Results for “serverless”

Provides AWS CDK TypeScript patterns for defining, validating, and deploying AWS infrastructure as code. Use when creating CDK apps, stacks, and reusable constructs, modeling serverless or VPC-based architectures, applying IAM and encryption defaults, or testing and reviewing `cdk synth`, `cdk diff`, and `cdk deploy` changes. Triggers include "aws cdk typescript", "create cdk app", "cdk stack", "cdk construct", "cdk deploy", and "cdk test".

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Databricks 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.

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Build 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.

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Execute code and manage compute on Databricks: run Python/Scala/SQL/R via serverless, classic, or interactive clusters, and create/resize/delete clusters and SQL warehouses.

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Previews, provisions, or diagnoses a uv-managed local Python .venv with `databricks environments setup-local`. Use when the user wants to set up or fix one for Databricks Connect, cluster or serverless compute, `--job-task`, or a bundle target, or when setup-local fails.

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Generate realistic synthetic data using Spark + Faker (strongly recommended). Supports serverless execution, multiple output formats (Parquet/JSON/CSV/Delta), and scales from thousands to millions of rows. For small datasets (<10K rows), can optionally generate locally and upload to volumes. Use when user mentions 'synthetic data', 'test data', 'generate data', 'demo dataset', 'Faker', or 'sample data'.

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Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.

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Cloudflare Workers AI for serverless GPU inference. Use for LLMs, text/image generation, embeddings, or encountering AI_ERROR, rate limits, token exceeded errors.

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Migrate to Cloudflare Workers from AWS Lambda, Vercel, Express, and Node.js. Use when porting existing applications to the edge, adapting serverless functions, or resolving Node.js API compatibility issues.

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Cloudflare D1 serverless SQLite on edge. Use for databases, migrations, bindings, or encountering D1_ERROR, statement too long, too many requests queued errors.

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Vast.ai CLI to manage GPU instances, volumes, serverless endpoints, and billing.

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Vast.ai Python SDK — high-level API for GPU instances, volumes, serverless endpoints, and billing.

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Deploy trained machine learning models as production-ready services using REST APIs, containers, serverless functions, and orchestration platforms. Use when the user requests model deployment or provides relevant inputs for this workflow.

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Serverless vector database at the edge with Cloudflare Vectorize. Use when: building semantic search on Cloudflare Workers, RAG pipelines at the edge, low-latency vector similarity search, or storing and querying embeddings without managing a separate vector database.

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Cloudflare Workers is an edge computing platform for running JavaScript, TypeScript, and WebAssembly close to users worldwide with sub-millisecond cold starts. Use when working with the Workers runtime, the Wrangler CLI, KV, D1, R2, Durable Objects, Queues, or Hyperdrive, or when a user asks to build and deploy an edge function.

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Run containerized applications with Amazon ECS. Define tasks and services, deploy on Fargate for serverless containers or EC2 for full control. Configure load balancing, auto-scaling, and service discovery for production workloads.

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Build with Amazon DynamoDB for fast, scalable NoSQL storage. Design tables with partition and sort keys, create GSI and LSI for flexible queries, enable streams for change data capture, and apply single-table design patterns for efficient data modeling.

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Build backends with Appwrite — open-source Backend-as-a-Service. Use when a user asks to set up user authentication, manage a database without writing backend code, handle file storage and uploads, add realtime subscriptions, set up cloud functions, build a mobile or web app backend, replace Firebase with an open-source alternative, or self-host a BaaS platform. Covers auth, databases, storage, functions, realtime, and SDK integration for web, mobile, and server-side.

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Manage Amazon RDS for fully managed relational databases. Provision MySQL, PostgreSQL, and Aurora instances with automated backups and patching. Configure read replicas for scaling, snapshots for recovery, and Aurora Serverless for variable workloads.

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Ably is a hosted realtime messaging service: clients publish and subscribe to named channels over WebSockets, see who is present, replay message history, and resume after a dropped connection. Use when a user asks to "add realtime updates", "push live notifications to the browser", "show who is online", "add a chat room with typing indicators", "publish from a serverless function", or "authenticate Ably clients without exposing the API key". Covers the ably 2.x JavaScript SDK (Realtime and REST), JWT token authentication, presence, history and rewind, batch publishing, and the @ably/chat 1.x SDK.

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Firebase gives you a complete backend in minutes - auth, database, storage, functions, hosting. But the ease of setup hides real complexity. Security rules are your last line of defense, and they're often wrong. Firestore queries are limited, and you learn this after you've designed your data model. This skill covers Firebase Authentication, Firestore, Realtime Database, Cloud Functions, Cloud Storage, and Firebase Hosting. Key insight: Firebase is optimized for read-heavy, denormalized data. If you're thinking relationally, you're thinking wrong. 2025 lesson: Firestore pricing can surprise you. Reads are cheap until they're not. A poorly designed listener can cost more than a dedicated database. Plan your data model for your query patterns, not your data relationships. Use when "firebase, firestore, firebase auth, cloud functions, firebase storage, realtime database, firebase hosting, firebase emulator, security rules, firebase admin, firebase, firestore, cloud-functions, serverless, backend, realtime, authentication, google-cloud" mentioned.

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AWS Advisor workflow skill. Use this skill when the user needs Expert AWS Cloud Advisor for architecture design, security review, and implementation guidance. Leverages AWS MCP tools for accurate, documentation-backed answers. Use when user asks about AWS architecture, security, service selection, migrations, troubleshooting, or learning AWS. Triggers on AWS, Lambda, S3, EC2, ECS, EKS, DynamoDB, RDS, CloudFormation, CDK, Terraform, Serverless, SAM, IAM, VPC, API Gateway, or any AWS service. Do NOT use for non-AWS cloud providers or general infrastructure without AWS context and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Recommend architecture patterns (monolith, microservices, serverless, modular monolith) based on scale, team size, and constraints. Use when cto-architect needs to select the right architectural approach for a new system or migration.

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Serverless Aura Graph Analytics (AGA) GDS Sessions — covers GdsSessions, AuraGraphDataScience, AuraAPICredentials, DbmsConnectionInfo, SessionMemory, get_or_create, remote graph projection with gds.graph.project.cypher and gds.graph.project.remote, gds.graph.project.native, gds.graph.construct, graphdatascience client 2.0 session endpoints, async compute and gds.jobs, AuraDB Cypher API memory/sessionId projection, algorithms, write-back, and session lifecycle. Use for AuraDB-connected, self-managed Neo4j, or standalone DataFrame/Spark session workloads. Does NOT cover the embedded GDS plugin on Aura Pro or self-managed Neo4j — use neo4j-gds-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover Snowflake Graph Analytics — use neo4j-snowflake-graph-analytics-skill.

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