「segment」の検索結果

Plan and execute integrative analysis combining multiple ENCODE experiments for cross-dataset or multi-omic workflows. Use when the user wants to combine experiments, perform cross-dataset comparison, multi-omic integration, peak overlap analysis, differential binding, signal correlation, chromatin state segmentation, enhancer-gene linkage, or any analysis that requires merging or comparing data from two or more ENCODE experiments. Covers same-assay cross-sample, multi-omic same-sample, cross-organism, and perturbation integration designs. Guides compatibility checks, batch effect detection, normalization, integration strategy selection, and provenance documentation.

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Discover and characterize regulatory elements (enhancers, promoters, silencers, insulators, super-enhancers) using ENCODE data and the cCRE catalog. Use when the user wants to find candidate regulatory elements, identify active enhancers in a tissue, map promoter states, classify chromatin states with ChromHMM, identify super-enhancers with ROSE, understand the functional validation hierarchy (CRISPR > MPRA > reporter), or characterize non-coding genomic regions. Covers the full workflow from ENCODE cCRE lookup through chromatin state segmentation to functional validation and enhancer-gene linkage. Use this skill for ANY regulatory element discovery, classification, or characterization task.

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Build comprehensive epigenomic profiles for tissues or cell types using ENCODE data. Use when the user wants to characterize chromatin states, assemble histone modification panels, create epigenomic landscapes, run ChromHMM segmentation, identify super-enhancers or bivalent domains, profile regulatory elements across a biosample, or understand epigenetic regulation in a specific biological context. Covers histone marks, chromatin accessibility, TF binding, transcription, DNA methylation, and 3D genome structure.

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

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

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Read and manage the user's Klaviyo email and SMS marketing campaigns, newsletters, flows, subscriber lists, and audience segments. Use to compare campaign performance and revenue, find recent customers, plan audience targeting, and manage marketing content and subscriptions through Klaviyo's official MCP server.

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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.

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Update user personas from new feedback, pilot data, or research. Use this skill when: - New pilot data or feedback reveals changed user behaviors or pain points - Annual persona review is due (1-2x per year) - New user segments are discovered that need persona documentation - Existing personas need validation against recent data

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Build a detailed buyer persona or customer profile. Use when the user says "write a persona", "build a buyer persona", "customer persona for [product]", "who is our target customer", "ICP document", "ideal customer profile", "define our buyer", "who are we building this for", "customer profile", "audience profile", or wants to create a documented, research-backed profile of who buys the product and why. For sourcing the real research behind a persona, see customer-research. For segment and firmographic definition, see icp-research. For writing it into the knowledge base, see brand-context.

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Build or refine the Ideal Customer Profile (ICP) and personas using the Jobs-to-be-Done framework (Clayton Christensen / Bob Moesta). Use when the user asks for ICP research, persona development, "who are our customers", buyer personas, audience research, "build our ICP", target customer definition, or wants to update persona docs. JTBD interview questions, three job dimensions (functional/emotional/social), and the Four Forces of Progress are wired into every persona. Writes to knowledge/icp/personas.md so all other skills get smarter. For the individual buyer persona rather than the segment, see customer-persona. For sourcing real customer language, see customer-research.

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

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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.

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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.

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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.

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

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

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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.

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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.

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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).

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Visualize copy number profiles, segments, allele-specific tracks, and cohort patterns from CNVkit, GATK, ASCAT, FACETS, Sequenza, and other callers. Covers genome-wide and per-chromosome log2 scatter plots, B-allele-frequency/minor-allele-fraction tracks, ideograms, cohort heatmaps, circos views, and caller-native plots. Use when creating publication CNV figures, choosing which plot answers a given question, diagnosing a wrong diploid baseline visually, displaying loss of heterozygosity, or deciding what depth-only plots cannot reveal.

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Detect introgression and admixture between species or populations using Dsuite (Malinsky 2021 fast D-statistics), Patterson's D / ABBA-BABA test (Green 2010; Durand 2011), f4-ratio and f-branch statistic (Malinsky 2018), TreeMix (Pickrell & Pritchard 2012), HyDe (Blischak 2018), QuIBL (Edelman 2019), sprime (Browning 2018), Twisst (Martin 2017), PhyloNet (Than 2008) for explicit phylogenetic networks, and qpAdm / qpGraph (Patterson 2012). Distinguish introgression from incomplete lineage sorting (ILS), ancestral structure, ghost-lineage admixture, and rate variation. Use when testing inter-species gene flow, dating admixture events, identifying introgressed segments, building phylogenetic networks for reticulate evolution, or applying the ABBAclustering (Koppetsch-Malinsky-Matschiner 2024) framework for divergent-species gene flow.

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Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.

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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.

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Detect, date, and contextualize whole-genome duplication (WGD / paleopolyploidy) events using wgd v2 (Chen et al 2024), KsRates (Sensalari 2022 substitution-rate-corrected Ks dating), DupGen_finder (Qiao 2019), MAPS (Li 2018 phylogenomic), POInT (Conant 2008 ordered-block), SLEDGe (2024 ML-based), Whale.jl (Bayesian DL+WGD), and synteny-anchored paranome construction. Use when identifying ancient polyploidy from Ks distributions and synteny block analysis, positioning WGD events relative to speciation, distinguishing tandem from segmental from WGD duplications, dating the 2R/3R vertebrate / fish / salmonid WGDs, building paranome and Ks-age mixture models, applying KsRates substitution-rate correction across lineages, or testing alternative biased-fractionation / dosage-balance models post-WGD.

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