210건 · 관련도순
제안서 QC 검수 에이전트 (Reviewer) v5.0. proposal_script.md 콘텐츠 품질 + proposal_slides.json 변환 정확성 + proposal.html 시각 품질을 검증합니다. SPARK-6, S-E-P, Win Theme, 패턴 다양성, 분위기 리듬 등 검수. PPTX 모드도 지원. 트리거: QC, 검수, 리뷰, 검증, 체크, 품질, /r
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Generate a production-grade Industrial IoT protocol bridge — OPC UA ↔ MQTT Sparkplug B, Modbus → MQTT, or direct PLC (Rockwell/Siemens/Beckhoff) → cloud (AWS IoT Core / Azure IoT Hub / GCP IoT / self-hosted HiveMQ)..
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Audit TikTok Ads measurement, Pixel, mobile-first creative, audiences, Smart+, Shop and commerce campaigns, bidding, budgets, pacing, attribution, and policy. Use for TikTok Ads, TikTok Pixel, Smart+, TikTok Shop Ads, GMV Max, or Spark Ads. Read-only; use ads-server-side-tracking for server-side events and ads-optimize to change campaigns.
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WCAG 準拠のアクセシビリティチェックをチェックリスト駆動で実行するスキル。 コンポーネント・ページ・PR に対して Pass/Fail/N/A/Needs review の構造化レポートを 生成し、エビデンスと修正案を提示する。 「アクセシビリティチェック」「a11y チェック」「WCAG をチェック」「a11y レビュー」 「アクセシビリティ監査」「WCAG 準拠確認」「アクセシビリティレポート」への言及、 または PR やコンポーネントのアクセシビリティレビュー依頼で発動する。 English: "check accessibility", "a11y audit", "a11y check", "WCAG review", "accessibility report", "screen reader test", "review for a11y", "review this PR for accessibility", "axe audit"
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Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).
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This skill should be used when designing terminal user interfaces, creating TUI layouts, choosing TUI color schemes, implementing keyboard navigation, building terminal dashboards, or working with any TUI framework. Activates on mentions of TUI design, terminal UI, Ratatui layout, Ink components, Textual widgets, Bubbletea views, terminal color palette, keybinding design, panel layout, split panes, terminal dashboard, box-drawing characters, sparklines, progress bars, modal dialogs, focus management, or terminal accessibility.
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Construa pipelines de dados escaláveis, data warehouses modernos e arquiteturas de streaming em tempo real. Implementa Apache Spark, dbt, Airflow e plataformas de dados nativas em nuvem.
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Releases Sparkle-updated macOS apps end to end: build, notarization, appcast, GitHub Release, Homebrew cask, and closeout checks. Use when shipping a new version of a Mac menu-bar app or utility.
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Use when the user asks to "amplify influencer content with paid media", "set up whitelisting or Spark Ads", "decide which posts to boost", "repurpose influencer content", "turn one video into multiple ads", or "build a UGC asset library"; produces (paid mode) a content-selection scorecard, a paid amplification strategy (whitelisting/boosting/dark posts), audience targeting, and a budget+optimization plan, or (repurpose mode) a rights-tracked content inventory, a 1-video-to-10+-asset repurposing map, per-format transformation specs, and a 30-day distribution plan. Not for gating whether a deliverable is publishable or FTC-compliant — use creator-content-auditor; not for the always-on brand posting calendar — use social-calendar-builder; not for drafting a net-new idea into platform-native packages — use social-creative-builder. 复用达人内容 / 内容放量.
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Audit TikTok Ads measurement, Pixel and Events API, mobile-first creative, audiences, Smart+, Shop and commerce campaigns, bidding, budgets, pacing, attribution, and policy. Use for TikTok Ads, TikTok Pixel, Events API, Smart+, TikTok Shop Ads, GMV Max, Spark Ads, or TikTok campaign optimization.
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Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads
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Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.
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Implement, review, or improve data visualizations using Swift Charts. Use when building bar, line, area, point, pie, or donut charts; when adding chart selection, scrolling, or annotations; when plotting functions with vectorized BarPlot, LinePlot, AreaPlot, or PointPlot; when customizing axes, scales, legends, or foregroundStyle grouping; or when creating specialized visualizations like heat maps, Gantt charts, stacked/grouped bars, sparklines, or threshold lines.
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TikTok Ads deep analysis covering creative quality, tracking, bidding, campaign structure, and TikTok Shop. Evaluates 28 checks with emphasis on creative-first strategy, safe zone compliance, and Smart+ campaigns. Use when user says TikTok Ads, TikTok marketing, TikTok Shop, Spark Ads, Smart+, or TikTok campaign.
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Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).
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Maxim Presentation Intelligence — pitch decks, investor presentations, executive briefings, product demos, conference talks, narrative-driven visual storytelling, data visualization in slides. Applies Minto Pyramid, Duarte Sparkline, McKinsey Slide Logic, and Dual Coding Theory to every deck. Powered by Maxim behavioral science layer.
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Master enterprise-grade Scala development with functional programming, distributed systems, and big data processing. Expert in Apache Pekko, Akka, Spark, ZIO/Cats Effect, and reactive architectures. Use PROACTIVELY for Scala system design, performance optimization, or enterprise integration.
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Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.