Skill UI

This comprehensive tool guides the systematic creation of detailed technical specifications. It orchestrates the entire design lifecycle, including context loading, deep discovery and analysis (tailored for new features, extensions, or simple additions), synthesis using established design principles, and generating a boundary-first, structured design draft. Ensures all requirements are mapped to explicit technical components.

A comprehensive guide for creating professional, publication-quality academic and scientific posters using LaTeX packages (beamerposter, tikzposter, baposter). This skill supports structured layout design, typography, color schemes, and integrates AI-generated scientific schematics, ensuring compliance with strict visual communication best practices for conferences and academic events.

This skill guides the creation of highly auditable, evidence-traceable market research reports. It provides a structured workflow for defining market scope, mapping every claim to primary sources (like official filings and statistics), and conducting rigorous, assumption-driven market sizing (TAM/SAM/SOM) and forecasting scenarios. Essential for professional market analysis and strategic planning.

A comprehensive guide for designing, reviewing, and migrating numerical workflows in MATLAB or GNU Octave. It emphasizes building reproducible projects, adhering to strict safety boundaries, and ensuring code semantics are explicit for robust data processing and analysis.

A comprehensive end-to-end toolkit for analyzing high-density Neuropixels extracellular recordings. It guides the user through the complete electrophysiology workflow, including data loading (SpikeGLX, NWB), robust preprocessing, motion/drift correction, advanced spike sorting (Kilosort4, etc.), quality metric calculation, and AI-assisted unit curation. Ideal for generating publication-ready results from large neural datasets.

A comprehensive tool supporting rigorous, evidence-bounded, and structured peer-review of scientific manuscripts, research protocols, and academic proposals. It guides reviewers through critical assessments of methods, statistics, ethical compliance, reproducibility, and claim-evidence alignment, while strictly enforcing confidentiality and human accountability throughout the review process.

This guide provides comprehensive, version-aware documentation for using PufferLib in reinforcement learning pipelines. It details best practices for adapting Gymnasium/PettingZoo environments, managing different PufferLib versions (3.0.0 vs 4.0 source), and executing safe, reproducible training and evaluation routines. Key focus areas include environment contract validation, sandboxing native builds, and maintaining data provenance.

A comprehensive guide for optimizing query latency in Qdrant. This resource explains advanced performance tuning techniques, focusing on critical resource management like RAM utilization, segment count adjustments, and HNSW parameter tuning. It details strategies for scaling, quantization, and hardware considerations (e.g., local NVMe) to ensure consistently low-latency search performance, and outlines common pitfalls to avoid.

A comprehensive guide for diagnosing production performance issues in Qdrant vector databases. Learn how to systematically check the optimizer status, analyze high memory usage (OOM crashes), and troubleshoot slow query latencies using key metrics and dedicated API endpoints. Essential for maintaining optimal operational health.

Master the art of writing highly competitive research proposals for major funding bodies, including NSF, NIH, DOE, DARPA, and NSTC. This skill provides comprehensive guidance on structuring technical narratives, developing specific aims, justifying budgets, incorporating broader impact statements, and ensuring compliance with diverse agency-specific guidelines and formatting requirements.

A comprehensive Python toolkit built on AnnData for analyzing single-cell RNA-seq data. It supports the complete workflow, including quality control (QC), normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering, marker identification, and cell type annotation. Users benefit from a suite of ready-to-run CLI scripts that guide the entire process from raw data loading to publication-quality visualization.

This skill provides a robust, systematic framework for evaluating the rigor of scientific claims and research studies. It guides users in assessing experimental design validity, detecting various biases (selection, confounding, publication), and evaluating statistical evidence. By applying established grading standards like GRADE and Cochrane Risk of Bias, it is essential for conducting systematic literature reviews, peer reviews, and deep scientific critical analysis.