「reduce」の検索結果

Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

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Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.

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Use this skill when a driver has vision in only one eye or significantly reduced vision in one eye. Covers § 391.41 vision standards, monocular driver variance, and depth-perception assessment.

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Use this skill when the user asks about DataQ — FMCSA's data review system at dataqs.fmcsa.dot.gov — for disputing inspection violations, crash records, or other entries that appear in a carrier's CSA / SMS score. Covers Request for Data Review (RDR) process, success rates, common dispute grounds, what evidence to attach, timeline expectations, and how successful disputes reduce BSI (BASIC Severity Indicator) scores. Cite 49 CFR 392.7 and the FMCSA DataQs User Guide.

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Safe removal of a duplicate component in favor of the one being kept. Use when asked to remove a component in favor of another, eliminate duplicates, reduce two versions of something to one, or clean up redundant UI components.

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Strip designs to their essence by removing unnecessary complexity. Great design is simple, powerful, and clean. Use when the user asks to simplify, declutter, reduce noise, remove elements, or make a UI cleaner and more focused.

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Generates Mediator pattern for PHP 8.5. Creates coordination layer for complex component interactions with event dispatching, request/response handling, and colleague classes. Reduces coupling between interacting objects. Includes unit tests.

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Configure output requests - field outputs, history outputs. Use when user asks what results to save, output variables, reduce output file size, or history output.

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Automated cleanup of unused AWS resources to reduce costs

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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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Compresses images to WebP (default) or PNG with automatic tool selection. Use when user asks to "compress image", "optimize image", "convert to webp", or reduce image file size.

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Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

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Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services

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Implement or refine UI motion, gestures, and transitions. Use when the user asks for animation, hover or press motion, drag or swipe behavior, transition timing, or reduced-motion alternatives. Mentioning a drawer, toast, or hover state alone does not request animation.

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Reduce visual intensity, saturation, and noise while preserving hierarchy and character. Use when the structure is basically sound but the design feels too loud, bold, garish, aggressive, or overstimulating—not when the real problem is structural clutter, too many features, or not enough personality.

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You are a workflow automation expert specializing in creating efficient CI/CD pipelines, GitHub Actions workflows, and automated development processes. Design and implement automation that reduces manual work, improves consistency, and accelerates delivery while maintaining quality and security.

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Build a minimal context pack and recommend the correct specialist path for token-efficient execution. Use when the task is non-trivial, spans multiple domains, or should reduce token usage before implementation.

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TLS 1.3 (RFC 8446) is the latest version of the Transport Layer Security protocol, providing significant improvements over TLS 1.2 in both security and performance. It reduces handshake latency to 1-R

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Designs and implements VLAN-based network segmentation on managed switches to isolate network zones, enforce access control between segments, and reduce the attack surface by limiting lateral movement paths in enterprise network environments.

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Automates the enrichment of raw indicators of compromise with multi-source threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks to reduce analyst triage time and standardize enrichment outputs. Use when building automated enrichment workflows integrated with SIEM alerts, email submission pipelines, or bulk IOC processing from threat feeds. Activates for requests involving SOAR enrichment, Cortex XSOAR, Splunk SOAR, TheHive, Python enrichment pipelines, or automated IOC processing.

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Optimizes Dockerfiles for smaller images, faster builds, better caching, and security hardening using multi-stage builds and best practices. Use when users request "optimize Dockerfile", "reduce Docker image size", "Docker best practices", or "containerize application".

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Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.

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Provides reusable interaction patterns and motion presets that make UI feel polished. Includes hover effects, transitions, entrance animations, gesture feedback, and reduced-motion support. Use when adding "animations", "transitions", "micro-interactions", or "motion design".

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Reduces LLM costs and improves response times through caching, model selection, batching, and prompt optimization. Provides cost breakdowns, latency hotspots, and configuration recommendations. Use for "cost reduction", "performance optimization", "latency improvement", or "efficiency".

日本語の概要は準備中です。原文の説明を表示しています。