Skill UI

This guide details the process of migrating search and LLM functionalities from legacy APIs (Google CSE, Bing, SerpAPI) to Perplexity Sonar. It demonstrates how to utilize the Strangler Fig pattern to build a robust adapter layer. The primary benefit is consolidating a complex, multi-step pipeline (Search -> Raw Results -> LLM Summarization) into a single, efficient API call, ensuring automatic citation handling and streamlined development.

This guide provides advanced architectural patterns to optimize interactions with the Perplexity Sonar API. By implementing smart model routing, query hashing with time-to-live (TTL) caching, streaming responses, and rate-limited parallel processing, developers can significantly reduce latency, improve overall throughput, and manage API costs. Ideal for integrating complex, research-intensive search-augmented applications.

This skill provides a rigorous framework for tackling complex software development tasks. It guides users to break down large features, refactoring efforts, or bug fixes into small, actionable, and independently verifiable steps. By focusing on clear outcomes, dependencies, and specific verification criteria, it ensures that the implementation process is structured, manageable, and traceable from start to finish.

This comprehensive guide outlines the process of migrating analytics data from major platforms like Google Analytics, Mixpanel, Amplitude, or Segment to PostHog. It covers implementing a dual-write strategy, mapping historical event names and properties, and performing bulk historical data imports, ensuring a smooth transition and data continuity.

This skill builds and executes automated pipelines to clean, validate, and transform data for machine learning, covering ETL workflows, error handling, and quality reporting so Claude can deliver analysis-ready datasets.

Auto-activating skill that delivers step-by-step guidance, best-practice validation, and production-ready code for crafting process flow visuals, diagrams, and documentation whenever you invoke the process flow generator.

Automate Process Street operations by discovering Rube MCP tools, managing connections, and executing workflows per the current schemas before every run.

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 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.

A comprehensive guide for designing and managing document control systems for ISO 13485-compliant Quality Management Systems (QMS), specifically tailored for the medical device industry. This resource covers document numbering, version control, change management workflows, approval processes, and ensures compliance with rigorous standards like 21 CFR Part 11, including electronic signatures and audit trails.

Expert in designing and implementing advanced Retrieval-Augmented Generation (RAG) pipelines. This role focuses on optimizing the entire information retrieval process—from document chunking and embedding model selection to vector database architectures and hybrid search strategies. The goal is to bridge the gap between raw knowledge documents and accurate LLM understanding, drastically reducing hallucination and enhancing context relevance for enterprise applications.