633건 · 관련도순
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.
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The pedagogy of feeding — how to talk about food with children, what the Division of Responsibility model says, age-appropriate autonomy in eating, how to handle picky eating without creating disordered eating, and how to teach nutrition concepts to learners at different levels without either moralizing or oversimplifying. Use when a question is about raising, teaching, or advising a child or learner on eating, or when planning curriculum or parent guidance.
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Constructs Kubernetes-native workflow DAGs using Argo Workflows CRDs with configurable retry strategies, artifact passing via S3/MinIO, and template composition through WorkflowTemplates and ClusterWorkflowTemplates.
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Analyzes Argo Workflows DAG templates to identify parallelization opportunities. Uses the Argo Server API to fetch workflow execution history and critical path analysis.
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Orchestrate end-to-end machine learning pipelines using Prefect or Airflow with DAG construction, task dependencies, retry logic, scheduling, monitoring, and integration with MLflow, DVC, and feature stores for production ML workflows. Use when automating multi-step ML workflows from data ingestion to deployment, scheduling periodic model retraining, coordinating distributed training tasks, or managing retry logic and failure recovery across pipeline stages.
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Analyze a repository's type system and generate type-safe DAG execution pipelines with GraphSentry-style certificate verification. This skill should be used when building LLM-driven workflows that need deterministic type safety, when composing typed operations into validated execution graphs, or when adding contract-checked pipeline orchestration to any codebase. Supports Python (Pydantic) and TypeScript (Zod).
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"Apply the TPACK framework to evaluate and design technology-integrated instruction at the intersection of technological, pedagogical, and content knowledge. Use this skill when the user needs to assess teacher readiness for technology integration, design professional development for ed-tech, or evaluate whether technology use is pedagogically grounded — even if they say 'how to integrate technology in teaching', 'ed-tech evaluation', or 'teacher technology competency'.".
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dbt Core/Cloud data transformations, testing, documentation, and CI/CD. Activate on: dbt, data transformation, analytics engineering, ref, source, staging model, mart, dbt test. NOT for: orchestration/scheduling (use airflow-dag-orchestrator), data warehouse tuning (use data-warehouse-optimizer).
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Tracks cumulative LLM costs across DAG execution and makes real-time decisions to stay within budget. Downgrades models, skips optional nodes, or stops early when cost exceeds thresholds. Use when managing execution budgets, analyzing cost breakdowns, or optimizing model routing for cost. Activate on "cost budget", "too expensive", "reduce cost", "cost optimization", "model downgrade", "budget exceeded". NOT for LLM model selection logic (use llm-router), pricing comparisons across providers, or billing/invoicing.
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Introduce people to AI, agentic AI, and AI skills with appropriate pedagogy for any audience. Covers LLM analogies, progressive complexity paths, aha-moment demos, audience-tailored explanations, workshop design, and overcoming emotional barriers to AI adoption. Activate on 'explain AI', 'introduce AI', 'AI workshop', 'AI demo', 'teach AI', 'AI literacy', 'onboard to AI', 'AI evangelism', 'explain agentic', 'explain skills to', 'AI training session', 'AI onboarding'. NOT for teaching ML engineering (use ai-engineer), not for building AI features (use ai-engineer), not for prompt engineering (use prompt-engineer), not for building training data pipelines.
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YAML-based DAG workflow engine with topological execution and failure strategies
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Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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Add a new Dagster asset to the pipeline. Use when creating a Bronze, Silver, or Gold layer asset.
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dev-graph の1 featureを exact 13 task specsへ分解したいとき、独立評価後に13-entry inventory・13-node DAGを atomic promotionしたいときに使う。
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Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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Build container-based Foundry Agents using Azure AI Projects SDK with ImageBasedHostedAgentDefinition. Use when creating hosted agents that run custom code in Azure AI Foundry with your own container images. Triggers: "ImageBasedHostedAgentDefinition", "hosted agent", "container agent", "Foundry ...
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Design and execute DAG-based automation workflows using the NV oOS Pro Workflow Builder — a ReactFlow visual builder for chaining tool calls, agent runs, and actions into repeatable pipelines. Covers the visual builder UI, 9 preset categories, 10 node types, Kahn's algorithm execution engine, scheduling via Pro Schedule Manager, template variable syntax, and best practices. Use when designing workflows, debugging DAG execution failures, choosing between a workflow schedule and raw tool chaining, or building CRM/PM automation pipelines with toolkit_cpt nodes.
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Design and execute DAG-based automation workflows using the NV oOS Pro Workflow Builder — a ReactFlow visual builder for chaining tool calls, agent runs, and actions into repeatable pipelines. Covers the visual builder UI, 10 preset categories (incl. AI Fashion Production, v1.1.92+), 10 node types, Kahn's algorithm execution engine, scheduling via Pro Schedule Manager, template variable syntax, and best practices. Use when designing workflows, debugging DAG execution failures, choosing between a workflow schedule and raw tool chaining, or building CRM/PM automation pipelines with toolkit_cpt nodes.
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Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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Use when working with Airflow — apache Airflow workflow orchestration management. Covers DAG management, task instance monitoring, executor health, pool status, variable management, connection audit, and scheduler diagnostics. Use when checking DAG run status, investigating task failures, managing Airflow resources, or auditing configurations.
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Use when working with Apache Airflow Deep — apache Airflow deep management covering DAG inventory, task instance monitoring, scheduler health, executor status, pool and variable management, connection auditing, and SLA miss tracking. Use when investigating DAG failures, analyzing task duration trends, monitoring scheduler performance, or auditing Airflow configurations.
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Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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