「spa」の検索結果

Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.

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Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2024 bioRxiv; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.

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Implement, review, or improve data visualizations using Swift Charts. Use when building bar, line, area, point, pie, donut, or iOS 26 3D charts; when adding chart selection, scrolling, annotations, axes, scales, legends, or foregroundStyle grouping; when plotting functions with BarPlot, LinePlot, AreaPlot, PointPlot, Chart3D, or SurfacePlot; or when creating heat maps, Gantt charts, grouped bars, sparklines, threshold lines, or spatial visualizations.

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Build MATLAB apps from requirements to working code. Asks discovery questions (or skips them when the path is known), recommends UIFigure or UIHTML architecture, identifies layout archetype (Dashboard, Explorer, Tabbed, Wizard, Canvas), produces an implementation plan, and executes the build. For UIFigure apps, optionally serializes as App Designer (.mlapp or plain-text .m + .xml). Use when a user wants to build a MATLAB app, create a GUI, make an interactive tool, build a uifigure app, build a uihtml app, build an App Designer app, build a .mlapp app, build a plain-text App Designer app, or asks which approach to use. Also use when user describes spatial layout needs: dashboard, control panel, sidebar, tabs, wizard, stepper, canvas, workspace.

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Use when moving skills between library workspaces or upgrading from a personal library to a team library. Export from one workspace, import into another.

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Use when regenerating README.md and WORK_AREAS.md in a managed library workspace. Always dry-run first to preview changes.

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Optional design-improvement pass for when you have spare usage to drain. Finds the shallowest modules in the code the spec touches, researches a deeper design, and proposes refactors that shrink interfaces and hide decisions — behavior held constant, tests green before and after. Proposes §I/§V/§T edits, never silent rewrites. Triggers when the user says "deepen this", "improve the design", "this module feels shallow", "pull complexity down", "use spare budget on the codebase", or invokes /ck:deepen. Leans on the codebase-design skill's deep-module vocabulary when present.

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Process images for web development — resize, crop, trim whitespace, convert formats (PNG/WebP/JPG), optimise file size, generate thumbnails, create OG card images. Uses Pillow (Python) — no ImageMagick needed. Trigger with 'resize image', 'convert to webp', 'trim logo', 'optimise images', 'make thumbnail', 'create OG image', 'crop whitespace', 'process image', or 'image too large'.

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Finds secrets, tokens, passwords, and API keys that can leak through generated serialization: Python dataclass repr and asdict, attrs, pydantic model_dump, NamedTuple, JavaScript JSON.stringify and util.inspect, structured logs, tracing spans, and error reports. Use when asked to "check for secret serialization", "credential in repr", "repr=False audit", "secret in spans", "token in logs", or when a change adds a credential field to a dataclass, model, or config object, or adds code that stringifies whole objects or every kwarg into telemetry.

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Expert guidance for the Fabric CLI (`fab`) and the Fabric and Power BI REST APIs: workspaces, items, lakehouses, notebooks, pipelines, semantic models, reports, capacities, OneLake, deployment and admin. Also estimates the capacity units (CU) an operation will consume and its impact on the capacity before running it. Automatically invoke whenever the user mentions Fabric, Power BI Service, a Fabric or Power BI workspace, a capacity or F SKU, OneLake, `fab`, or asks to create, run, refresh, schedule, deploy or delete anything in Fabric, including preview items such as Plan, Ontology, Graph or Copilot.

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Trace relationships between semantic models and downstream reports across Fabric workspaces. Automatically invoke when the user asks to "find downstream reports", "show report lineage", "impact analysis", "what depends on this dataset", "cross-workspace lineage", "which reports are connected", "get model dependencies", or mentions model-to-report dependency tracing.

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When the user wants to define, audit, or apply visual identity (typography, colors, spacing, design tokens, frontend aesthetics). Also use when the user mentions "brand style guide," "visual identity," "design system," "typography," "color palette," "brand guidelines," "AI brand aesthetics," "brand colors," "font choices," "spacing system," "design tokens," "motion," "distinctive design," "frontend aesthetics," "PowerPoint theme," "Google Slides brand," or "slide master colors." For brand story, positioning, and voice, use branding.

