Skill UI

A comprehensive guide providing step-by-step instructions for upgrading Langfuse SDK versions (v3 to v5) and migrating between major API changes. It covers best practices for both TypeScript/JavaScript and Python implementations, focusing on adopting OpenTelemetry standards to ensure seamless LLM observability integration.

This comprehensive guide outlines a multi-step process for safely upgrading the Linear SDK across major versions. It details how to check for breaking changes (like renamed fields or changed return types), implement compatibility layers using patterns like the Bridge pattern in TypeScript, run thorough unit and integration tests, and establish a robust deployment and rollback plan, ensuring zero downtime migration.

Automated Linux Commands Guide skill offers step-by-step help with foundational DevOps tasks such as version control, containerization, CI/CD, and infrastructure routines whenever you mention linux commands guide.

This skill provides comprehensive handling for Lokalise translation data, covering the entire localization lifecycle. It facilitates creating keys with rich metadata, managing versions using immutable snapshots, isolating work with feature branches, and generating various export formats (JSON, XLIFF). It is essential for handling sensitive translation data, ensuring PII compliance, and managing scalable internationalization within development pipelines.

A comprehensive guide for developers upgrading the Lokalise Node.js SDK across major versions (e.g., v8 CommonJS to v9 ESM). This skill detects and resolves breaking changes, including module system shifts (require to import), updating package configurations, and modernizing pagination from offset to cursor-based methods, ensuring smooth API integration.

This specialized skill provides a comprehensive framework for migrating applications using the MaintainX API across different versions (e.g., v1 to v2). It covers best practices for handling API breaking changes, deprecations, and version compatibility. Users can audit existing API usage, implement version adapters, and manage feature flag rollouts, ensuring smooth and safe integration upgrades.

Auto-activating DevOps skill that walks through makefile generator requests, offering best-practice guidance, production-ready snippets, and standard validations for foundational version control, containerization, and CI/CD workflows.

Manages and automates deployment rollbacks with safety checks, ensuring rapid recovery from failed releases by reverting to known-good versions and verifying system health.

Memory provides persistent context across multiple coding sessions. It stores crucial project decisions, user preferences (like preferred package managers or specific libraries), and architectural conventions in a local memory file. This ensures that the AI maintains consistency, recalls past choices automatically, and applies project-specific rules without needing constant reminders, significantly improving workflow continuity and reliability.

This skill outlines a comprehensive, low-downtime process for migrating APIs between different versions, frameworks, or platforms. It guides the implementation of advanced architectural patterns like the Strangler Fig pattern, traffic shadowing, and canary deployments. Use this when upgrading a monolithic API to microservices or transitioning between incompatible API versions.

This comprehensive skill guides developers through major version upgrades of the Mistral AI SDK. It specifically detects breaking changes, such as the shift from CommonJS to ESM, and provides detailed steps, automated code transformation scripts, and best practices for migrating existing code. Use this when jumping major SDK versions or refactoring complex Mistral integrations to ensure a smooth transition to the latest API.

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.