Skill UI

Guide to architect and deploy Firebase projects that securely call Vertex AI/Gemini from Cloud Functions, covering Auth, Firestore, Hosting, observability, and ops guardrails for production readiness.

Guides building production Firebase Genkit apps with RAG flows, multi-step tooling, schema validation, and deployment via Firebase Functions or Cloud Run, including AI monitoring and error-handling best practices.

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.

Automates Google Cloud Vision workflows by discovering tools, managing the Composio Rube MCP connection, and executing schema-compliant operations via RUBE_MULTI_EXECUTE_TOOL.

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.

GroqCloud Automation orchestrates high-performance GroqCloud APIs through Composio, covering inference, chat completions, audio translation, and TTS voice selection for production workflows.

Execute complex and compute-intensive workloads directly on fully managed Hugging Face cloud infrastructure. This skill eliminates the need for local setup, allowing users to run jobs using cloud CPUs, GPUs, or TPUs. Ideal for large-scale data processing, batch inference, reproducible model training, and scheduled automation tasks, while persisting results to the Hugging Face Hub.

Playbook for running ML training and inference on Lambda Labs’ on-demand GPU cloud, covering when to choose it, console/SSH/API workflows, available GPUs, pricing, and persistent storage with Lambda Stack pre-installed.

Guides production deployment of LangChain applications via LangServe, Docker, Cloud Run, and LangSmith observability, covering containerization, API setup, and health checks for reliable production infrastructure.

A comprehensive MLOps engineer skill that covers building scalable ML infrastructure, pipelines, experiment tracking, registries, and cloud-native deployments across AWS, Azure, and GCP with best practices for reliable automation.

Guides deploying Perplexity-powered apps to Vercel, Fly.io, or Cloud Run, covering secret setup, streaming search API handlers, caching with Redis, and health checks for production releases.

Comprehensive skill covering the full lifecycle of visual AI systems. Expertise includes object detection (YOLO, Faster R-CNN), instance and semantic segmentation (SAM, Mask R-CNN), and video analysis. The workflow guides users through dataset preparation, selecting optimal architectures, training using frameworks like PyTorch, and achieving production readiness via model optimization (ONNX, TensorRT) and deployment on various edge or cloud targets.