Skill UI

Vaex is a high-performance Python library designed for processing and visualizing enormous tabular datasets that exceed available RAM (gigabytes to terabytes). It excels at out-of-core DataFrame operations, lazy evaluation, fast statistical aggregation, and machine learning integration on massive datasets. Use it for analyzing large CSV, Parquet, or HDF5 files efficiently on a single machine, without memory constraints.

Zarr is a robust Python library designed for storing massive N-dimensional arrays. It supports efficient chunking, compression, and parallel I/O, making it ideal for large-scale scientific computing pipelines. Seamlessly integrates with NumPy, Dask, and Xarray, and supports cloud storage backends like S3 and GCS via fsspec, ensuring high performance in cloud-native environments.

This advanced skill optimizes LLM context usage by implementing strict rules for prompt caching, efficient log pruning, AST-based code inspection, and compact data structuring. It minimizes token consumption and maximizes information density, suitable for highly technical and iterative development workflows.

Access a comprehensive network of 78 public databases across scientific, biomedical, environmental, and physical domains. This skill enables deterministic and reproducible data retrieval through documented REST APIs. It manages complex workflows including exhaustive pagination, cross-referencing identifiers across sources (e.g., PubChem, UniProt), and generating full provenance logs, ensuring results are auditable for high-stakes scientific research.

Provides expert consultation on ITIL 4 and the emerging ITIL 5 Digital Product & Service Management (DPSM) framework. This skill guides the integration of AI governance, ESG sustainability principles, and product-centric thinking into modern, high-velocity IT service value streams. Ideal for optimizing IT operations and achieving continuous compliance.

This skill automates the process of transforming high-level product specifications, requirements, and design documents into a detailed, actionable, and executable task plan. It follows a multi-stage workflow, including context loading, parallel research, and two rigorous review gates (Task Plan Review and Task-Graph Sanity Review). The output provides properly sized (1-3 hours), logically sequenced tasks, ensuring every requirement is covered and dependencies are explicitly managed, dramatically improving project planning accuracy.

This expert acts as a Design Systems Lead and Prompt Engineer for creating high-fidelity, consistent UI designs. It transforms vague ideas into structured specifications by enhancing prompts with professional UI/UX terminology, synthesizing design system source files, and managing multi-step generation and editing workflows using the Stitch MCP.

This comprehensive workflow guides the implementation of a core Algolia search feature. It covers advanced concepts such as full-text search, combining multiple filters (e.g., price range, stock status), utilizing faceted navigation for refined filtering, managing pagination, and displaying highlighted search results. This is essential for building robust and highly functional e-commerce search UIs.

This skill connects to the DeepEar Lite platform to fetch real-time, high-frequency financial signals. It retrieves crucial data points including signal titles, sentiment summaries, confidence scores, and detailed reasoning. Use this when you require immediate, actionable insights into volatile market trends, stock performance factors, or comprehensive financial analysis.

This guide provides comprehensive strategies to significantly reduce spending on the Anthropic Claude API. Key techniques include model routing (selecting the most cost-effective model for the task), prompt caching for repeated prompts, using Message Batches for asynchronous tasks, and implementing real-time spend tracking and budget alerts. Essential for managing large-scale, high-cost AI deployments and optimizing cloud LLM expenditures.

A comprehensive guide detailing common pitfalls and anti-patterns encountered when integrating with the Anthropic Claude API. It highlights crucial differences from other models (e.g., OpenAI), such as the mandatory nature of `max_tokens`, correct handling of system prompts, and precise response parsing techniques. Essential for developers debugging subtle API integration issues and ensuring best practices.

This guide details advanced strategies for optimizing Claude API usage to reduce latency and cost. Techniques covered include prompt caching (significant cost reduction), strategic model selection (Haiku, Sonnet, Opus) based on task complexity, implementing streaming for perceived speed, and managing token limits. Use this guide when building high-performance, cost-efficient LLM applications that interact with large language models.