Skill UI

This comprehensive guide provides step-by-step instructions to optimize localization spending on Lokalise. Learn how to audit current usage across projects and languages, minimize per-seat costs by managing contributor access, maximize Translation Memory (TM) hits, and efficiently triage Machine Translation (MT) to keep your localization projects well within budget.

Automates guidance for setting up and maintaining long polling handlers within API integrations, covering best practices, code samples, validation, and webhook flow optimization.

This skill guides the design and implementation of a complete, production-grade Machine Learning pipeline. It employs a multi-agent orchestration workflow, systematically covering all stages of the ML lifecycle: data ingestion and quality assurance, feature engineering, model training, optimization, and robust deployment (MLOps). It ensures built-in reproducibility, monitoring, and scalability.

A comprehensive guide to minimizing API usage costs by implementing advanced integration patterns. Learn to reduce request volume through response caching with TTL, replacing expensive polling with webhook-driven sync, and utilizing smart polling with change detection filters. Also covers usage tracking and request deduplication for highly efficient system architecture.

Automatic assistant for Make scenario creator tasks, offering guidance on workflow automation, spreadsheets, email processing, and business process optimization aligned with best practices.

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 toolkit provides a comprehensive framework for managing and optimizing API costs when using Mistral AI. It includes functions for calculating token-based costs across various models, implementing a smart model router based on task complexity, and managing daily/monthly budgets with critical alerts. Furthermore, it offers methods for prompt optimization to reduce token consumption, making it essential for large-scale application cost control and billing analysis.

Mastery of modern mobile development across multiple platforms, including React Native, Flutter, and native iOS/Android. Expertise covers the entire lifecycle, specializing in architectural patterns (Clean, MVVM), performance optimization, offline-first data synchronization (GraphQL, SQLite), advanced DevOps practices (CI/CD, Fastlane), and App Store Optimization (ASO). Ideal for building robust, scalable, and highly performant cross-platform applications.

Automates guidance for model registry manager tasks within ML deployment, covering serving, MLOps pipelines, monitoring, and production optimization whenever that trigger phrase appears.

Provides automated guidance for managing model versions in ML deployment, including serving, monitoring, and production optimization whenever someone requests the model versioning manager.

This skill provides comprehensive guidance on training Mixture of Experts (MoE) models, including architectures like Mixtral and DeepSeek-V3. It details techniques for achieving large-scale model capacity with significantly reduced computational costs (e.g., 5x reduction vs. dense models). Topics covered include top-k routing, load balancing, expert parallelism, and integration with frameworks like DeepSpeed and HuggingFace for efficient, resource-constrained training.

Enables Claude to monitor and analyze application CPU usage, detect hotspots, inefficient loops and regexes, and offer optimization strategies such as asynchronous processing or algorithmic improvements to boost performance.