Skill UI

Integrates Apple's FoundationModels into iOS apps so developers can run on-device LLMs for text generation, guided structured outputs via @Generable, custom tool calls, and snapshot streaming without leaving the device.

Automate Composio Gamma workflows by querying Rube MCP for current tool schemas, managing Gamma connections, and executing schema-compliant tool calls with session-aware workflows.

Ensures Gamma integrations respect privacy by implementing consent gates, PII sanitization, retention schedules, GDPR request handling, and audit trails so teams can deploy compliant workflows.

Minimal Gamma integration sample showing how to initialize the SDK, create a simple three-slide presentation, and handle common setup errors; ideal for smoke testing new Gamma projects or learning basics.

A comprehensive guide to building enterprise-grade AI applications using Firebase Genkit. This skill covers the full development lifecycle, including implementing advanced RAG systems, multi-step tool-calling agents, and complex workflows across Node.js, Python, and Go. It teaches best practices for schema validation (Zod/Pydantic), robust error handling, OpenTelemetry tracing, and deploying reliably to Firebase Functions or Cloud Run.

Master Google Cloud’s Agent Development Kit and Starter Pack to build containerized multi-agent systems, automate RAG workflows, and deploy production-ready agents via Cloud Run, GKE, Agent Engine, or CI/CD pipelines.

Automate Google Photos workflows through Rube MCP (Composio) to list and manage albums, upload or batch-create media, search items, and organize albums using the provided toolkit tools and schema-aware commands.

GPTQ enables post-training 4-bit quantization for large LLMs, delivering up to 4× memory reduction and 3-4× faster inference with under 2% perplexity loss on consumer GPUs through AutoGPTQ, PEFT, and Transformer integrations.

Automates GPU resource optimization for ML deployments by guiding best practices, generating production-ready configurations, and validating setups for model serving, MLOps pipelines, and monitoring.

Guide to reducing Groq inference spend by routing requests to cost-effective models, trimming token usage, caching repeated calls, batching requests, and setting spend limits for Groq Cloud billing scenarios.

Guides Groq integrations through prompt sanitization, response filtering, token tracking, and audit logging so teams can safely handle PII, enforce retention policies, and keep GDPR/CCPA privacy guarantees.

Defines best-practice Groq deployment with tiered model routing, middleware, streaming pipelines, and fallback chains for ultra-fast LLM inference and production monitoring when launching new Groq integrations.