Skill UI

An expert skill for transforming CPU-bound Python code into highly performant GPU-accelerated code using NVIDIA technologies. It specializes in optimizing scientific workflows, including machine learning (ML), physics simulations, geospatial analysis, and large-scale array computations. Utilize tools like CuPy for array operations, Numba CUDA for custom kernels, and Warp for spatial computing and differentiable simulations, achieving dramatic speedups (10x to 1000x). Use this when dealing with NumPy, pandas, SciPy, or complex computational loops that benefit from parallel processing on NVI...

A versatile, hardware-agnostic quantum computing framework designed for training quantum circuits and building hybrid quantum-classical models. It provides automatic differentiation, supporting advanced applications in Quantum Machine Learning, Quantum Chemistry (VQE), and variational algorithms, while ensuring portability across major quantum hardware backends (IBM, Google, etc.).

This skill generates research posters using modern HTML/CSS layouts, allowing for easy integration of AI-generated visuals. It is specifically designed for academic presentations that require a PowerPoint (PPTX) or PDF format. It enforces strict guidelines for visual simplicity, text size, and content density to ensure readability from a distance.

Pymatgen is a comprehensive Python toolkit designed for computational materials science. It enables users to create, analyze, and manipulate complex crystal structures, calculate electronic properties (DOS, band structures), simulate phase transitions, and interact with large material databases like the Materials Project. Ideal for solid-state physics, chemistry, and advanced materials research.

Pysam is a comprehensive Python toolkit for handling genomic data. It provides a Pythonic interface for reading, writing, and manipulating essential genomic formats, including SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences. It is critical for developing robust bioinformatics pipelines, performing coverage analysis, and analyzing genetic variations.

Pyzotero is a powerful Python wrapper for the Zotero Web API v3. It enables developers to programmatically interact with Zotero libraries, allowing for CRUD operations on items, collections, tags, and attachments. Use this skill to build robust research automation workflows, manage bibliographic references, export citations (e.g., BibTeX), or perform bulk data processing within a Zotero environment.

RDKit is a powerful, open-source cheminformatics library providing extensive Python APIs for molecular analysis and structural manipulation. It supports molecular I/O (SMILES, SDF, InChI), calculating physicochemical descriptors (MW, LogP), generating fingerprints, performing substructure searching, and analyzing stereochemistry. This toolkit is essential for advanced research in drug discovery, computational chemistry, and materials science.

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.

This skill generates comprehensive and structured requirements documents for a new specification. It systematically aggregates context from project files, business rules, and technical constraints, ensuring all generated requirements focus on user-observable behavior (WHAT) rather than implementation details (HOW). It follows established standards like EARS format and includes a rigorous review gate to ensure scope clarity and completeness.

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 skill provides an interactive, comprehensive quality review for technical design documents. It systematically validates a feature's readiness for implementation by assessing critical issues, identifying strengths, and providing a clear GO/NO-GO decision with actionable next steps. Ideal for ensuring architectural soundness and catching risks early in the development lifecycle.

This guide provides comprehensive instructions for setting up monitoring and observability for Qdrant deployments. It helps users track critical performance metrics, check system health, and diagnose complex production issues such as memory growth or slow query requests. Use cases include setting up Prometheus, Grafana, and implementing robust health checks.