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Only to be triggered by explicit super-swarm-spark commands.

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Only to be triggered by explicit /parallel-task-spark commands.

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。

Generate the dispatcher PCF for a third-party `.ppmplugin` (wrap-runtime) control. Runs `pac pcf init` in the pcf/ subfolder, rewrites ControlManifest.Input.xml from ARCHITECTURE §6, and writes index.ts derived from ARCHITECTURE §4 (message contract), §8 (PCF surface), §9 (error UX) — no placeholders. The bridge dispatches the composite key `<name>/<receiver>` to `NativeModules.<nativeModule>.<method>` via the host-injected `window.PowerApps.NativeExtension.sendAsync` global (never `cordova.exec` — not in the PCF sandbox); also emits a `PowerAppsNativeExtension.d.ts` ambient declaration. Responses are peeled with `extractResponse`. Emits structured JSON debug/error logs. Validated by `npm run build`. **Local only** — does not deploy. Needs only `pac` CLI. Run after the native module exists. Uses npm (not pnpm).

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Read the approved PRD.md and generate the native sources for a third-party PAM control (the compiled `.ppmplugin` track) — iOS Obj-C `<Pascal>Module` plus optional system-frameworks podspec, Android Kotlin `<Pascal>Module` with build.gradle, AndroidManifest and ReactPackage, a dev-only private package.json (react + react-native devDeps for the builds), and the committed `./manifest.json` dispatch contract the PCF and build stage both read. No TypeScript INativeExtension layer — the contract is the manifest plus the native modules' dispatch surface. Emits the layout in shared/repo-layout.md and generates substantially complete native code (compiled later by /build-android-binary and /build-ios-binary, not here). Local only — writes files, runs no git and touches no remote or feed. PCF is generated by /generate-pcf-companion; the bundle is built by /generate-ppmplugin.

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Use when generating or editing images with Seedream models on Scenario via MCP: text-to-image, image-to-image editing with reference images, posters or packaging with exact in-image text (non-Latin scripts too), subject-preserving edits, sets of related images in one run, editing a transparent asset without losing its alpha, or splitting an image into transparent PNG layers with Layerize. Keywords: Seedream 5.0 Pro, Lite, Flash, 4.5, Layerize, ByteDance, txt2img, img2img, layer extraction.

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Run upstream brand discovery covering audience research, competitive landscape, category dynamics, problem space, and positioning territory exploration. Use this skill at the very start of a brand or website project when the user needs to understand who they're for, who they compete with, what the audience actually needs, and where the brand could plausibly stand. Triggers on brand discovery, audience research, market research, competitive scan, category research, customer research, who is this for, who are we, positioning research, intake, kickoff. Also triggers when a creative brief is requested but the upstream inputs (audience, competitors, problem space) are not yet clear.

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Apply production-grade design standards when building or reviewing pages, components, or UI. Use this skill whenever the user asks to build a page, design a component, lay out a section, review the UI, fix the layout, or check design quality. Triggers on build a page, create a component, design a section, hero, card, CTA, layout, review the UI, fix the design, design system, design tokens, spacing, typography scale, button standards, mobile design. Also triggers for any production design decision where contrast, accessibility, spacing, or visual hierarchy matters.

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Make sure email actually reaches inboxes. Use this skill when setting up email authentication (SPF, DKIM, DMARC), diagnosing emails landing in spam, planning a domain reputation strategy, monitoring sender reputation, or hardening against email spoofing. Triggers on email deliverability, SPF, DKIM, DMARC, spam folder, sender reputation, mailbox provider, soft bounces, bounce rate, BIMI, MTA-STS, deliverability audit. Also triggers when a marketing or transactional email isn't reaching users.

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Query JASPAR transcription factor binding profile database

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Writes Spark jobs, debugs performance issues, configures cluster settings for distributed data processing, DataFrame transformations, and structured streaming analytics.

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Confluence expert for spaces, knowledge bases, documentation, page layouts, macros, templates, and Jira-Confluence integration. Use for documentation strategy, space architecture, content organization, and collaborative knowledge management.

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Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use when designing data architectures, building pipelines, adding data-quality checks, optimizing ETL/ELT, or troubleshooting pipeline failures.

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。