Skill UI

Builds AutoML pipelines by analyzing user requests, generating code for data preparation, model selection, hyperparameter tuning, validation, and metrics, then documenting artifacts for streamlined automated machine learning workflows.

Automates cross validation setup for ML training requests, offering best-practice guidance, production-ready code samples, and validation steps across data preparation, model tuning, and experiment tracking.

Automatically activates when you mention feature engineering helper, offering guidance on data preparation, model training, hyperparameter tuning, and experiment tracking for ML projects.

nanoGPT is a simplified, minimalist implementation designed specifically for educational purposes. It reproduces the core architecture of GPT-2 (124M) using clean, hackable code, allowing users to understand the entire transformer pipeline from scratch. The framework supports full workflows, including data preparation on various datasets (e.g., Shakespeare, OpenWebText), model training, and text generation, making it ideal for students and researchers learning NLP and deep learning principles.

Automates PyTorch training workflows by guiding data preparation, model configuration, hyperparameter tuning, and experiment tracking while following industry best practices for production readiness.

Auto-activating skill that guides tensorflow model trainer workflows, covering data preparation, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

This tool generates comprehensive briefing documents for scheduled meetings. It integrates data from calendar systems, email history (up to 90 days and historical), professional networking sites (LinkedIn), and past interaction records (Granola). It provides context on attendees, relationship history, key discussion points, and critical action items to ensure users are fully prepared for every call, regardless of whether it's a first introduction or a follow-up.

This skill safely randomizes the order of repetitive JSON objects within an array without corrupting the data or breaking the JSON syntax. It acts as a data engineer, rigorously validating the input schema (checking for property consistency and lack of nesting) before performing the shuffle. It ensures data integrity is maintained while reordering records, making it ideal for testing, simulation, or data preparation workflows.

A comprehensive coaching guide for advancing careers in the technology industry, covering roles like Software Engineering, Data Science, and Product Management. It provides actionable strategies for resume optimization (ATS format), technical interview preparation (Algorithms, System Design, Behavioral), salary negotiation, and navigating complex career paths (IC vs. Management). It adapts advice to both global (FAANG) and Chinese tech ecosystems.

A comprehensive checklist to guide businesses through year-end financial closing, tax preparation, and compliance filing. It verifies the synchronization of data from banking, payroll, and expense platforms, ensuring all integrations are active, records are reconciled, and regulatory requirements are met to prevent missed deadlines or audit risks.

A comprehensive guide detailing the process of migrating bookkeeping and accounting services to Fondo. It covers multiple scenarios, including transfers from DIY solutions, other firms (like QuickBooks, Pilot, Bench), and managing changes in accountants. The guide outlines necessary data preparation (GL exports, bank statements, tax records) and the phased onboarding process to ensure seamless financial continuity for growing businesses.

This comprehensive skill provides a structured, actionable checklist for verifying and auditing Notion API integrations before they go live in a production environment. It covers critical areas such as environment variable security, least-privilege capability scoping, robust rate limit handling, data pagination, and comprehensive error management. Use this checklist during go-live preparation, system audits, or major feature deployments to ensure the integration is secure, stable, and fully functional.