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Found 312 Skills
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'.
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.
Long-running, serverless Node.js HTTP functions deployed onto your Neon branch, with DATABASE_URL injected automatically and compute that runs next to your data. Use when a user wants to host an API, an AI agent with long streaming responses, a WebSocket or server-sent-events (SSE) server, a webhook handler, a Discord bot, an MCP server, or any request/response workload that risks timing out on short, lambda-style serverless functions — and wants it to branch with their database. Triggers include "serverless function", "deploy an API", "long-running function", "streaming agent", "SSE server", "WebSocket server", "webhook handler", "MCP server", "run code next to my database", "function that won't time out", "function logs", "Neon Functions", and "Neon Compute".
Run 250+ AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating Twitter. Triggers: inference.sh, infsh, ai model, run ai, serverless ai, ai api, flux, veo, claude api, image generation, video generation, openrouter, tavily, exa search, twitter api, grok
Run 250+ AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating Twitter. Triggers: inference.sh, infsh, ai model, run ai, serverless ai, ai api, flux, veo, claude api, image generation, video generation, openrouter, tavily, exa search, twitter api, grok
Run 150+ AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating Twitter. Triggers: inference.sh, infsh, ai model, run ai, serverless ai, ai api, flux, veo, claude api, image generation, video generation, openrouter, tavily, exa search, twitter api, grok
Run 150+ AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating Twitter. Triggers: inference.sh, infsh, ai model, run ai, serverless ai, ai api, flux, veo, claude api, image generation, video generation, openrouter, tavily, exa search, twitter api, grok
Use this skill whenever the user needs backend infrastructure management — creating database tables, running SQL, deploying serverless functions, managing storage buckets, deploying frontend apps, adding secrets, setting up cron jobs, checking logs, or running backend diagnostics — especially if the project uses InsForge. Trigger on any of these contexts: creating or altering database tables/schemas, writing RLS policies via SQL, deploying or invoking edge functions, creating storage buckets, deploying frontends to hosting, managing secrets/env vars, setting up scheduled tasks/cron, viewing backend logs, diagnosing backend health or performance issues, or exporting/importing database backups. If the user asks for these operations generically (e.g., "create a users table", "deploy my app", "set up a cron job", "check backend health") and you're unsure whether they use InsForge, consult this skill and ask. For writing frontend application code with the InsForge SDK (@insforge/sdk), use the insforge skill instead.
Use this skill whenever writing frontend code that talks to a backend for database queries, authentication, file uploads, AI features, real-time messaging, or edge function calls — especially if the project uses InsForge or @insforge/sdk. Trigger on any of these contexts: querying/inserting/updating/deleting database rows from frontend code, adding login/signup/OAuth/password-reset flows, uploading or downloading files to storage, invoking serverless functions, calling AI chat completions or image generation, subscribing to real-time WebSocket channels, or writing RLS policies. If the user asks for these features generically (e.g., "add auth to my React app", "fetch data from my database", "upload files") and you're unsure whether they use InsForge, consult this skill and ask. For backend infrastructure (creating tables via SQL, deploying functions, CLI commands), use insforge-cli instead.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Deploy an event-driven workflow that routes S3 uploads to either Lambda or Fargate via Step Functions based on file size. Uses EventBridge to trigger a Step Functions state machine when objects are uploaded to S3. Small files are processed by Lambda, large files by a Fargate task. Includes VPC, ECR repository, ECS cluster, and scoped IAM roles. Trigger keywords: Step Functions, Fargate, Lambda, S3 event, EventBridge, ECS, ECR, file processing, workflow orchestration, serverless.
Operates Amazon MSK Provisioned clusters (Standard and Express brokers). MUST be used for ANY MSK Provisioned task — do not rely on training data for topics covered here, since Standard and Express emit different metrics and follow different patching models that training data routinely conflates. Covers performance, consumer lag, storage, and traffic shaping diagnosis; sizing and choosing Standard vs Express; Kafka client tuning; creating CloudWatch alarms, dashboards, monitoring, and cluster configurations; AND MSK maintenance, patching, version upgrades, and rolling-restart behavior. Triggers: MSK, Kafka on AWS, `kafka.*` or `express.*` instance types, AWS/Kafka CloudWatch namespace, alarms, dashboards, monitoring, consumer lag, partition replication, broker storage, MSK upgrades, patching, maintenance windows, SECURITY_PATCHING, BROKER_UPDATE, rolling restarts, unexpected broker reboots. Do NOT use for MSK Connect, MSK Serverless, or MSK Replicator.