Skill UI

Automates Microsoft Tenant operations through Composio's toolkit by orchestrating Rube MCP tool discovery, connection validation, and schema-aware executions for reliable tenant management workflows.

Guides production-grade miles RL training for massive MoE models, covering FP8/INT4 low precision, train-inference alignment, and speculative rollout workflows to maximize throughput in enterprise deployments.

Automate Minerstat workflows through Composio’s Minerstat toolkit by orchestrating Rube MCP commands, covering discovery, connection checks, execution, and pagination-safe patterns for reliable operations.

Automate Missive operations by querying Composio’s Rube MCP toolkit, ensuring you search for current schemas first, maintain active connections, and execute workflows via RUBE_MULTI_EXECUTE_TOOL with schema-compliant arguments and shared session IDs.

This comprehensive workflow guide covers all production-ready patterns for Mistral AI chat completions. It demonstrates critical advanced features including multi-turn conversation management, real-time streaming responses, structured JSON output using schemas, and implementing robust guardrails and moderation. Ideal for building scalable, production-grade conversational AI applications and advanced chat interfaces.

A comprehensive guide to establishing a fast, reproducible local development lifecycle for Mistral AI integrations. This workflow includes setting up project scaffolding, implementing hot reloading with tsx, performing unit tests with Vitest mocking, and executing live integration tests against the Mistral API. Ideal for setting up reliable AI application development environments.

A comprehensive guide to implementing advanced asynchronous patterns with Mistral AI. This skill covers using the Agents API for stateful, multi-turn workflows, the Batch API for cost-effective bulk inference, and event-driven streaming architectures. It is essential for building robust, long-running, or high-volume AI applications that require reliable background job handling.

Mastering the full lifecycle of machine learning models from data ingestion to deployment. This expertise covers building robust, production-grade MLOps pipelines using orchestrators like Kubeflow, Airflow, or Prefect. Key skills include feature store implementation with Feast, rigorous data validation, experiment tracking (e.g., MLflow), model registry management, and automated deployment workflows. Essential for automating model training and ensuring reproducibility at scale.

This comprehensive guide covers the entire MLOps lifecycle, specializing in building robust, scalable, and production-grade Machine Learning systems. Expertise includes orchestrating complex ML pipelines using Kubeflow, Airflow, Prefect, and cloud-native services (AWS SageMaker, Azure ML, GCP Vertex AI). It covers experiment tracking (MLflow, W&B), model versioning, model registries, infrastructure as code (Terraform), and container deployment using Kubernetes. Ideal for engineers tasked with moving ML models from research to reliable, managed production environments.

Automates Mocean operations through Composio’s Mocean toolkit via Rube MCP, enforcing a workflow that always queries available tools, verifies Mocean connections, reuses sessions, and includes explicit memory placeholders before executing multi-tool plans.

Automates Moneybird workflows by always discovering Rube MCP tools first, managing the toolkit connection, and executing schema-compliant tool calls via Composio.

An expert guide for designing, setting up, and optimizing large-scale monorepositories. Masters advanced build systems like Nx, Turborepo, Bazel, and Lerna to manage complex dependency graphs, implement efficient build caching (local and remote), and optimize slow CI/CD pipelines for multi-project development workflows.