188 个 · 按相关度排序
Build data-driven compensation structures that attract talent without overpaying. Covers base salary bands, equity/bonus frameworks, geographic differentials, and total rewards packaging.
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TanStack Query (React Query) best practices for data fetching, caching, mutations, and server state management. Activate when building data-driven React applications with server state.
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Scientific research engine for hypothesis testing, literature gap analysis, experimental validation, and data-driven discovery. Enforces adversarial review (Reviewer 2), 32 quality gates, tree search over hypotheses, confounder harness for quantitative claims, and serendipity detection. TRIGGER when: user asks to analyze scientific data, test hypotheses, validate findings, search for research gaps, design experiments, or investigate results. DO NOT TRIGGER when: pure code review, documentation writing, devops tasks, general conversation, or non-scientific data transformation.
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Define dygo App Pages and metadata-driven Studio experiences for Business Apps. Use when an App needs a Page, navigation entry, renderer choice, or Studio presentation beyond default Entity routes.
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Systematically document research findings, competitive analysis, market trends, and user insights in Notion. Build searchable research repository for data-driven decisions.
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Elite content marketing strategist specializing in AI-powered content creation, omnichannel distribution, SEO optimization, and data-driven performance marketing.
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Design UI components/design systems, review visual design, check accessibility. Triggers: design system, color palette, font pairing, WCAG check. Data-driven: palettes, fonts, UX guidelines.
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Use when designing UI components, creating design systems, reviewing visual design, or checking accessibility compliance. Triggers on "design system", "UI review", "color palette", "font pairing", "accessibility audit", "responsive design", "WCAG check". Auto-activates when ui-ux-designer agent is working. Data-driven: 160 palettes, 73 font pairings, 161 product types, 98 UX guidelines, 25 chart types.
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Use when writing Delphi / FreePascal / C++Builder code that reads, writes, manipulates, or exports Excel (.xlsx / .xls) files, generates PDF or HTML from Excel, or produces data-driven reports with FlexCel Studio for VCL and FireMonkey (TMS FlexCel). Triggers include Excel/xlsx from Delphi, TXlsFile, TFlexCelReport, TFlexCelPdfExport, TFlexCelHtmlExport, FireMonkey Excel export, Lazarus Excel, and Excel reporting from Pascal.
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Use when writing C# / VB.NET / F# code that reads, writes, manipulates, or exports Excel (.xlsx / .xls) files, generates PDF or HTML from Excel, or produces data-driven reports with FlexCel Studio for .NET (TMS Software). Triggers include Excel/xlsx from C#, XlsFile, FlexCelReport, FlexCelPdfExport, FlexCelHtmlExport, FlexCelImgExport, ExcelFile, PdfWriter, .NET Excel export, ASP.NET Excel generation, .NET MAUI Excel, Native AOT + Excel, and Excel reporting from .NET.
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Expert-level materials informatics covering materials databases, machine learning for property prediction, high-throughput computation, and data-driven materials discovery.
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Add data-driven audio with correct prediction, audience, resources, attribution, and verification.
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Transforms data into compelling visual narratives by applying narrative structure, annotation techniques, scrollytelling patterns, and honest framing to data journalism, presentations, and infographics. Use when creating data-driven articles or reports, designing infographics with narrative, building scrollytelling experiences, annotating charts to guide interpretation, or when user mentions data storytelling, presentation design, annotated chart, narrative visualization.
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NYT-discipline data visualization. Applies the design rules of The New York Times graphics desk + Upshot — color, typography, annotation, chart-type selection — to make any chart render at Times grade. Use whenever generating charts, dashboards, reports, or data-driven webpages. Triggers: "make this chart look professional", "NYT style", "editorial chart", "data viz", "dashboard design".
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Create, revise, and validate publication-ready academic paper figures and tables. Use for LaTeX tables, related-work comparison tables, result tables, notation/dataset/taxonomy tables, precise source-data-driven experiment plots from CSV/JSON/logs, generated conceptual figures such as system overviews/pipelines/architectures/threat models, captions, artifact specs, source-data traceability, and paper-ready PDF/SVG/PNG/LaTeX exports. Do not use for prose-only paper writing, self-review, reviewer response, rebuttal drafting, or external literature-management workflows.
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Comprehensive data science workflow skill for analyzing tabular and time series data. Use when conducting exploratory data analysis, building predictive models, performing feature engineering, or generating data-driven insights. Supports end-to-end analysis from data understanding through model deployment, following industry best practices for regression, classification, time series forecasting, and statistical analysis.
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Produces marketing performance reports and data-driven recommendations from WooCommerce sales data, social media metrics, and campaign analytics. Use when the task asks for "campaign performance", "ROI analysis", "monthly report", "sales trends", "product velocity", "audience insights", or "KPI tracking". Covers metric definition, reporting templates, slow-mover detection, and insight-to-action frameworks.
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Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why.
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Elite IT Management Advisor specializing in data-driven strategy, executive communication, and human-centric leadership for the 2026 digital era.
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Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.
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Master modern business analysis with AI-powered analytics, real-time dashboards, and data-driven insights. Build comprehensive KPI frameworks, predictive models, and strategic recommendations.
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TanStack Query (React Query) v5 best practices for data fetching, caching, mutations, and server state management. Use when building data-driven React applications, setting up query configurations, implementing mutations/optimistic updates, configuring caching strategies, integrating with SSR, or fixing v4→v5 migration errors.
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xUnit testing patterns and data-driven test guidance. Use when writing or reviewing .NET unit tests.
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Use when acting as the authoritative design lead for all frontend work, enforcing brand discovery, Anti-Slop rules, DTCG design tokens, fluid motion, and data-driven design generation.
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