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概要と使いどころ
Expert guide for Data Pipelines, ETL/ELT, and Analytics Engineering. Covers dbt, Apache Airflow, Dagster, BigQuery, ClickHouse, and DuckDB / Panduan ahli untuk Data Pipelines, ETL/ELT. Mencakup dbt, Airflow, Dagster, BigQuery, ClickHouse, dan DuckDB.
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 building data pipelines, ETL jobs, event processors, message-broker producers/consumers, application caching layers, database migrations, BigQuery queries, or event-sourcing handlers in TypeScript. Triggers on edits to indexer/ETL/migration code, Prisma or Drizzle schemas, BigQuery integration code, RabbitMQ/Kafka/SQS/BullMQ producer or consumer code, Redis cache wrappers, or mentions of "data engineering", "ETL", "pipeline", "indexer", "event sourcing", "data warehouse", "data migration", "message queue", "RabbitMQ", "Kafka", "SQS", "BullMQ", "Redis cache", or "caching".
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。
Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).
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。
Operate Netlify's REST API (sites, deploys, builds, env vars, forms, DNS). Use programmatically without Netlify MCP.
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。
Run AI agent tasks remotely on Netlify using Claude, Codex, or Gemini. Use when the user wants to run an AI agent on their site, get a second opinion from another model, or delegate development tasks to run remotely against their repo.
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。
Designs reliable ETL and data synchronization jobs with incremental updates, idempotency guarantees, watermark tracking, error handling, and retry logic. Use for "ETL jobs", "data sync", "incremental sync", or "data pipeline".
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。
Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).
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。
大量データの取り込みや過去分の補充、変換、同期の遅さを工程別に調べ、処理方法を比較して高速化し、行数・ファイル数・時刻の整合性まで確認するスキル。
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
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。
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
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。
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
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。
Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, vector indexes for similarity search, and zero-ETL integrations to OpenSearch/Redshift/SageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines, or storing embeddings and running semantic/vector similarity search with SearchVectors on items already in DynamoDB — even when they don't say "axioms" or "design review." Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.
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。
Analyze KiCad projects and PDF schematics: schematics, PCB layouts, Gerbers, footprints, symbols, netlists, and design rules. Reviews designs for bugs, traces nets, cross-references schematic to PCB, extracts BOM data, checks DRC/ERC, DFM, power trees, and regulator circuits. Every finding carries a confidence label and evidence source with trust_summary rollup. Analyzes PDF schematics from dev boards, reference designs, eval kits, and datasheets. Supports KiCad 5–10. Use whenever the user mentions .kicad_sch, .kicad_pcb, .kicad_pro, PCB design review, schematic analysis, PDF schematics, reference designs, Gerber files, DRC/ERC, netlist issues, BOM extraction, signal tracing, power budget, DFM, or wants to understand, debug, compare, or review any hardware design. Also for "check my board", "review before fab", "what's wrong with my schematic", "is this ready to order", "check my power supply", "verify this circuit", OSHWA certification readiness, or any electronics/PCB design question.
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。
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
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。
Analyze KiCad projects and PDF schematics: schematics, PCB layouts, Gerbers, footprints, symbols, netlists, and design rules. Reviews designs for bugs, traces nets, cross-references schematic to PCB, extracts BOM data, checks DRC/ERC, DFM, power trees, and regulator circuits. Every finding carries a confidence label and evidence source with trust_summary rollup. Analyzes PDF schematics from dev boards, reference designs, eval kits, and datasheets. Supports KiCad 5–10. Use whenever the user mentions .kicad_sch, .kicad_pcb, .kicad_pro, PCB design review, schematic analysis, PDF schematics, reference designs, Gerber files, DRC/ERC, netlist issues, BOM extraction, signal tracing, power budget, DFM, or wants to understand, debug, compare, or review any hardware design. Also for "check my board", "review before fab", "what's wrong with my schematic", "is this ready to order", "check my power supply", "verify this circuit", OSHWA certification readiness, or any electronics/PCB design question.
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。
Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use when designing data architectures, building pipelines, adding data-quality checks, optimizing ETL/ELT, or troubleshooting pipeline failures.
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 automated ETL (Extract-Transform-Load) pipelines for construction data. Process PDFs, Excel, BIM exports. Generate reports, dashboards, and integrate with other systems. Orchestrate with Airflow or n8n.
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。
Applies pipes-and-filters for sequential data transformations. Use when data flows through discrete stages like ETL, streaming analytics, or CI/CD pipelines.
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。
| Nuxt 5 production optimization: hydration, performance, testing with Vitest, deployment to Cloudflare/Vercel/Netlify, and migration from Nuxt 4. Use when: debugging hydration mismatches, optimizing performance and Core Web Vitals, writing tests with Vitest, deploying to Cloudflare Pages/Workers/Vercel/Netlify, or migrating from Nuxt 4 to Nuxt 5.
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。
| Nuxt 4 production optimization: hydration, performance, testing with Vitest, deployment to Cloudflare/Vercel/Netlify, and v4 migration. Use when: debugging hydration mismatches, optimizing performance and Core Web Vitals, writing tests with Vitest, deploying to Cloudflare Pages/Workers/Vercel/Netlify, or migrating from Nuxt 3 to Nuxt 4.
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。
dlt (data load tool) is an open-source Python library that loads data from APIs, files and databases into warehouses and lakes, inferring the schema and tracking incremental state for you. Use when a user asks to build a data pipeline in Python, load a REST API into DuckDB, BigQuery, Snowflake or Postgres, set up incremental or merge loading, enforce a schema contract, or replace a hand-written ETL script or a hosted ELT connector.
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。
When the user needs to migrate data between databases, transform schemas, or consolidate data sources. Use when the user mentions "data migration," "database migration," "migrate from MySQL to PostgreSQL," "schema migration," "ETL pipeline," "data transfer," "database consolidation," "legacy migration," or "move data between databases." Covers schema analysis, mapping, transformation, batch processing, validation, and cutover planning. For query optimization during migration, see sql-optimizer.
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。
Apache Spark is a distributed engine for batch, SQL and streaming data processing, used from Python through PySpark. Use when a user asks to process big data, run distributed computations, build ETL pipelines, analyse data at scale, read Parquet from S3, or stream from Kafka with PySpark.
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 a data pipeline — ETL/ELT with extraction, transformation, loading, error handling, and scheduling. Use when asked to "build ETL", "data pipeline", "move data from X to Y", or "sync data".
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。