Skill UI

This skill facilitates an interactive Quality Assurance (QA) session. Users can conversationally describe encountered bugs or issues. The agent clarifies the problem, explores the codebase for context and domain language, and then meticulously files durable GitHub issues. It supports both single-issue reports and breaking down complex problems into multiple, independently verifiable tickets, ensuring developers receive actionable, high-quality reports written from the user's perspective.

A comprehensive guide for diagnosing and reducing high memory consumption in Qdrant. This resource is crucial when dealing with reports of 'memory leak', 'out of memory', or excessive RAM growth. It details how Qdrant manages resident memory and OS page cache, and provides advanced techniques such as quantization, using float16/int8 data types, and strategically storing components on disk (like HNSW indexes or sparse vectors) to minimize the memory footprint and ensure stable, scalable operation.

This mechanism is designed to optimize Qdrant's performance when handling queries with very large limits or when paginating results across multiple shards. Instead of forcing every shard to transfer the full requested limit (which is bandwidth-intensive), it uses Poisson distribution statistics to calculate smaller, optimized limits for each shard. This significantly reduces inter-shard data transfer and improves the efficiency of large-scale vector search, while maintaining a very high degree of accuracy.

qzcli is a powerful command-line interface (CLI) tool designed for managing complex GPU compute jobs on the Qizhi platform. Modeled after industry standards like kubectl, it allows users to log in, discover available resources (nodes, workspaces), submit interactive or highly complex batch jobs (including matrix sweeps), and monitor job status efficiently. It is essential for handling high-performance computing (HPC) and distributed machine learning training workflows.

This skill provides the comprehensive reference architecture and implementation patterns for integrating Runway's advanced AI capabilities. It focuses on guiding developers through the process of AI video generation, image-to-video conversion, and creative content creation using the dedicated Python SDK. Use this when building applications that require high-quality, generative visual media.

This guide helps users select and implement the optimal architecture pattern for Salesforce integrations. It compares three main approaches: simple Direct API polling, highly scalable Event-Driven using Change Data Capture (CDC), and complex Enterprise Middleware/iPaaS solutions. Use this when designing new integrations, planning data migration, or determining the best sync method based on data volume, latency, and complexity requirements.

This skill provides advanced methods for interacting with Salesforce, focusing on high-volume data processing. It covers using Bulk API 2.0 for mass data ingestion, querying, and exporting millions of records. Furthermore, it demonstrates the Composite API for executing complex, multi-step, and dependent transactions (e.g., creating Account, then referencing it to create Contact) in a single, atomic call, ensuring data integrity and maximum efficiency.

This guide provides comprehensive methods for load testing and capacity planning specifically for Salesforce integrations. It walks users through calculating API limit budgets, running high-volume performance tests using tools like k6, and optimizing Bulk API throughput. Use this when scaling complex, high-volume, or mission-critical Salesforce processes.

Master advanced techniques for optimizing Salesforce API interactions. This guide covers optimizing SOQL queries using selective fields and indexed WHERE clauses. It details methods to minimize API calls by utilizing Collections and Composite APIs, implementing metadata caching, and efficiently streaming large result sets via Bulk API 2.0, ensuring stable and high-performance integrations.

A comprehensive guide and implementation pattern for handling Salesforce API quotas and rate limits. Learn how to monitor daily usage, implement exponential backoff with jitter for transient errors (like REQUEST_LIMIT_EXCEEDED), and optimize API calls using bulk operations, caching, and queryMore to ensure stable, high-throughput integrations.

This guide details how to manage and reduce the high rate-limit costs associated with the SalesLoft API. It explains the cost model, emphasizing that deep pagination significantly increases consumption. Learn advanced strategies like incremental syncing, caching frequently accessed data, implementing webhooks, and request consolidation to maintain efficiency and stay within budget limits.

This guide provides advanced techniques to optimize interactions with the SalesLoft REST API v2. Key strategies include implementing LRU caching for frequently accessed data, utilizing incremental sync with `updated_at` filtering to avoid costly deep pagination, configuring connection pooling for stability, and running parallel reads safely. Use this when experiencing high latency, managing large datasets, or minimizing API usage costs for bulk operations.