8,765 件 ・ 人気順
概要と使いどころ
Submit compact Open Targets Platform GraphQL requests for target, disease, drug, variant, study, and search data, including associated-disease datasource heatmap matrices. Use when a user wants concise Open Targets summaries or per-datasource evidence context
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 installs mixpanel_headless, pandas, numpy, matplotlib, seaborn, networkx, anytree, scipy (and pyarrow on Python 3.11+), then verifies Mixpanel credentials. It should be invoked when setting up a new environment for Mixpanel data analysis, when dependencies are missing, or when configuring service account or OAuth credentials for the first time.
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。
Submit compact NCBI Datasets v2 requests for assembly, genome, taxonomy, and related metadata endpoints. Use when a user wants concise NCBI Datasets summaries; save raw JSON or text only on request.
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。
Submit compact ClinicalTrials.gov API v2 requests for study search, metadata, enums, search areas, and field statistics. Use when a user wants concise ClinicalTrials.gov summaries
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。
Submit compact CELLxGENE Discover API requests for public collection and dataset metadata. Use when a user wants concise single-cell collection summaries
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。
Submit compact cBioPortal API requests for studies, molecular profiles, mutations, clinical data, and samples. Use when a user wants concise cBioPortal summaries
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。
Prompt- and template-complete Google Docs creation and editing with explicit-instruction-authoritative structural preservation, including semantic roles, relationships, comparison dimensions, and instructed extensions; full-topology native-copy routing; source-grounded per-tab adaptation for past/example references; style-preserving hyperlink and table edits; canonical smart-chip-first authoring for dates and relevant supported people or Google resources; a file-backed advisory trusted read before existing-document writes; automatic protected-control awareness; direct connector APIs by default; DOCX-first import only when no supplied Google Doc template/reference constrains the output; and checked-in code mode only for exact native dropdown mutation. Use when Codex must create, edit, fill, adapt, redesign, or verify Google Docs without overriding explicit user/template instructions, adding unrequested document scope, or carrying stale reference facts into a new deliverable.
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。
Assess whether structured data, query results, dashboards, or analytical evidence are trustworthy enough to use. Use when the task is to check data quality, reconcile conflicting sources or metric definitions, or decide whether evidence is safe to cite.
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。
Validate whether an analysis is accurate, well-supported, and ready to share or use for a decision. Use when reviewing methodology, calculations, comparisons, visuals, caveats, or conclusions.
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 Data Analytics plugin-level requests and broad analytics work to the right focused workflow. Use when Data Analytics is at-mentioned, or for analytics requests involving data, metrics, dashboards, reports, charts, notebooks, spreadsheets, KPIs, market sizing, or semantic layers.
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。
Publish a validated Data Analytics report or dashboard artifact through Sites. Use automatically for durable artifacts in any positively identified Work Mode when the full Sites lifecycle is callable, or in ChatGPT Desktop outside Work Mode only after an explicit Sites request or acceptance of the optional post-handoff sharing offer.
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, build, revise, or QA quantitative charts and figures. Use when an analytical answer needs visual judgment, whether for an inline answer, report, dashboard, notebook, or artifact.
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, update, inspect, or repair Data Analytics semantic layers. Use when the user asks to save data context or create a semantic layer that future Data Analytics work can inspect and cite.
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 or debugging ANY network request, API call, or data fetching. Covers fetch API, React Query, SWR, error handling, caching, offline support, and Expo Router data loaders (`useLoaderData`).
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。
Design statistically honest and uncertainty-aware visualizations. Use when the user needs help showing distributions, intervals, confidence, missingness, sampling effects, or analytical rigor in charts and dashboards.
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。
Bulk-create Canva designs from tabular data using a brand template with autofill fields, producing one design per row. Use when users say "bulk create designs from this CSV", "generate one design per row", "create a design for each product", "batch generate from a template", or "autofill a template from a spreadsheet". Accepts any tabular data source — uploaded files, pasted tables, JSON, or URLs.
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 implement parallax scrolling and scrollytelling data visualizations. Use when the user asks for parallax scrolling, scrollytelling, scroll-driven timelines, sticky graphics, Scrollama, ScrollTrigger, ScrollTimeline, view timelines, rich-media timelines, moviescrollers, scroll-scrubbed charts, staged narrative reveals, or interactive visual stories where scrolling changes a data visualization or media scene.
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 typed data visualizations in TypeScript. Use when the user wants TypeScript visualization code, typed data models, browser visualization components, UML-like diagram models, interactive graph or architecture diagram contracts, scroll-driven scene contracts, library selection guidance, or a maintainable visualization architecture beyond React- or Next-specific concerns.
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。
Integrate data visualizations into React and Next.js applications. Use when the user needs chart components, UML-like or architecture diagram components, React integration patterns, Next.js client or server boundaries, hydration-safe rendering, lazy loading, framework-aware performance, scroll-driven visual stories, or export guidance.
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 data visualizations with Canvas2D. Use when the visualization needs high mark counts, fast redraws, immediate-mode rendering, custom hit testing, or a hybrid Canvas plus SVG or HTML architecture.
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 custom data visualizations with D3. Use when the user needs SVG or DOM-based charts, rich annotation, domain-native contextual backgrounds, data joins, custom scales or interactions, scroll-driven SVG scene states, or precise control over browser visualization behavior.
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。
Explains what Airtable is and how data is structured — bases, tables, fields, records, views, automations, and interfaces. Use when you need context about the Airtable data model.
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。