Skill UI

This guide provides comprehensive strategies for minimizing operational costs within the MindTickle learning platform. It details cost drivers across various components, including seat licenses, content storage, quiz assessments, call recording analysis, and API integrations. Users can learn best practices for content deduplication, implementing incremental sync, setting proper retention policies, and optimizing resource usage to reduce unnecessary spend and improve overall platform efficiency for sales organizations.

This guide provides advanced techniques to optimize integration performance with the MindTickle API. It addresses common enterprise bottlenecks such as slow learner progress queries, rate limit errors, and high-volume webhook backpressure. Techniques covered include implementing Redis caching, using offset-based pagination for bulk data retrieval, managing concurrent requests with connection pooling, and applying exponential backoff for robust rate limit handling.

Programmatically manages advanced elements on Miro boards using the REST API v2. Functions include creating connectors, uploading images (from URL or Base64), embedding external content (like videos/URLs), managing custom App Cards, and adding documents. Ideal for building complex, automated, and visually rich workflows, architecture diagrams, and collaborative dashboards.

This tool collects comprehensive diagnostic evidence for troubleshooting Miro REST API v2 integration problems. It gathers environment details, performs connectivity tests (DNS, HTTPS), checks rate limits, and aggregates recent application logs into a single, structured bundle. Use this when preparing a detailed support ticket or diagnosing persistent API issues.

A comprehensive guide and working example demonstrating how to programmatically interact with the Miro REST API v2 using TypeScript. This skill covers the full board lifecycle, allowing users to create boards, add diverse items (sticky notes, shapes), connect elements with connectors, and list all board contents. Ideal for testing new Miro integrations or automating workflow processes.

This skill provides a comprehensive guide to setting up a robust local development workflow for building Miro integrations. It encompasses hot reloading for rapid iteration, structured unit and integration testing using Vitest, and configuring ngrok tunneling for simulating real-world webhooks and interacting with the Miro REST API v2.

A comprehensive guide for migrating complex Miro board data across teams and organizations. It covers exporting board content—including items, connectors, tags, and members—to structured JSON files, and bulk-importing data into Miro using the REST API v2. Ideal for re-platforming from competing whiteboard tools or updating large-scale, legacy v1 integrations.

This guide provides advanced best practices for optimizing interactions with the Miro REST API v2. It covers essential patterns like cursor-based pagination to prevent over-fetching, implementing robust caching strategies (In-Memory/Redis) for reduced latency, and managing connections and concurrency for reliable bulk operations. It is designed for building scalable, high-throughput, and efficient integrations into Miro.

A comprehensive guide for developers implementing secure integrations with the Miro REST API v2. This guide covers critical security best practices, including OAuth 2.0 scope minimization, robust token lifecycle management (refreshing tokens), verifying webhooks using signatures, and implementing client secret rotation protocols. Essential for building reliable and secure SaaS applications connected to Miro.

This comprehensive guide assists developers migrating their Miro integrations from REST API v1 to v2. It details key breaking changes, such as 'Widget' becoming 'Item' and 'Line' becoming 'Connector,' provides updated endpoint maps, and offers step-by-step instructions for upgrading the `@mirohq/miro-api` SDK and adapting request/response payloads. Use this resource when updating your integration to handle the latest Miro platform standards and data structures.

This comprehensive skill manages the entire computer vision pipeline, from custom dataset annotation to model deployment. Users can fine-tune YOLO models on proprietary COCO-formatted datasets using an agent-driven workflow. Key features include hardware-aware training (supporting CUDA, MPS, ROCm), automatic export to optimized formats (TensorRT, CoreML, OpenVINO, ONNX), and seamless, one-click deployment as an active detection skill.

This skill runs a comprehensive, sequenced pipeline (Patterns 1-5) to structurally analyze a monolithic codebase. It inventories components, detects domain duplication, resolves hierarchy issues, measures inter-module coupling, and groups components into candidate, domain-aligned services. It is essential for architects planning service extraction, providing actionable metrics and DDD insights before refactoring or splitting the application.