Data Science

WHEN: Pandas/NumPy code review, data processing, vectorization, memory optimizationWHAT: Vectorization patterns + Memory efficiency + Data validation + Performance optimization + Best practicesWHEN NOT: Web framework → fastapi/django/flask-reviewer, General Python → python-reviewer

Use when designing interfaces, data visualizations, documentation, or any output where clarity and visual hierarchy matter - applies Swiss design principles of reduction, grid structure, hierarchy, and typography

Expert direct mail marketing strategist for writing compelling copy, designing high-converting mail pieces, and developing measurement strategies. Use when planning direct mail campaigns, writing mailer copy, designing postcards/letters, or measuring campaign effectiveness with incremental lift analysis.

Perform comprehensive exploratory data analysis on research data. Automatically analyze data structure, quality, distributions, and generate insights. Use when the user provides a dataset, asks to "explore data", "analyze this file", or needs to understand their data before formal analysis.

Generates a deep-psychology buyer persona dossier. It strictly follows a 10-step market analysis protocol (Demographics, Failed Solutions, Tone Switching, Genie Simulation, Anti-Goals) to create a strategic foundation for marketing campaigns.

Comprehensive guide to Learning Path patterns for the Plataforma B2B de treinamento técnico corporativo educational platform. This skill covers the conceptual difference between Study Areas (courses) and Proposed Paths (sequences of courses), data modeling, UI/UX patterns, and implementation guidelines.Learn how to structure Learning Paths correctly: a Proposed Path is NOT a container of loose flashcards, but a curated sequence of Study Areas (courses). Each path references existing courses with metadata like order, availability status, and estimated hours.Real-world examples are taken from the Hub MVP implementation (US-044), including caminhoExemploData.js, HubView.jsx, and LearningPathView.jsx. The skill demonstrates the reference pattern used for "Desenvolvedor Backend" path that sequences Bash, Linux, Docker, and DevOps courses.Key topics include data schema design (cursos array with ordem, areaId, disponivel flags), computed properties (getters for statistics), navigation patterns (path -> course

Use when investigating Kubernetes pod failures, crashes, resource issues, or service degradation. Provides systematic investigation methodology for incident triage, root cause analysis, and remediation planning in any Kubernetes environment.

WasteWise Complete Analysis with automated regulatory compliance research and LLM Judge validation. Includes all standard analysis features PLUS automated ordinance research, compliance checklists, and quality-scored evaluation. Use when you need both waste analysis AND regulatory compliance documentation.

Find the most recently downloaded Fidelity and Tastytrade portfolio CSV files from Downloads directory. Use when you need to locate the latest portfolio data files before analysis or processing.

Systematic vulnerability finding, threat modeling, and attack surface analysis for AI/LLM security assessments

Use when reviewing design documents, technical specifications, or architecture docs before implementation planning. Performs exhaustive analysis to ensure the design is specific enough to create a detailed, coherent, and actionable implementation plan without hand-waving or ambiguity.

Systematic Citrix issue diagnosis and resolution. Use when troubleshooting VDA registration failures, session launch problems, application errors, performance issues, or connectivity problems. Provides structured troubleshooting workflows, log analysis techniques, and proven solutions for common Citrix issues.

Ljung-Box portmanteau tests for model diagnostics and residual analysis

Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.

Work with Jupyter notebooks without leaving Claude Code. Execute cells, inspect outputs, validate structure, and convert formats. Activate when working with .ipynb files, user mentions notebooks, Jupyter, or needs to run/debug notebook code.

Loop 3 of the Three-Loop Integrated Development System. CI/CD automation with intelligent failure recovery, root cause analysis, and comprehensive quality validation. Receives implementation from Loop 2, feeds failure patterns back to Loop 1. Achieves 100% test success through automated repair and theater validation. v2.0.0 with explicit agent SOPs.

AI-driven development planning skill that produces TDD-ready specifications using research-backed methodologies (Plan-then-Act, Reflexion, ToT/LATS, ReAct).USE THIS SKILL PROACTIVELY when:- User requests a feature implementation- User reports a bug that needs fixing- User asks for code refactoring- Task involves multiple files or components- Requirements need clarification before coding- You need to create a comprehensive development planThis skill implements the complete planning pipeline: repo analysis → test-first design → structured plan generation → self-critique → TDD handover specification.OUTPUT: Structured TDD Handover Spec (JSON + Markdown) ready for TDD agent consumption.

Use when building features with unknowns or uncertainties - start from well-understood parts (business logic, UX, pure functions), write code as if ideal collaborators exist, use mocks to define APIs through usage, preventing analysis paralysis from trying to understand everything upfront

Expert in retrieving and analyzing GCP cost data from L'Oréal's BTDP infrastructure. **Use this skill whenever the user mentions "GCP cost", "GCP costs", "cloud cost", "cloud spending", "billing", "GCP expenses", or asks about cost breakdown, cost analysis, or cost optimization for Google Cloud Platform.**

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures understanding before attempting solutions