「spark」の検索結果

Adhoc workflow for small targeted changes to existing behavior or appearance, without the full story ceremony. Use when the orchestrator encounters a bug with a clear root cause and surgical solution, OR a small enhancement to something already built (a different icon, wording, spacing, color). Triggers on: 'fix this', 'this is broken', 'the alignment is off', 'that button doesn't work', 'quick fix', 'patch this', 'tweak this', 'change the icon', 'adjust the wording', post-story corrections, or any situation where the write gate blocks a small targeted change. Acts NOW - a defect to file and fix later belongs in /craft:bugs, other deferred work in the notebook. Do NOT use for new features, design exploration, or changes requiring creative spark.

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This skill should be used when the user asks to "brainstorm", "explore options", "get creative", "what if we...", or selects "Let's get creative" during story or cycle creation. NOT for a bare vague feature idea where generating options would mean inventing the premise first - that routes to /craft:riff (riff first, options after). Generates 2-3 creative options with trade-offs, visual direction for UI stories, and wireframes.

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This skill should be used when the user asks to "plan chunks", "break this down", "plan the implementation", or when a story has status planning and needs implementation details before building. Required before any story can be implemented. Transforms story sparks into detailed chunk-by-chunk implementation plans with full technical specifics, file lists, and risk analysis. Supports parallel planning of multiple stories via batch mode.

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Render a dashboard of the current Claude Code session — message counts, token usage, cache read/write, dollar cost, context %, lines changed, cost-per-turn sparkline, and a tool-call histogram — parsed from the session transcript JSONL by a bundled bash script. One bash call, zero analysis, zero confirmation. Optionally pass a session id to inspect a different session. Use when the user asks for session stats, usage, token or cost breakdown, or a session dashboard.

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Start and maintain a fire using friction, spark, and solar methods. Covers site selection, material grading (tinder/kindling/fuel), fire lay construction (teepee, log cabin, platform), ignition techniques (ferro rod, flint & steel, bow drill), flame nurturing, and Leave No Trace extinguishing. Use when needing warmth, light, or a signal in a wilderness setting, when boiling water for purification, when cooking foraged food, or in an emergency survival situation requiring heat or morale support.

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Converter 에이전트 v7.0. 승인된 proposal_script.md + visualization_strategy.md를 Gemini 슬라이드 디자인 요청용 proposal_slides.json (v2.0)으로 변환합니다. SPARK-6 구조, S-E-P, 40종 레이아웃, design_intent, gemini_instructions를 포함합니다. 트리거: JSON 변환, convert, proposal_slides.json, Gemini 디자인, 스크립트 변환, 제미나이

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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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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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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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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"

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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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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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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Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads

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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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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.

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 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.

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 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).

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。