lucifertrj

This skill should be used when users need help with Git, GitHub workflows, pull requests, branching strategies, repository contributions, and open source collaboration.

FastAPI Python backend for LLM Arena game server. Use when working on API endpoints, LLM provider integration via LiteLLM, game move processing, prompt engineering for AI players, error handling, or backend testing.

React 19 + Vite frontend for LLM Arena game visualization. Use when working on UI components, game board rendering, state management with hooks, API integration, Tailwind CSS v4 styling, Framer Motion animations, or frontend build configuration.

Full-stack LLM Arena application for AI vs AI game battles. Use when working on project-wide configurations, deployment, architecture decisions, or coordinating between frontend and backend. Covers Tic-Tac-Toe and Reversi games with OpenAI, Anthropic, and Google Gemini support.

Audits mobile app codebases for pre-release readiness across iOS (Swift/SwiftUI/UIKit), Android (Kotlin/Jetpack Compose), Flutter (Dart), and React Native. Produces a ship / ship-with-caveats / block verdict backed by findings in six layers — privacy & permissions, accessibility, localization, crash-prone code, store metadata, and release-build hygiene. Use when given a mobile app repo (or parts of one) and asked to review, audit, pre-flight, check if ready to ship, prepare for App Store or Play Store submission, run a release checklist, do an accessibility or privacy compliance review, or diagnose a likely store rejection. Also use when user mentions TestFlight, beta submission, privacy manifest, PrivacyInfo.xcprivacy, Data Safety form, ATT prompt, content rating, age rating, export compliance, Info.plist audit, AndroidManifest audit, or "why will this get rejected".

Assistant for designing, building, and optimizing Power BI semantic models. Helps with data architecture decisions, DAX formula development, relationship configuration, performance tuning, and implementation of analytics best practices. Triggers on questions about model design patterns, metric calculations, schema optimization, data integrity, security frameworks, and documentation standards.

Use this skill when the user asks to load, fine-tune, or run inference with pre-trained transformer models using the Hugging Face transformers library. Triggers include requests like "classify this text with BERT", "fine-tune GPT-2 on my dataset", "build a text summarization pipeline", "tokenize text for transformers", or "run question answering with a pre-trained model". Also use when the user asks about AutoTokenizer, AutoModel, Trainer API, or pipeline(). Do NOT use for CNNs, RNNs, NLTK, spaCy, or tasks that don't involve transformer architecture or the Hugging Face library.

Use this skill when the user asks to clean, preprocess, tokenize, stem, lemmatize, or vectorize raw text data using Python's NLTK library. Triggers include requests like "clean this text dataset", "tokenize my corpus", "build an NLP preprocessing pipeline", "convert text to TF-IDF or Bag of Words", "compare stemming vs lemmatization", or "evaluate my NLP model". Also use when the user asks about stopword removal, punctuation stripping, frequency analysis, or building scikit-learn compatible text pipelines with NLTK. Do NOT use for deep learning NLP (transformers/BERT/spaCy) or for tasks that don't involve text preprocessing.

AI Memory is a feature that allows you to store and retrieve information from your AI assistant. This can be useful for keeping track of important information, such as your schedule, contacts, and other data.

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

AI Memory is a feature that allows you to store and retrieve information from your AI assistant. This can be useful for keeping track of important information, such as your schedule, contacts, and other data.

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

Full-stack LLM Arena application for AI vs AI game battles. Use when working on project-wide configurations, deployment, architecture decisions, or coordinating between frontend and backend. Covers Tic-Tac-Toe and Reversi games with OpenAI, Anthropic, and Google Gemini support.

FastAPI Python backend for LLM Arena game server. Use when working on API endpoints, LLM provider integration via LiteLLM, game move processing, prompt engineering for AI players, error handling, or backend testing.

React 19 + Vite frontend for LLM Arena game visualization. Use when working on UI components, game board rendering, state management with hooks, API integration, Tailwind CSS v4 styling, Framer Motion animations, or frontend build configuration.