部署与运维
4,382 个结果
- Data Pipeline BuilderOneWave AIDesigns 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.
- Databricks Ai RuntimeDatabricksDatabricks AI Runtime, the `databricks air` CLI commands for submitting and managing GPU training workloads on Databricks serverless compute. Use for: writing and submitting `databricks air` workload YAML, passing hyperparameters and secrets, checking run status, listing/cancelling runs, streaming a run's logs and watching its progress, custom Docker image setup, and environment configuration.
- Databricks App DesignDatabricksDesign the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS…
- Databricks Apps PythonDatabricksPython backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only.
- Databricks DabsDatabricksCreate, configure, validate, deploy, run, and manage Declarative Automation Bundles (DABs, formerly Databricks Asset Bundles). Use when working with Databricks resources via DABs including dashboards, jobs, pipelines, alerts, volumes, and apps.
- Databricks JobsDatabricksDevelop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation. For compute (serverless, performance mode, classic), also use databricks-serverless.
- Databricks Lakeflow ConnectDatabricksBuild managed ingestion pipelines into Databricks using Lakeflow Connect. Use when ingesting from SaaS apps (Salesforce, Workday Reports, ServiceNow, Google Analytics 4, HubSpot, Confluence) or databases (SQL Server cloud and on-prem; PostgreSQL/MySQL CDC in PuPr) into Unity Catalog with serverless pipelines.
- Databricks Mlflow EvaluationDatabricksMLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt…
- Databricks Model ServingDatabricksDatabricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage legacy AI Gateway rate limits (not Unity Gateway); discover Foundation Model API endpoints at runtime…
- Databricks ServerlessDatabricksServerless compute for Databricks jobs, Lakeflow pipelines and Declarative Automation Bundles (DABs), with STANDARD or PERFORMANCE_OPTIMIZED and classic only where serverless cannot run the workload. Use when creating, deploying, scheduling or editing a job or pipeline, or deciding its compute. Invoke BEFORE writing a job spec.
- Lucas Runbooka2wioLucas runbook-driven troubleshooting for Kubernetes issues. Use when investigating pod errors, crashes, restarts, or any Kubernetes problem. Automatically search runbooks before taking action.
- Writing Science SkillsK-DenseUse when creating new skills, editing existing skills, or verifying skills work before deployment
- AWS Lambda Java Integrationgiuseppe-trisciuoglioProvides AWS Lambda integration patterns for Java with cold start optimization. Use when deploying Java functions to AWS Lambda, choosing between Micronaut and Raw Java approaches, optimizing cold starts below 1 second, configuring API Gateway or ALB integration, or implementing serverless Java applications.
- AWS Lambda Php Integrationgiuseppe-trisciuoglioProvides AWS Lambda integration patterns for PHP with Symfony using the Bref framework. Creates Lambda handler classes, configures runtime layers, sets up SQS/SNS event triggers, implements warm-up strategies, and optimizes cold starts.
- AWS Lambda Python Integrationgiuseppe-trisciuoglioProvides AWS Lambda integration patterns for Python with cold start optimization. Use when deploying Python functions to AWS Lambda, choosing between AWS Chalice and raw Python approaches, optimizing cold starts, configuring API Gateway or ALB integration, or implementing serverless Python applications.
- AWS Lambda Typescript Integrationgiuseppe-trisciuoglioProvides AWS Lambda integration patterns for TypeScript with cold start optimization. Use when creating or deploying TypeScript Lambda functions, choosing between NestJS framework and raw TypeScript approaches, optimizing cold starts, configuring API Gateway or ALB integration, or implementing serverless TypeScript applications.
- AWS Sdk Java V2 Bedrockgiuseppe-trisciuoglioProvides Amazon Bedrock patterns using AWS SDK for Java 2.x. Invokes foundation models (Claude, Llama, Titan), generates text and images, creates embeddings for RAG, streams real-time responses, and configures Spring Boot integration. Use when asking about Bedrock integration, Java SDK for AI models, AWS generative AI, Claude/Llama invocation, embeddings for RAG, or Spring Boot AI setup.
- AWS Sdk Java V2 Coregiuseppe-trisciuoglioCore patterns and best practices for AWS SDK for Java 2.x. Use when configuring AWS service clients, setting up authentication, managing credentials, configuring timeouts, HTTP clients, or following AWS SDK best practices.
- AWS Sdk Java V2 Dynamodbgiuseppe-trisciuoglioProvides Amazon DynamoDB patterns using AWS SDK for Java 2.x. Use when creating, querying, scanning, or performing CRUD operations on DynamoDB tables, working with indexes, batch operations, transactions, or integrating with Spring Boot applications.
- AWS Sdk Java V2 Kmsgiuseppe-trisciuoglioProvides AWS Key Management Service (KMS) patterns using AWS SDK for Java 2.x. Use when creating/managing encryption keys, encrypting/decrypting data, generating data keys, digital signing, key rotation, or integrating encryption into Spring Boot applications.
- AWS Sdk Java V2 Lambdagiuseppe-trisciuoglioProvides AWS Lambda patterns using AWS SDK for Java 2.x. Use when invoking Lambda functions, creating/updating functions, managing function configurations, working with Lambda layers, or integrating Lambda with Spring Boot applications.
- AWS Sdk Java V2 Messaginggiuseppe-trisciuoglioProvides AWS messaging patterns using AWS SDK for Java 2.x for SQS queues and SNS topics. Handles sending/receiving messages, FIFO queues, DLQ, subscriptions, and pub/sub patterns. Use when implementing messaging with SQS or SNS.
- AWS Sdk Java V2 Rdsgiuseppe-trisciuoglioProvides AWS RDS (Relational Database Service) management patterns using AWS SDK for Java 2.x. Use when creating, modifying, monitoring, or managing Amazon RDS database instances, snapshots, parameter groups, and configurations.
- AWS Sdk Java V2 S3giuseppe-trisciuoglioProvides Amazon S3 patterns and examples using AWS SDK for Java 2.x. Use when working with S3 buckets, uploading/downloading objects, multipart uploads, presigned URLs, S3 Transfer Manager, object operations, or S3-specific configurations.