Optimize Perplexity API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Perplexity integrations. Trigger with phrases like "perplexity performance", "optimize perplexity", "perplexity latency", "perplexity caching", "perplexity slow", "perplexity batch".
Build asynchronous video generation workflows with Kling AI. Use when integrating video generation into larger systems or pipelines. Trigger with phrases like 'klingai async', 'kling ai workflow', 'klingai pipeline', 'async video generation'.
Diagnose and fix Retell AI common errors and exceptions. Use when encountering Retell AI errors, debugging failed requests, or troubleshooting integration issues. Trigger with phrases like "retellai error", "fix retellai", "retellai not working", "debug retellai".
Process multiple video generation requests efficiently with Kling AI. Use when generating multiple videos or building content pipelines. Trigger with phrases like 'klingai batch', 'kling ai bulk', 'multiple videos klingai', 'klingai parallel generation'.
Optimize Retell AI costs through tier selection, sampling, and usage monitoring. Use when analyzing Retell AI billing, reducing API costs, or implementing usage monitoring and budget alerts. Trigger with phrases like "retellai cost", "retellai billing", "reduce retellai costs", "retellai pricing", "retellai expensive", "retellai budget".
Execute extend video duration using Kling AI continuation features. Use when creating longer videos from shorter clips or building seamless sequences. Trigger with phrases like 'klingai extend video', 'kling ai video continuation', 'klingai longer video', 'extend klingai clip'.
Generate videos from text prompts with Kling AI. Use when creating videos from descriptions or learning prompt techniques. Trigger with phrases like 'kling ai text to video', 'klingai prompt', 'generate video from text', 'text2video kling'.
Optimize Retell AI API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Retell AI integrations. Trigger with phrases like "retellai performance", "optimize retellai", "retellai latency", "retellai caching", "retellai slow", "retellai batch".
Create a minimal working Retell AI example. Use when starting a new Retell AI integration, testing your setup, or learning basic Retell AI API patterns. Trigger with phrases like "retellai hello world", "retellai example", "retellai quick start", "simple retellai code".
Apply production-ready Retell AI SDK patterns for TypeScript and Python. Use when implementing Retell AI integrations, refactoring SDK usage, or establishing team coding standards for Retell AI. Trigger with phrases like "retellai SDK patterns", "retellai best practices", "retellai code patterns", "idiomatic retellai".
Implement speech-to-text transcription workflow with Deepgram. Use when building pre-recorded audio transcription, batch processing, or implementing core transcription features. Trigger with phrases like "deepgram transcription", "speech to text", "transcribe audio", "audio transcription workflow", "batch transcription".
Execute integrate Kling AI video output with cloud storage providers. Use when storing generated videos in S3, GCS, or Azure Blob. Trigger with phrases like 'klingai storage', 'save klingai video', 'kling ai cloud storage', 'klingai s3 upload'.
Build and deploy production-ready generative AI agents using Vertex AI, Gemini models, and Google Cloud infrastructure with RAG, function calling, and multi-modal capabilities
Create dataset loader creator operations. Auto-activating skill for ML Training. Triggers on: dataset loader creator, dataset loader creator Part of the ML Training skill category. Use when working with dataset loader creator functionality. Trigger with phrases like "dataset loader creator", "dataset creator", "dataset".
Configure Retell AI across development, staging, and production environments. Use when setting up multi-environment deployments, configuring per-environment secrets, or implementing environment-specific Retell AI configurations. Trigger with phrases like "retellai environments", "retellai staging", "retellai dev prod", "retellai environment setup", "retellai config by env".
Analyze, plan, and execute Groq SDK upgrades with breaking change detection. Use when upgrading Groq SDK versions, detecting deprecations, or migrating to new API versions. Trigger with phrases like "upgrade groq", "groq migration", "groq breaking changes", "update groq SDK", "analyze groq version".
Configure custom keyboard shortcuts for Cascade and AI features. Activate when users mention "keyboard shortcuts", "keybindings", "hotkeys", "shortcut configuration", or "customize shortcuts". Handles keybinding setup and optimization. Use when working with windsurf keyboard shortcuts functionality. Trigger with phrases like "windsurf keyboard shortcuts", "windsurf shortcuts", "windsurf".
Implement Vast.ai rate limiting, backoff, and idempotency patterns. Use when handling rate limit errors, implementing retry logic, or optimizing API request throughput for Vast.ai. Trigger with phrases like "vastai rate limit", "vastai throttling", "vastai 429", "vastai retry", "vastai backoff".
Build production-ready AI agents using Google's Agent Development Kit with Claude integration, React patterns, multi-agent orchestration, and comprehensive tool libraries
Apply production-ready LangChain SDK patterns for chains, agents, and memory. Use when implementing LangChain integrations, refactoring code, or establishing team coding standards for LangChain applications. Trigger with phrases like "langchain SDK patterns", "langchain best practices", "langchain code patterns", "idiomatic langchain", "langchain architecture".
Create a minimal working Vast.ai example. Use when starting a new Vast.ai integration, testing your setup, or learning basic Vast.ai API patterns. Trigger with phrases like "vastai hello world", "vastai example", "vastai quick start", "simple vastai code".
Create a minimal working Groq example. Use when starting a new Groq integration, testing your setup, or learning basic Groq API patterns. Trigger with phrases like "groq hello world", "groq example", "groq quick start", "simple groq code".
Implement function/tool calling with OpenRouter models. Use when building agents or structured outputs. Trigger with phrases like 'openrouter functions', 'openrouter tools', 'openrouter agent', 'function calling'.
This skill optimizes prompts for Large Language Models (LLMs) to reduce token usage, lower costs, and improve performance. It analyzes the prompt, identifies areas for simplification and redundancy removal, and rewrites the prompt to be more concise and effective. It is used when the user wants to reduce LLM costs, improve response speed, or enhance the quality of LLM outputs by optimizing the prompt. Trigger terms include "optimize prompt", "reduce LLM cost", "improve prompt performance", "rewrite prompt", "prompt optimization".
