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Found 231 Skills
AWS Lambda, Vercel Edge Functions, Cloudflare Workers, cold starts, deployment patterns, and infrastructure as code (SST, Serverless Framework). Use when building serverless applications or optimizing function-based architectures.
Provides TypeScript patterns for DynamoDB-Toolbox v2 including schema/table/entity modeling, .build() command workflow, query/scan access patterns, batch and transaction operations, and single-table design with computed keys. Use when implementing type-safe DynamoDB access layers with DynamoDB-Toolbox v2 in TypeScript services or serverless applications.
How to choose and configure data sources for MapLibre GL JS — rendering your own data without tiles, hosted tile services, serverless PMTiles, self-hosted tile servers, tile schemas, glyphs, and sprites.
Official Reference Guide for the PPIO Platform, covering LLM API (OpenAI-compatible), Agent Sandbox, GPU (Instances and Serverless), integration, authentication, pricing, rate limiting, and troubleshooting. Suitable for common questions such as 'How to integrate PPIO in specific application scenarios?' and PPIO request failures.
Novita AI: LLM, Image Generation & Editing, Video Generation, Audio (TTS/ASR), and GPU Cloud. Use this skill whenever the user wants to call Novita AI APIs — chat with LLMs (DeepSeek, Llama, Qwen), generate images (FLUX, Stable Diffusion, Seedream, Hunyuan Image), edit images (remove background, upscale, inpainting, img2img, outpainting, reimagine, merge face, replace background, remove text), generate videos (Kling, Wan, Hunyuan, Minimax Hailuo, Vidu, PixVerse, Seedance), do text-to-speech or speech-to-text (MiniMax TTS, GLM TTS, Fish Audio, ASR, voice cloning), run OpenAI-compatible batch jobs, manage GPU cloud instances and serverless endpoints, or check account balance and billing. Also trigger when the user mentions novita.ai, Novita AI, Novita API key, or wants to use any Novita platform service — even if they just say "generate an image" or "run an LLM" and Novita is available as a provider.
TensorLake SDK for building agentic workflows, sandboxed code execution, and document parsing/extraction. Use when the user mentions tensorlake, or asks about TensorLake APIs/docs/capabilities. Also use when the user is building AI agents or agentic applications that need serverless workflow orchestration (parallel map/reduce DAGs), sandboxed execution of LLM-generated code, or document parsing, structured extraction, and OCR from PDFs/images. Works with any LLM provider (OpenAI, Anthropic), agent framework (LangChain, CrewAI, LlamaIndex), database, or API as the infrastructure layer.
Generates a Jupyter notebook that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, and RLVR trainers, including RLVR Lambda reward function creation.
Generates a Jupyter notebook that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Use when the user says "deploy my model", "create an endpoint", "make it available", or asks about deployment options. Identifies the correct deployment pathway (Nova vs OSS), generates deployment code, and handles endpoint configuration.
DataWorks Infrastructure Management: Create and query operations for Data Sources (51 types), Compute Resources, and Serverless Resource Groups, plus connectivity testing and resource group binding/unbinding. Uses aliyun CLI to call dataworks-public OpenAPI (2024-05-18). Trigger keywords: DataWorks data source, compute resource, resource group, datasource, data source, compute resource, resource group, mysql/hologres/maxcompute data source, holo/mc/flink resource, Serverless resource group, DataWorks infra, create/list datasource, DW environment config, infrastructure initialization, connect database to DataWorks, database connection failure, configure holo/mc resource. Not triggered: data development tasks, scheduling configuration, MaxCompute table management, data integration tasks, ECS/RDS/OSS operations, workspace member management, data quality monitoring, data lineage, data preview.
Microservice architecture patterns — service decomposition, inter-service communication, API gateway, saga pattern, event-driven architecture, service mesh, circuit breaker, CQRS, event sourcing. Activate on "microservices", "service decomposition", "saga pattern", "API gateway", "event-driven", "service mesh", "circuit breaker", "CQRS", "event sourcing", "bounded context", "strangler fig", "distributed transactions", "choreography vs orchestration". NOT for monolith design, serverless functions, or Kubernetes infrastructure.
Deployment and hosting platform specialist covering Vercel, Railway, and Convex. Use when deploying applications, configuring edge functions, setting up continuous deployment, managing serverless infrastructure, containerized deployments, real-time backends, or choosing deployment platforms. Covers edge computing (Vercel), container orchestration (Railway), and reactive backends (Convex).
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.