537 件 ・ 関連度順
概要と使いどころ
Interactive viewer for microscopy. Displays 2D/3D/4D arrays as Image, Labels, Points, Shapes, Tracks layers; supports annotation, plugin analysis, headless screenshots. Core visualization for Python bioimage workflows. Use ImageJ/FIJI for macro processing; napari for Python-native interactive visualization and DL segmentation review.
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 whole-slide images (WSIs): load slides, extract tiles, stain normalization, nuclear segmentation, feature extraction, and ML training. Supports H&E and multiplex. For end-to-end pipelines from raw WSIs to quantitative outputs.
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 best practices for robot perception systems covering cameras, LiDARs, depth sensors, IMUs, and multi-sensor setups. Use this skill when working with RGB image processing, depth maps, point clouds, sensor calibration (intrinsic, extrinsic, hand-eye), object detection, semantic segmentation, 3D reconstruction, visual servoing, or perception pipeline optimization. Trigger whenever the user mentions OpenCV, Open3D, PCL, RealSense, ZED, OAK-D, camera calibration, AprilTags, ArUco markers, stereo vision, RGBD, point cloud filtering, ICP registration, coordinate transforms, camera intrinsics, distortion correction, image undistortion, sensor streaming, frame synchronization, or any computer vision task in a robotics context. Also covers multi-camera rigs, time synchronization across sensors, perception latency budgets, and production deployment of perception pipelines.
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。
Provides AWS CloudFormation patterns for VPC foundations, including subnets, route tables, internet and NAT gateways, endpoints, and reusable outputs. Use when creating a new network baseline, segmenting public and private workloads, or preparing CloudFormation networking stacks for application deployments.
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 structured podcast episodes. Segment timing, debate points, hot takes, listener questions, ad break placement.
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 and roll out a price increase or packaging change without triggering churn. Segments the base by price sensitivity, models revenue and churn scenarios, decides grandfathering and notice strategy, and produces the full comms kit (customer email, in-app notice, sales talk track, support FAQ, objection handling). Use when raising prices, repackaging tiers, or moving customers to new plans.
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 need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.
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 Detectron2 for object detection, segmentation, configuration, datasets, training, inference, export, and extension 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。
Guide DeepMedic 0.8.4 workflows for multi-modal 3D NIFTI medical-image segmentation, including data preparation, model configuration, training, checkpoint continuation, inference, and CPU/CUDA troubleshooting.
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 the DeepGCNs PyTorch operating guide for graph convolution layers, dynamic and dilated KNN blocks, point-cloud classification or segmentation, PPI, OGB/DeeperGCN, and reversible memory-efficient GNN 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。
Route BiRefNet image segmentation, matting, inference, configuration, training, evaluation, and model-export 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。
Guides Attention-Gated Networks PyTorch medical imaging workflows for ultrasound classification, attention-gated U-Net segmentation, data layout, CUDA setup, and visualization helpers.
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 ANTsPy/antspyx for medical image IO, ANTsImage metadata, image operations, registration, segmentation, visualization, and learning helper 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。
Use when implementing verify codes with segmented input.
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 and optimize email marketing campaigns including sequences, newsletters, drip campaigns. Design subject line strategies, develop segmentation approaches, implement A/B testing, and establish analytics tracking for email 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。
Enrich CRM records with firmographic and contact data, filling gaps in company and person profiles to improve segmentation, routing, and outreach quality. Use when the user requests crm data enrichment or provides relevant inputs for this workflow.
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。
Business model classification, business model analysis, structural analysis of how a company makes money, product offering decomposition, distribution channel analysis, customer segment analysis, revenue model identification, market sizing TAM SAM SOM, competitive positioning, business unit performance, management team & leadership analysis, what does the company sell, how does the company go to market, business model type platform service product, channel mix direct vs indirect, revenue concentration risk, CEO CFO executive backgrounds and changes
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。
Think and work like an expert Connectomics Scientist. Use when a task calls for Connectomics Scientist judgment. Reasons from vEM acquisition and petascale alignment through FFN/RoboEM segmentation, FlyWire/neuPrint/MICrONS/H01 graphs, and synapse-level QC while treating split/merge errors, alignment tears, false synapses, and release-version drift as first-class failure modes.
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。
Think and work like an expert Anatomist. Use when a task calls for Anatomist judgment. Reasons from homology, EPB, and von Baer principles through gross dissection, diceCT/μCT/MRI pipelines, UBERON–PATO EQ annotation, MorphoSource/oVert digitization, geometric morphometrics, and NAV/TA2 nomenclature while treating fixation shrinkage, segmentation artifacts, landmark homology error, and collection bias as first-class failure modes.
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。
Map any industry's competitive landscape using company databases, academic research, news coverage, LinkedIn activity, and job market signals. Produces industry overviews with key players, market segments, growth indicators, and emerging disruptors.
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。
Production guidance for assessing, exporting, packaging, integrating, capturing, validating, and handing off Lottie vector animations. Use for Bodymovin/Lottie JSON, dotLottie archives, renderer and player compatibility, fonts and glyphs, image assets, markers and segments, deterministic video capture, responsive sizing, optimization, accessibility, and cross-platform QA; not for general After Effects animation craft or live-action/video delivery.
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。
Produce audio-first podcast episodes with generative tools — design the show and episode format (interview, narrative, news brief, two-host conversational), write scripts for the ear, decide between fully-synthetic and hybrid (recorded human + synthetic) production and meet the disclosure duty each triggers, cast and direct multi-voice TTS for consistency and chemistry, build episode structure (cold open, intro/outro, segments, ad slots), assemble and edit, clear music and SFX rights, hit podcast loudness and delivery standards, ship metadata/chapters/transcripts through RSS, and pass platform AI-content policy and QA before publishing. Use this skill whenever the deliverable is a podcast episode or a podcast show bible, or when an agent must make production, rights, disclosure, or delivery decisions for spoken-word audio. Not for music tracks, single-voice notification prompts, or video where picture leads (route audio-for-video to a video skill).
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。
Load and preprocess imaging mass cytometry (IMC) and MIBI data. Covers MCD/TIFF handling, hot pixel removal, and image normalization. Use when starting IMC analysis from raw MCD files or preparing images for segmentation.
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。
Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.
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。