「spatial」の検索結果

Assess whether furniture fits in a measured Pascal room or layout. Use this skill for sofa, table, bed, cabinet, appliance, staging, placement, collision, clearance, or rotated-footprint questions. Produce a tool-backed spatial report that distinguishes footprint fit from unsupported height, door-swing, assembly, and delivery-route claims, and return insufficient evidence when dimensions or scale are missing.

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。

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.

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。

Create, inspect, and update Obsidian JSON Canvas boards with text, file, link, group, and edge nodes. Use for canvas status, canvas lists, visual maps, zones, spatial layouts, adding vault notes or media to a .canvas file, and requests such as create canvas, add to canvas, or put this on the canvas.

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。

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

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。

Comprehensive OSINT methodology skill for offensive security, red team intelligence gathering, and bug bounty reconnaissance. Covers domain recon, email harvesting, social media profiling, GitHub/code leaks, Shodan/Censys enumeration, breach data lookup, employee profiling, infrastructure mapping, cryptocurrency tracing, geospatial intelligence, and AI-assisted analysis workflows. Use when performing reconnaissance against a target domain or organization, investigating a person or entity, tracing cryptocurrency flows, geolocating images or events, or building an attack-surface map.

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。

Render WebGL-accelerated data visualizations with Three.js, raw WebGL, deck.gl, luma.gl, PixiJS, Sigma.js, Plotly WebGL traces, ECharts GL, CesiumJS, Babylon.js, or related GPU libraries. Use when the visualization needs true spatial structure, dense 2D or 3D GPU rendering, particle or flow animation, volumetric views, or interactive exploration that adds real analytical value.

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。

Route web data visualization work. Use when the user needs chart choice, visual critique, dashboards, maps or geospatial views, Gantt timelines, UML/software diagrams, scrollytelling, reports or exports, testing, accessibility, browser implementation, or concept-first visual design.

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。

Build rich, scroll-controlled real-time Three.js experiences as one persistent 3D world whose camera, lighting, atmosphere, materials, objects, DOM story, and interactions evolve across authored chapters. Use for 3D scrollytelling, scroll-driven WebGL worlds, camera journeys, interactive portfolios, product stories, exhibitions, explainers, game or film microsites, spatial narratives, and multi-scene websites where native scroll should travel through one continuous place. Not limited to landing pages.

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。

Design or implement responsive audio feedback for a Three.js or web game. Use for action sounds, combat layers, music states, spatial audio, mix priorities, mute controls, accessibility, mobile audio unlock, and audio performance.

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。

Frontend design skill fused from Impeccable + custom extensions. Covers design philosophy, anti-AI-slop patterns, typography, color (OKLCH), spatial design, motion, interaction, responsive, UX writing, state management, engineering, and 4 style variants. Includes 20 command skills for audit/critique/polish/animate/etc.

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。

Probabilistic single-cell RNA-seq with scvi-tools — scVI for a batch-corrected latent space, scANVI for semi-supervised label transfer, and Bayesian differential expression. Reach for this skill to integrate scRNA-seq batches, embed cells for clustering, transfer annotations from a reference onto a query, or score differentially expressed genes per cluster. For spatial deconvolution / mapping use the cell2location, DestVI, or Tangram methods instead.

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。

Redistricting analysis in R using the redistverse ecosystem. Use whenever the user is working with redist, redistmetrics, ggredist, geomander, adj, alarmdata, PL94171, censable, easycensus, tinytiger, baf, rict, or redistio. Covers the complete pipeline: Census and spatial data loading, adjacency graph construction, SMC/MCMC simulation, constraints (population balance, county splits, VRA compliance), convergence diagnostics, plan metrics (compactness, partisan fairness, splits), visualization, summary tables, and interactive plan drawing. Invoke whenever the user mentions redistricting, gerrymandering, district plans, simulation ensembles, or any redistverse package by name.

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。

Run Apache Tika as a Docker container when you need guaranteed OCR (scanned PDFs, images) or geospatial raster support with zero local install — `apache/tika:<version>-full` bundles Tesseract, GDAL, ImageMagick, and fonts. Also covers the minimal image, port/volume/memory conventions, the path-identity mount gotcha, and how to confirm OCR actually ran rather than silently returning no text. Powered by Apache Tika. Use when a local `tika-app`/`tika-server` doesn't have Tesseract installed, or you want a disposable, self-contained parsing environment. Companion to the `file-to-markdown` skill, which covers the parsing calls themselves once a server is up.

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。

Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.

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。

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

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。

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

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。

Create Obsidian Canvas files from text content, supporting both MindMap and freeform layouts. Use this skill when users want to visualize content as an interactive canvas, create mind maps, or organize information spatially in Obsidian format.

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。

Official NVIDIA-authored guidance for PhysicsNeMo ShardTensor domain parallelism — integrate domain parallelism into training/inference scripts (new or existing) with DDP or FSDP2, write and register shard patches to enable new layers/ops, and bootstrap multi-GPU correctness tests. Use when working with ShardTensor, scatter_tensor, domain parallelism, sequence/spatial sharding, ring attention, DeviceMesh + DDP/FSDP2 hybrid parallelism, or physicsnemo.domain_parallel. Do NOT use for generic PyTorch DDP/FSDP setup without domain parallelism, picking a PhysicsNeMo model or example (use physicsnemo-discover), or non-distributed training questions.

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。

Use when you want to retrieve semi-quantitative protein expression and spatial localisation data from the Human Protein Atlas (HPA).

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。

Futuristic sci-fi aesthetic with dark themes, vibrant neon accents, and immersive spatial elements.

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。

Spatial depth design with isometric views, vanishing points, and layered elements that guide attention through 3D-like realism.

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。

Apply the Law of Proximity — spatial closeness groups elements more strongly than any other cue. Use when spacing alone must carry grouping. For grouping via containers use `law-of-common-region`; via shared appearance use `law-of-similarity`.

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。

Author and edit a Grida `.canvas` board — a `.canvas.json` manifest plus document files (references, generated images, notes) placed on an infinite canvas. Use when working on a `.canvas` bundle or arranging visuals/design work spatially. For a linear deck/presentation, use the `slides` skill instead.

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。

Migrate a whole ArcGIS Hub site into a PortalJS Arc portal end-to-end. Harvests the Hub /data.json (DCAT-US) inventory, exports every FeatureService layer through the ArcGIS REST query API with resultOffset paging, converts each to the serverless dual tier (PMTiles render + GeoParquet query) with tabular items to Parquet, pushes everything to Cloudflare R2 via Git LFS, appends dual-tier datasets.json entries, and writes a source-vs-derived parity report. Use to move a City or sector ArcGIS Hub open-data portal onto PortalJS with no server-side compute.

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。