Skill UI

This skill provides a comprehensive pattern for optimizing bulk API interactions. It enables efficient processing of large arrays of operations by supporting both synchronous (for small batches) and asynchronous (for large batches) execution. Key features include concurrency control, robust partial failure handling, progress tracking via background workers, and dedicated validation steps, ensuring reliable data throughput for high-volume requests.

Enables Claude to invoke the computer-vision-processor plugin, auto-generating Python calls to run object detection, classification, and segmentation on user-provided images for detailed analysis.

A comprehensive toolkit designed for modern product managers covering the entire product lifecycle. It provides essential frameworks for feature prioritization (RICE scoring), conducting deep customer discovery interviews, structuring detailed Product Requirement Documents (PRDs), and developing strategic product roadmaps. Use this guide for robust feature validation, user research synthesis, and cross-functional strategy development.

This skill specializes in designing, optimizing, and evaluating prompts for Large Language Models (LLMs). It allows users to generate optimized prompt templates, structured output schemas (e.g., JSON mode, function calling), and comprehensive test suites. Use this skill when building new LLM applications, implementing complex reasoning patterns like Chain-of-Thought, defining system guardrails, or building robust evaluation frameworks to measure and improve model performance.

Provides automated guidance and production-ready code for property-based test helper tasks, covering unit, integration, and mocking scenarios within Test Automation workflows.

This skill provides comprehensive methodologies for capturing, analyzing, and documenting network protocols. It is crucial for security researchers, developers ensuring interoperability, and engineers debugging complex network communications by understanding the underlying data exchange mechanisms.

Provides prioritized performance optimization recommendations across frontend, backend, and infrastructure, offering bottleneck analysis, impact estimates, and phased implementation roadmaps when users request speed improvements or reviews.

This skill provides guidance and patterns for generating pairwise test cases. Pairwise testing is an advanced testing technique used in software quality assurance (QA) to ensure that every combination of two input parameters is tested. It is highly effective for complex systems with multiple interacting variables, helping developers improve test coverage efficiently while minimizing the overall number of required test cases.

A comprehensive guide for profiling, analyzing, and optimizing Python code. It addresses identifying performance bottlenecks related to CPU usage, excessive memory consumption, I/O operations, and slow data processing. Use this skill to improve application speed and efficiency in production environments.

Provides a comprehensive pytest-focused testing strategy for Python applications, covering TDD cycles, fixtures, parametrization, coverage tools, and test infrastructure setup for reliable suites.

This comprehensive guide provides robust strategies for testing Python code using industry best practices. It covers essential techniques like pytest, fixtures, mocking, parameterization, and implementing Test-Driven Development (TDD). Use this skill when writing unit tests, setting up complex test suites, performing integration testing, or establishing continuous testing infrastructure in CI/CD pipelines.

Qdrant is a high-performance, Rust-powered vector database designed for production-grade RAG (Retrieval-Augmented Generation) and semantic search applications. It provides fast nearest neighbor search, supports complex hybrid search using metadata filtering, and offers horizontal scalability through sharding and replication. Ideal for building real-time recommendation systems and enterprise-level knowledge retrieval.