Skill UI

Guide for automating Mboum toolkit tasks through Rube MCP and Composio, emphasizing pre-flight tool discovery, connection checks, and schema-compliant execution using the provided workflow pattern.

Automated memcached configuration assistant that provides best-practice guidance and production-ready snippets for caching, database, messaging, and microservices patterns in backend services.

This skill provides a comprehensive set of cross-language patterns and best practices for achieving memory-safe systems programming. It covers fundamental concepts such as RAII, ownership models, smart pointers, and robust resource management. Use this guide when writing low-level or systems code, managing critical resources (files, sockets, memory), or debugging complex memory issues to prevent use-after-free errors and memory leaks.

This skill provides a comprehensive guide to mastering microservices architecture patterns. It covers critical areas such as service boundary identification, inter-service communication protocols, distributed data management, and implementing resilience patterns. Use this when decomposing large monolithic applications or designing complex, highly available distributed systems.

Provides comprehensive methodologies for planning, executing, and validating complex, zero-downtime system migrations. This skill covers critical patterns across data (Schema Evolution, CDC), services (Strangler Fig, Canary), and infrastructure (On-Prem to Cloud). It ensures minimal business impact, data consistency, and reliable rollback strategies.

Automates migration guide creation by providing step-by-step documentation patterns, best practices, and validated snippets for APIs, user guides, and architecture docs.

Provides a quick reference for diagnosing and fixing common Mistral AI errors, including authentication issues, rate limits, invalid parameters, payload limits, service outages, and timeouts, with concrete logging checks, retry logic, and remediation code snippets.

Provides a minimal Mistral AI chat completion example in TypeScript and Python for bootstrapping a new integration, validating your setup, and experimenting with streaming responses.

Guides teams through migrating major integrations from OpenAI or other providers to Mistral AI, covering assessment, provider-agnostic adapters, feature-flag rollouts, model mapping, validation tests, and rollback strategies.

Guides teams on reducing latency and improving throughput when integrating Mistral AI by selecting low-latency models, enabling streaming, caching deterministic requests, trimming prompts, and managing request concurrency.

Provides a step-by-step checklist for credential validation, code quality, health probes, circuit breaker safeguards, monitoring, gradual rollout, verification, and rollback when deploying Mistral AI integrations to production.

Provides code for tracking per-minute request and token limits with rate limiter, retry/backoff loops, model routing, and batching to keep Mistral AI usage within tiers and avoid 429 errors.