Skill UI

Alex converts high-level requirements into a precise, dependency-aware, and actionable implementation checklist. It maps critical paths, breaks down features into atomic micro-tasks, and establishes clear Definitions of Done (DoD) across various layers. Ideal for bridging the gap between conceptual goals and step-by-step engineering execution.

This skill generates a comprehensive, role-assigned checklist for product launches, feature releases, or major updates. It guides cross-functional teams through pre-launch readiness (Engineering, Marketing, Support), critical Go/No-Go decision points, launch day execution, and post-launch monitoring, ensuring a structured and complete deployment process.

A specialized presentation deck template designed for internal engineering talks, technical conferences, and deep-dive knowledge sharing. It features a professional GitHub-dark theme, JetBrains Mono coding font, and a structured layout including Cover, Agenda, main content slides (with code blocks), demo slides (terminal screenshots), and Q&A sections.

This comprehensive guide details the methodology for reverse engineering macOS and Mach-O binaries. It covers analyzing application bundles, deciphering Objective-C/Swift symbols, examining code signing and hardened runtime features, and performing both static (using tools like Ghidra, IDA) and dynamic analysis (using lldb, Frida) to aid in malware analysis and platform security research.

This gate review forces a transition from vague product direction to a concrete, buildable technical specification. It mandates the creation of architectural diagrams, data flow maps, state machines, and failure mode inventories before a single line of implementation code is written. Essential for complex features with async components or external dependencies.

A comprehensive companion tool for the foundational Deep Learning textbook. It indexes 20 chapters and provides a crucial delta layer, updating core concepts from 2016 to 2026 (e.g., Transformers, Diffusion Models). The tool features four deterministic utilities: a reading-path planner, a training-failure diagnostic, a capacity planner, and a parameter/FLOP calculator. Ideal for students and practitioners who need to study the theory, understand its modern relevance, and translate academic knowledge into actionable engineering decisions.

This sophisticated sub-agent takes comprehensive Software Design Documents (SDD), proposals, and specifications to generate a detailed, actionable task breakdown in the format of a tasks.md file. It structures the work into distinct phases (e.g., Foundation, Core Implementation, Testing, Cleanup), providing concrete, verifiable, and small units of work suitable for immediate engineering execution and managing complex feature delivery chains.

This expert analyzes and refactors existing codebases to enhance quality, maintainability, and performance. It specializes in applying clean code principles, SOLID design patterns, and modern software engineering best practices. Use it to clean up technical debt, simplify complex modules, or prepare a codebase for new feature development.

Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation. This skill provides a structured workflow from exploratory data analysis and hypothesis formation to feature implementation with integrated analytics, A/B testing infrastructure, and post-launch analysis.

Automate binary reverse engineering with IDA Pro using MCP. Includes decompilation, disassembly, data-flow tracking, and cross-references. Features PowerShell scripts for server management and file handling to bypass schema validation and permission issues. Supports PE/ELF/Mach-O targets.

Design and implement production-ready ML pipelines using multi-agent MLOps orchestration, covering data engineering, feature engineering, model training, experiment tracking, deployment, and serving.

This skill enables building production-ready machine learning systems using modern frameworks like PyTorch and TensorFlow. It covers model serving, feature engineering, A/B testing, monitoring, and MLOps practices. Focuses on scalable, reliable, and efficient ML infrastructure for delivering business value in production environments.