44 件 ・ 関連度順
概要と使いどころ
[omh] Data pipeline work -- an ETL or streaming job, a backfill or replay, duplicate events, a schema change downstream, a lineage question, a data-quality regression: make every rerun idempotent, bound every replay, and gate each load on observed checks. Use when the user says: data-pipelines, data pipeline, data pipelines, etl, elt, etl pipeline, etl job, etl backfill.
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 and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
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 data pipelines for AI model training. TRIGGERS - Use when user needs help with ai-data-pipeline related tasks.
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。
Process, analyze, and visualize geospatial data at scale. Handles drone imagery, GPS tracks, GeoJSON optimization, coordinate transformations, and tile generation. Use for mapping apps, drone data processing, location-based services. Activate on "geospatial", "GIS", "PostGIS", "GeoJSON", "map tiles", "coordinate systems". NOT for simple address validation, basic distance calculations, or static map embeds.
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 data pipelines for AI model training. TRIGGERS - Use when user needs help with ai-data-pipeline related tasks.
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 pipeline expert for ETL, Apache Spark, Airflow, dbt, and data quality
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。
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
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。
ETL/ELT patterns, batch vs streaming, idempotency, data quality framework, and pipeline orchestration
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 pipeline specialist for ETL design, data quality, CDC patterns, and batch/stream processingUse when "data pipeline, etl, cdc, data quality, batch processing, stream processing, data transformation, data warehouse, data lake, data validation, data-engineering, etl, cdc, batch, streaming, data-quality, dbt, airflow, dagster, data-pipeline, ml-memory" mentioned.
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。
Dagster is a data pipeline orchestrator built around the concept of software-defined assets. Learn to define assets, ops, jobs, schedules, sensors, and resources for building maintainable data platforms.
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。
Validate data quality in CSV, JSON, and database exports by checking for missing values, type mismatches, duplicates, outliers, and schema violations. Use when building ETL pipelines, auditing data imports, checking data freshness, or ensuring data contracts between teams. Trigger words: data quality, validation, null values, duplicates, schema check, data contract, ETL, pipeline, data drift.
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You are an expert in Airbyte, the open-source data integration platform with 300+ pre-built connectors. You help developers sync data from SaaS tools, databases, and APIs into data warehouses and lakes — handling incremental syncs, CDC (Change Data Capture), schema evolution, and error recovery for production data 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。
Use when adding or changing a scheduled data or ML pipeline — Dagster assets, Prefect flows, Airflow DAGs or a cron'd entrypoint — idempotent partitioned stages, retries, backfills, quality gates, and testing the pipeline without the scheduler
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 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。
This skill should be used when a story has been captured but content direction is unresolved - the spark describes WHAT to build structurally but not WHAT goes in it. Reads the story spark, splits it into Resolved (structurally clear) vs. Assumed (would have to guess) content dimensions, and surfaces each assumption for the human to confirm, correct, or expand. Also classifies implementation risk tags (has-variants, has-data-pipeline, has-animation, has-touch-targets, diverges-from-existing) so plan-chunks translates them into acceptance criteria that name the implementation mechanism. Captures answers into
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 ingestion-to-serving pipelines on MotherDuck, including stage boundaries, transformations, and publication.
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。
Go development intelligence. 12 domains, 10 stacks, 300+ entries. Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check Go code. Domains: pattern, package, error, perf, test, concurrency, interface, arch, idiom, anti, tooling, module. Stacks: web-gin, web-echo, web-fiber, web-stdlib, cli, grpc, database, microservice, cloud-native, data-pipeline. Integrations: arch_system generator.
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 an arbitrary codebase to auto-detect workflows, data pipelines, and file dependencies using putior's put_auto() engine. Produces an annotation plan that maps detected I/O patterns to source files across 30+ supported languages with 902 auto-detection patterns. Use when onboarding onto an unfamiliar codebase to understand data flow, starting putior integration in a project without existing annotations, auditing a project's data pipeline before documentation, or preparing an annotation plan before running annotate-source-files.
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。
Write and harden reusable agent-loop specification documents for biomechanics, data science, and vailá coding work. Use when the user says `/preto-loop`, asks to write/create/forge/specify/document an agent loop, turn an iterative vailá task into an autonomous loop, make an overnight loop, or design a biomechanics, data-pipeline, markerless-tracking, testing, refactoring, or performance loop. Include external verification, scientific validity, GUI/CLI parity, reproducibility, memory, guardrails, and named terminal states. Do not use merely to repeat a fixed scheduled prompt, execute an existing loop, or create unrelated documents.
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Use this skill when configuring Data Manager in CRM Analytics: enabling objects for sync, scheduling data sync runs, setting up remote connections to external databases, monitoring sync status and error logs, or troubleshooting connected object issues. Trigger keywords: data sync, connected objects, Data Manager, sync schedule, remote connection, Snowflake connector, sync error, incremental sync, CRM Analytics data pipeline. NOT for pushing external data in programmatically via the External Data API — use data/analytics-external-data. NOT for authoring dataflow nodes — use admin/analytics-dataflow-development.
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 production-ready data API from scratch: scaffold REST endpoints with models and validation, generate integration tests that verify every route, then load test for scalability. Use when you need an API backend, data service, CRUD layer, or microservice with verified correctness and performance.
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。