216건 · 관련도순
Create flat SVG illustrations of long-legged people in design and coding scenes. Large black shapes, bright colors, and consistent poses give onboarding screens, error pages, and marketing cards a shared visual style.
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Create original SVG illustration cards featuring a cosy animal with computers, books, or mugs. A bright pink background, softly shaded shapes, and fine grain give the scenes a textured, painted feel for product pages and blog headers.
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Build subtle animated petals, snow, confetti, or other decorative particles within a single page section. Adjust their movement and density while preserving readable content, limiting device workload, and providing static alternatives for readers who reduce motion.
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Create original SVG illustrations with expressive cartoon characters, bold ink outlines, and a prominent prop. Use the transparent artwork for onboarding, empty states, success screens, blog headers, or marketing cards, with checks for consistent styling and believable hands.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Create SVG illustrations centered on one physical object, with white faces, teal sides, dark outlines, and hard shadows. Use this consistent, people-free style for onboarding steps, empty states, feature cards, and product imagery.
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Inspect an Apache Hudi table’s storage layout using a read-only analysis tool. Get report-based findings on table size, uneven data distribution, excessive partitions, small files, and concentrated writes, with suggested next steps.
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Inspect images and videos you generated or edited for AI watermarks and embedded origin information, then remove selected signals. The skill guides a command-line tool and reports completed operations without treating missing signals as proof of human authorship.
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Coordinate code migrations and repeated implementations through specialized agents. Use test-driven development, writing failing tests before code, then run builds, quality checks, reviews, and comparisons with a reference pattern to validate the resulting implementation.
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Plan experiments, analyze data, and build and evaluate predictive models. The skill also covers explaining findings to stakeholders and designing production workflows for model deployment, monitoring, and retraining to support business decisions.
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Set up a NVIDIA Holoscan Sensor Bridge demo for the selected development kit. The workflow configures the host, builds and launches the demo container, checks board connectivity and FPGA version, and reports issues encountered.
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Selects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services. Applies when a user asks where to store or archive data based on their usage patterns; which storage service to choose or how two compare; how to migrate data from on-premises or between AWS services; how to protect, replicate, or recover data; how to optimize storage costs; where to deploy shared NFS, SMB, or POSIX file systems; where to store vector embeddings or tabular data; what storage backs enterprise file shares, self-managed databases on EC2, VMware, or stateful containers; or asks what an AWS storage service can do or how it works. Relevant for storage needs for workloads such as AI/ML, analytics, EDA, HPC, media, genomics, or financial trading. Not applicable for SQL query engines (Athena, Spark, Redshift, EMR), ETL (Glue), streaming (Kafka, MSK, Kinesis), or managed database services (RDS, Aurora, DynamoDB).
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Data engineering patterns for ETL pipelines, data warehousing, Apache Spark, and data quality validation
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Build a dense, data-heavy screen - an analytics console or a trading terminal - with real charts drawn from tokens: candlestick, volume, depth, stacked area, waterfall, scatter, correlation matrix, donut, gauge, sparklines, heatmaps and order books. Use when the request is a dashboard, terminal, monitoring view, data console, or any screen whose job is to show many numbers at once. Covers the layout sequence, the SVG geometry, the accessibility contract per chart type, and the contrast traps that only appear in dark mode.
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When the user wants to plan, brief, source, or measure organic creator / influencer / UGC marketing for their app — including TikTok creators, Instagram Reels, YouTube Shorts, micro-influencers, paid creator briefs, UGC ad creative for Meta/TikTok, affiliate programs, and seeding strategy. Use when the user mentions "creators", "influencers", "TikTok marketing", "UGC", "user-generated content", "creator briefs", "creator outreach", "influencer marketing", "Reels", "Shorts", "creator gifting", "seeding", "affiliate", "Whop", "TikTok Spark Ads", "Instagram collab posts", or "I want my app to go viral on TikTok". For paid social ad campaigns (Meta/TikTok ad accounts), see ua-campaign. For PR / press, see press-and-pr. For viral in-app loops, see referral-program.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality monitoring, and DataOps. Use when designing data architectures, building batch or streaming data pipelines, optimizing data workflows, or implementing data governance.
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Use this skill to build a multi-element Excel dashboard — Dashboard sheet on open, multiple formula-driven KPI cards, multiple charts, sparklines, and conditional formatting — from CSV or tabular input. Trigger on: 'dashboard', 'KPI dashboard', 'analytics dashboard', 'executive dashboard', 'metrics dashboard', 'CSV to dashboard', 'data visualization'. Output is a single .xlsx. Scene-layer on officecli-xlsx: inherits every xlsx hard rule. DO NOT invoke for: a single budget tracker / one-sheet CSV-with-formatting (use xlsx), a 3-statement / DCF / LBO financial model (use financial-model), a weekly report with ≤ 1 chart and < 10 rows (use xlsx).
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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.
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Use this skill to build a multi-element Excel dashboard — Dashboard sheet on open, multiple formula-driven KPI cards, multiple charts, sparklines, and conditional formatting — from CSV or tabular input. Trigger on: 'dashboard', 'KPI dashboard', 'analytics dashboard', 'executive dashboard', 'metrics dashboard', 'CSV to dashboard', 'data visualization'. Output is a single .xlsx. Scene-layer on officecli-xlsx: inherits every xlsx hard rule. DO NOT invoke for: a single budget tracker / one-sheet CSV-with-formatting (use xlsx), a 3-statement / DCF / LBO financial model (use financial-model), a weekly report with ≤ 1 chart and < 10 rows (use xlsx).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when reviewing a DataFusion Comet pull request that touches memory pools, memory reservations, spilling, the Spark TaskMemoryManager bridge, allocators, or memory-related configuration. Load alongside review-comet-pr.
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Use when reviewing a DataFusion Comet pull request that adds or changes a Spark expression, its Scala serde, its protobuf message, or its native Rust implementation. Load alongside review-comet-pr, which covers the parts of the review that apply to every PR.
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Audit an existing Comet expression for correctness and test coverage. Studies the Spark implementation across versions 3.4.3, 3.5.8, 4.0.1, and 4.1.1, reviews the Comet and DataFusion implementations, identifies missing test coverage, and offers to implement additional tests.
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Manages Fabric Dataflow Gen2 items, including creation, M editing, connections, output destinations, getDefinition and updateDefinition inspection, executeQuery, refresh history, and Gen1-to-Gen2 save-as upgrades via saveAsNativeArtifact. Use for Dataflow items or Power Query M definitions. For pipeline JSON use pipeline-migration; for Spark or Warehouse transformations use their own skills.
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