「segmentation」の検索結果

Design, implement, and audit firewall policy — rule hygiene, segmentation, change control, and continuous compliance.

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。

Copy-number variant analysis — detection from sequencing/arrays, segmentation, and clinical interpretation.

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。

Analyze nonprofit fundraising software for RFM donor segmentation, campaign performance modeling with A/B testing, recurring giving retention and failed payment recovery, major gift prospect scoring using capacity-affinity-propensity frameworks, event ROI calculation.

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 working on SAM 3 video segmentation, vaila_sam.py, checkpoint handling, or GPU memory issues.

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。

Configure Communications Cloud for the first time: org setup sequence, permission sets, Enterprise Product Catalog (EPC) service catalog, TM Forum-aligned order decomposition setup, Account record-type segmentation, contract lifecycle activation. NOT for designing the commercial-to-technical order process itself - use admin/industries-process-design. NOT for calling TM Forum Open APIs (TMF622, TMF679) - use omnistudio/industries-api-extensions.

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 designing nonprofit fundraising lifecycle stages, donor pipeline workflows, cultivation-to-stewardship cycles, or Salesforce Opportunity sales process configuration for NPSP or Nonprofit Cloud. Trigger keywords: fundraising lifecycle, donor pipeline, cultivation stage, solicitation workflow, major gift process, moves management, NPSP Opportunity stages, stewardship cycle, NPC fundraising stages. NOT for lapsed donor re-engagement or portfolio segmentation — use admin/donor-lifecycle-requirements. NOT for Engagement Plan setup — use admin/npsp-engagement-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。

Requirements mapping for the full donor lifecycle in NPSP or Nonprofit Cloud: acquisition stage design, moves management, upgrade paths, lapsed donor re-engagement, and portfolio segmentation. NOT for mapping the Opportunity sales processes and stage picklists — use admin/fundraising-process-mapping. NOT for stewardship task automation — use admin/npsp-engagement-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。

CRM strategist AI — RFM segmentation, lifecycle, churn prediction, win-back สำหรับธุรกิจไทย

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。

Email marketing AI — sequence, automation, deliverability, segmentation สำหรับธุรกิจไทย

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。

AI-powered conversion rate optimization: landing page audits, CRO scoring, survey segmentation, and lead magnet generation. Use when asked to audit a landing page, run a CRO analysis, segment survey responses, or generate lead magnets. For content quality scoring see content-ops.

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。

Automate advertising campaigns with AI. Create ads, buy media, manage ad budgets, discover ad inventory, run display ads, video ads, CTV campaigns, and optimize ad performance. Perfect for marketing automation, programmatic advertising, media buying, ad management, campaign optimization, creative management, and performance tracking. Launch Facebook ads, Google ads, display advertising, video marketing, and multi-channel campaigns using natural language. Supports ad targeting, audience segmentation, ROI tracking, and automated bidding.

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。

Plan, prioritize, and design rigorous A/B tests using the Test Velocity Method. Use when a user wants to test a landing page, CTA, email, signup flow, pricing page, or any digital experience — this skill produces a complete, dev-ready test plan with hypothesis, sample size, duration, segmentation strategy, and guardrail metrics.

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。

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

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。

Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

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 for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

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。

SAM: zero-shot image segmentation via points, boxes, masks.

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。

Modelo de fundação para segmentação de imagens com transferência zero-shot. Use quando precisar segmentar qualquer objeto em imagens usando pontos, caixas ou máscaras como prompts, ou gerar automaticamente todas as máscaras de objetos em uma imagem.

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。

Identifies missing information required to complete qualification, segmentation, or outreach preparation. Use when the user provides records and a goal and wants prioritized research gaps with sources.

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。

Divides records into geographic or account-ownership territories. Use when the user provides records with location or account data and wants consistent territory assignments with unresolved locations flagged.

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。

Divides a lead list into useful commercial segments. Use when the user provides a combined lead list and wants segments with criteria, sizes, recommended treatment, and priority.

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。

Groups companies by industry, business model, size, maturity, market, or strategic fit. Use when the user provides company records and wants consistent segment assignments with definitions and confidence.

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。

给定 PMID/PMCID 取结构化全文（NLM BioC XML/JSON）。封装 BioC PubMed（标题+摘要）和 BioC PMC OA（开放获取全文）端点，输出按章节切分的段落、表格上下文、RAG 友好 chunk，适合大批量入向量库。Fetches structured fulltext from NLM BioC API for PMID/PMCID, outputs section-segmented paragraphs and RAG-ready chunks. 触发词：全文抽取 / BioC / RAG 入库 / 文献全文 / PMC 开放获取 / structured fulltext / 段落抽取 / 表格抽取 / chunking / fulltext to vector store / paragraph extraction.

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。

给定 PMID / PMCID 取结构化全文（BioC XML/JSON）。封装 NLM BioC PubMed API（标题+摘要）和 BioC PMC OA API（开放获取全文），输出按章节切分的段落、表格上下文、RAG 友好 chunk。适合大批量入向量库。触发词：全文抽取 / BioC / RAG 入库 / 文献全文 / PMC 开放获取 / structured fulltext / 段落抽取 / 表格抽取 / fulltext to vector store。

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。