Learn index types, explain plans, performance tuning, and query analysis. Use when optimizing slow queries, analyzing performance, or designing indexes.

Essential Reinvention Loop based on Elon Musk's First Principles Thinking. A 3-step problem-solving framework for the AI era. (1) Deconstruct problems to fundamental facts, (2) Delete unnecessary processes boldly, (3) Reconstruct 10x faster with AI as partner. Use for complex problem analysis, process optimization, and innovative solution design.

Linear algebra operations in NumPy, including matrix multiplication, SVD, system solving, and least squares fitting. Triggers: linalg, matrix multiplication, SVD, eigenvalues, matrix decomposition, lstsq, multi_dot.

Use when working with DuckDB databases, Makefiles, or building/deploying notebooks. Triggers on DuckDB queries, database creation, Makefile editing, make targets (build, data, etl), GitHub Actions workflows, CI/CD, and creating new notebook repositories.

Automatically applies when optimizing database queries. Ensures EXPLAIN analysis, proper indexing, N+1 prevention, query performance monitoring, and efficient SQL patterns.

Standards for generating Model Cards from data analysis notebooks. Aligned with Hugging Face and ISO transparency standards.