Skill UI

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.

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.

Prepare journal papers, conference submissions, posters, and grant documents with venue-specific formatting guidance, official template checks, and LaTeX scaffolds for drafting and compliance review.

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.

A comprehensive guide to diagnosing and fixing performance bottlenecks when using Qdrant. Covers optimizing slow uploads, resolving stuck optimizers, and tuning HNSW parameters, including best practices for sharding, bulk loading, and multi-tenant indexing to ensure efficient vector search performance.

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.

A comprehensive guide for developers to optimize performance when integrating Adobe APIs (Firefly, PDF, Photoshop). Techniques covered include implementing token caching, parallelizing asynchronous job submissions, leveraging response caching (LRU), and optimizing connection handling to minimize latency and maximize throughput.

This comprehensive guide details advanced strategies for optimizing performance when interacting with blockchain data using the Alchemy SDK. Techniques covered include implementing TTL-based caching for data freshness, achieving parallel multi-chain fetches, utilizing batch API calls (e.g., NFT metadata) to reduce Compute Unit (CU) costs, and adopting WebSockets for real-time event subscriptions. Essential for scaling robust, low-latency dApps.

A comprehensive guide to implementing secure and compliant data handling within Algolia. This skill demonstrates how to transform source records, strip Personally Identifiable Information (PII), implement attribute filtering, and enforce data access controls. It provides robust workflows for GDPR and CCPA compliance, including data deletion (Right to Erasure) and Data Subject Access Requests (DSAR).

This utility collects comprehensive diagnostic evidence for Algolia support tickets or internal troubleshooting. It gathers index statistics, API key access control lists (ACLs), recent query logs, and network diagnostic information using API endpoints and shell scripting. Use it when diagnosing search performance or unexpected service behavior.