Skill UI

This workflow is designed for comprehensive industrial asset and location management within the MaintainX platform. It enables users to establish detailed facility location hierarchies, register complex equipment assets (including serial numbers and models), query asset lists, and initiate maintenance work orders. Use it for managing equipment lifecycles, tracking physical assets, and maintaining structured facility records.

This comprehensive skill manages the entire lifecycle of database testing, ensuring data integrity and system reliability. It covers setting up isolated test environments (using Docker or in-memory databases), executing migrations, and implementing robust test isolation strategies (such as transaction rollback). Use cases include verifying CRUD operations, validating referential constraints, performance benchmarking (EXPLAIN ANALYZE), and ensuring backward compatibility across multiple databases (PostgreSQL, MySQL, MongoDB) and ORMs.

This comprehensive playbook guides Series A+ B2B SaaS companies on international scaling. It covers the full demand generation lifecycle, from TOFU awareness campaigns using SEO and paid social (LinkedIn, Google, Meta), to MOFU lead nurturing, and BOFU pipeline conversion. It details core KPIs (CAC, MQL/SQL), multi-channel optimization, technical SEO best practices, and strategic partnership building for sustained growth.

This comprehensive skill guides the entire product marketing lifecycle, from defining Ideal Customer Profiles (ICP) and developing core positioning statements (e.g., Dunford's method) to executing Go-To-Market (GTM) strategies and product launch plans. It also covers competitive intelligence, building detailed battlecards, and improving sales enablement through win/loss analysis, ensuring successful market entry and adoption across international markets.

A comprehensive guide for solo founders and indie hackers to launch profitable micro-SaaS products quickly. Covers the entire lifecycle: idea validation frameworks, building a Minimum Viable Product (MVP) using modern solo-founder stacks, implementing effective pricing psychology, and executing high-impact launch playbooks. Ship a real business in weeks, not months.

This SDK provides Azure Functions extensions for handling Microsoft Entra ID custom authentication events within a .NET environment. Developers can intercept key stages of the authentication lifecycle, such as token issuance, attribute collection, and OTP sending. Use cases include enriching access tokens with custom claims, fetching external user data to populate profiles, and customizing the sign-up user experience.

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 for migrating existing AI integrations from providers like OpenAI or Anthropic to Mistral AI. This blueprint covers the entire lifecycle: initial assessment of AI touchpoints, detailed model mapping (e.g., gpt-4o to mistral-large), implementing a provider-agnostic adapter pattern, and executing controlled, feature-flag-controlled rollout plans for major refactoring projects.

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 skill provides comprehensive guidance for building production-grade MLOps pipelines. It covers the entire machine learning lifecycle, from initial data ingestion and rigorous feature engineering to model training, advanced validation (A/B testing, performance drift), and robust deployment strategies (Canary, Blue-green). It enables the design and orchestration of scalable, reproducible, and observable ML systems using tools like Airflow and Kubeflow.

Track experiments, catalog models, compare versions, and deploy trained artifacts with MLflow’s framework-agnostic lifecycle tools across PyTorch, TensorFlow, and other stacks.

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.