Skill UI

SymPy is a comprehensive Python library designed for exact symbolic computation. It enables users to perform advanced mathematical operations, including symbolic calculus (derivatives, integrals), algebraic manipulation, solving complex systems of equations, and linear algebra, all without relying on floating-point approximations. It is essential for scientific research, physics modeling, and generating precise mathematical documentation (e.g., LaTeX).

Utilizes Google's TimesFM foundation model for zero-shot time series forecasting. It processes any univariate time series (e.g., sales, sensor data, weather) without requiring custom model training. The skill provides probabilistic forecasts with calibrated prediction intervals (quantiles) and includes a mandatory system checker to ensure sufficient RAM, GPU, and disk resources for safe local inference.

This skill formats and structurally validates pre-existing, clinician-authored treatment plan documentation. It is designed for record-keeping, source traceability, and managing intervention records, but it is strictly forbidden from performing any clinical decision-making, diagnosis, or recommending treatments. It only validates data supplied and verified by licensed professionals.

UMAP (Uniform Manifold Approximation and Projection) is a powerful technique for non-linear dimensionality reduction. This skill enables generating scalable and structure-preserving embeddings for visualization (2D/3D), clustering preprocessing, and feature engineering. It supports supervised and semi-supervised workflows, allowing users to guide the embedding process with labeled data while maintaining local and global data structure integrity.

Access the U.S. Department of the Treasury's free, open REST API to retrieve comprehensive federal financial data. This tool allows users to query key metrics such as national debt, interest rates, exchange rates, daily treasury statements, and auction data. Built with Python and Pandas, it is perfect for financial analysis, economic monitoring, and tracking public sector finances without needing an API key.

Vaex is a high-performance Python library designed for lazy, out-of-core DataFrames. It enables processing and visualization of massive tabular datasets (billions of rows) that exceed available RAM. Users can perform fast statistics, aggregations, and build robust ML pipelines on big data files (CSV, Parquet, HDF5) without relying on distributed clusters, making it ideal for single-machine, large-scale analytics.

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 acts as a comprehensive Music Learning Coach, guiding users through the full spectrum of music education. It covers fundamental music theory (scales, harmony), structured instrument practice routines, advanced ear training techniques, performance skills, and deep dives into music history. Ideal for beginners and advanced learners looking to build a solid, well-rounded understanding and practical ability in music.

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.