Skill UI

This guide provides comprehensive, structured error handling patterns for modern applications using TypeScript, Python, and general architectural principles. It covers essential concepts like typed custom errors, Result patterns, API error transformers, and React Error Boundaries. Learn how to implement fail-fast mechanisms, differentiate user-facing messages from developer logs, and ensure system resilience against unexpected failures.

A comprehensive guide for training or fine-tuning advanced sentence-transformer models. This skill assists in selecting the correct architecture—including Bi-encoders (SentenceTransformer), CrossEncoders (for reranking), and SparseEncoders—for various Natural Language Processing (NLP) tasks. Use cases include semantic retrieval, similarity matching, document clustering, and pair-wise classification.

This skill provides expert capabilities for training and fine-tuning transformer language models using the TRL (Transformers Reinforcement Learning) library. It supports state-of-the-art post-training methods, including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and Reward Model training. Use this skill to align and customize foundation models for advanced tasks via CLI commands.

This guide provides comprehensive, structured error handling patterns for modern applications using TypeScript, Python, and general architectural principles. It covers essential concepts like typed custom errors, Result patterns, API error transformers, and React Error Boundaries. Learn how to implement fail-fast mechanisms, differentiate user-facing messages from developer logs, and ensure system resilience against unexpected failures.

This skill calculates model interpretability for algorithmic trading signals using PageRank. It constructs a feature-contribution graph from an LSTM or Transformer signal and executes a single-entry PageRank calculation. The result identifies the top-K features that most significantly drove the prediction, providing a "regulator-grade" audit trail essential for compliance with regulations like the EU AI Act.

A comprehensive tool for training and evaluating advanced neural network models (including LSTM, Transformer, and N-BEATS) on time-series market data. It facilitates the prediction of financial trends, generating results with confidence intervals, and comparing performance across multiple architectures. Ideal for quantitative trading research and financial forecasting.

Predict regulatory features, gene structure, and expression from DNA sequences using hosted transformer DNA language models via REST API or MCP server. Six tasks: promoter, splice, enhancer, chromatin, expression, annotation. No local GPU needed.

Systematically transforms high-level ideas or goals into a detailed, structured, and executable taskbook designed for AI agents. This process involves deep research, validation against existing codebases, adherence to strict formatting rules, and generating a single, copy-pastable block of instructions for immediate agent execution. It distinguishes between execution and exploration tasks, ensuring all steps are verifiable and anti-cheating measures are included.

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.