Skill UI

Automates guidance for log analysis security within advanced security workflows, offering best-practice patterns, production-ready snippets, and compliance validation when you mention log analysis security needs.

Implement robust, structured logging for API requests, capturing performance metrics, correlation IDs, and detailed security audit trails. This skill ensures compliance by handling PII redaction and providing comprehensive, JSON-formatted logs suitable for ELK Stack or Loki. Essential for debugging, performance analysis, and maintaining SOC 2 compliance in distributed microservices.

A comprehensive guide for diagnosing and fixing common Lokalise API errors and exceptions. This skill details the error response format and provides diagnostic cURL commands for specific HTTP status codes (401, 400, 404, 429, 413, 500, 503). Use it when encountering failed requests, debugging integration issues, or needing to verify API token validity and rate limit status.

A comprehensive tool designed to collect all necessary diagnostic evidence for troubleshooting Lokalise integration issues or filing support tickets. It gathers environment details (Node, Python, OS), tests API connectivity, lists project metrics, checks upload processes, and collects redacted logs, bundling everything into a single archive.

A structured playbook for rapidly diagnosing and resolving production incidents related to Lokalise localization services. It guides users through essential quick diagnostics (API health, token validity, rate limits) and provides a decision tree for common failure modes, such as missing or stale translations, API outages, and upload failures, ensuring swift mitigation and postmortem readiness.

This skill provides a comprehensive guide to setting up full observability for Lokalise integrations. It covers crucial metrics, traces, and alert configurations, monitoring everything from API response times and rate limit consumption to translation progress and webhook reliability. Ideal for developers building robust monitoring dashboards and alerting systems for localization workflows.

This skill provides advanced techniques for optimizing throughput and reducing latency when interacting with the Lokalise API. Learn to implement cursor-based pagination for massive key sets, establish robust local caching strategies using project activity timestamps, and utilize efficient batch operations (up to 500 items) for creating or updating keys. Essential for building scalable, high-performance translation pipelines.

This comprehensive script suite provides a structured, multi-step audit for internationalization (i18n) integrations using Lokalise. It automates crucial checks, including verifying translation coverage across all required locales, detecting key synchronization issues (missing or orphaned keys) between the source code and the Translation Management System (TMS), and validating the format of localized files. Use this utility before any major production deployment or go-live phase to ensure localization readiness.

This guide provides production-grade coding patterns for integrating Lokalise into TypeScript and Node.js applications. It covers crucial best practices such as client singleton implementation, robust cursor-based pagination for large datasets, structured error handling with retry logic, and efficient batch operations. Implementing these patterns ensures reliable, scalable, and maintainable API usage.

A comprehensive guide detailing security best practices for integrating with Lokalise. Covers strategies for managing API token scopes (read-only vs. read-write), enforcing the principle of least privilege, validating translated content for XSS or credential leakage, verifying webhook secrets, and securing CI/CD pipelines against hardcoded secrets. Essential for developers and DevOps engineers.

This comprehensive SDK provides tools for building enterprise-grade AI agents integrated across the Microsoft 365 ecosystem, including Teams and Copilot Studio. It supports advanced features such as Express hosting, structured routing, streaming responses via Azure OpenAI, and direct client integration, enabling developers to build sophisticated business automation solutions.

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.