Use this agent when working with D3.js visualizations for network traffic analysis, including: creating new visualizations from session data, optimizing performance of existing D3.js charts, improving visual clarity and interactivity, debugging rendering issues, implementing advanced D3.js patterns for time-series or flow-based network data, or enhancing user experience in network traffic dashboards.\n\nExamples:\n- User: 'I need to visualize 10,000 network sessions with source/destination IPs, timestamps, and packet counts'\n Assistant: 'I'm going to use the d3-network-viz-optimizer agent to design an appropriate D3.js visualization architecture for your network session data.'\n- User: 'My D3.js force-directed graph is lagging with more than 1000 nodes'\n Assistant: 'Let me use the d3-network-viz-optimizer agent to analyze and optimize the performance of your force-directed network visualization.'\n- User: 'How can I add time-based filtering to my network flow diagram?'\n Assistant: 'I'll use the d3-network-viz-optimizer agent to implement interactive time-based filtering for your network flow visualization.'\n- Context: User has just written a basic D3.js scatter plot for network latency data\n User: 'Here's my initial implementation...'\n Assistant: 'Now let me use the d3-network-viz-optimizer agent to review and suggest improvements for your network latency visualization.
Use this agent when you need to design, implement, or optimize backend systems with AI components, resolve backend errors, ensure AI valuation accuracy, optimize performance, or improve loading times. This includes database optimization, API design, AI model integration, caching strategies, and performance bottlenecks. <example>Context: The user has implemented an AI valuation feature but it's running slowly and producing inconsistent results. user: 'The property valuation AI is taking 30 seconds to respond and the estimates vary wildly for the same property' assistant: 'I'll use the backend-ai-optimizer agent to analyze and fix these backend performance and accuracy issues' <commentary>Since this involves backend performance optimization and AI accuracy issues, the backend-ai-optimizer agent is the right choice to diagnose and resolve these problems.</commentary></example> <example>Context: The user is experiencing slow page loads and backend errors in their application. user: 'Users are complaining about slow loading times and I'm seeing 500 errors in the logs' assistant: 'Let me invoke the backend-ai-optimizer agent to investigate and resolve these backend performance issues' <commentary>Backend errors and performance issues fall directly within the backend-ai-optimizer's expertise.</commentary></example>
Use this agent when you need to analyze code for performance bottlenecks, optimize resource usage, review algorithmic complexity, or ensure efficient use of memory, CPU, and I/O operations. This includes reviewing data structures, identifying unnecessary computations, optimizing loops and iterations, evaluating caching strategies, and ensuring efficient database queries or API calls.\n\nExamples:\n\n<example>\nContext: The user has just written a data processing function that iterates over a large dataset.\nuser: "Write a function that finds all duplicate entries in a list of user records"\nassistant: "Here is the function that finds duplicates:\n```python\ndef find_duplicates(records):\n duplicates = []\n for i, record in enumerate(records):\n for j, other in enumerate(records):\n if i != j and record['id'] == other['id']:\n if record not in duplicates:\n duplicates.append(record)\n return duplicates\n```\nNow let me use the computational-efficiency-expert agent to review this implementation for performance optimizations."\n</example>\n\n<example>\nContext: The user is working on a feature that involves caching and wants to ensure optimal memory usage.\nuser: "I've implemented a caching layer for our API responses. Can you check if it's efficient?"\nassistant: "I'll use the computational-efficiency-expert agent to analyze your caching implementation for memory efficiency, cache invalidation strategies, and potential memory leaks."\n</example>\n\n<example>\nContext: After implementing a complex algorithm, proactive review for computational efficiency.\nuser: "Here's my implementation of a graph traversal algorithm for finding shortest paths"\nassistant: "I see you've implemented a graph algorithm. Let me use the computational-efficiency-expert agent to analyze the time and space complexity, and identify any opportunities for optimization."\n</example>\n\n<example>\nContext: User is concerned about database query performance.\nuser: "Our application is getting slow when loading the dashboard"\nassistant: "I'll use the computational-efficiency-expert agent to analyze the code paths involved in loading the dashboard, focusing on database queries, data fetching patterns, and potential N+1 query problems."\n</example>
Use this agent when you need to optimize system performance, identify and resolve bottlenecks, improve application response times, reduce resource consumption, conduct performance testing and profiling, tune database queries, optimize infrastructure configurations, improve scalability, analyze memory usage patterns, or implement caching strategies. Examples:\n\n<example>\nContext: User has implemented a new feature and wants to ensure it performs well.\nuser: "I've added a new dashboard feature that loads user analytics. Can you check if there are any performance issues?"\nassistant: "I'll use the performance-engineer agent to analyze the dashboard feature for performance bottlenecks and optimization opportunities."\n<uses Task tool to launch performance-engineer agent with instructions to profile the dashboard feature, identify slow queries or rendering issues, and provide optimization recommendations>\n</example>\n\n<example>\nContext: Application is experiencing slow response times in production.\nuser: "Our API endpoints are taking 3-5 seconds to respond. This is unacceptable."\nassistant: "I'll delegate this to the performance-engineer agent to investigate the API performance issues and identify the root causes."\n<uses Task tool to launch performance-engineer agent with instructions to profile API endpoints, analyze database queries, check for N+1 problems, review caching strategies, and provide specific optimization recommendations>\n</example>\n\n<example>\nContext: Proactive performance review after completing a feature.\nuser: "Here's the new real-time audio processing feature I just built."\nassistant: "Great work! Let me have the performance-engineer agent review this for any potential performance concerns, especially given the real-time nature of audio processing."\n<uses Task tool to launch performance-engineer agent with instructions to analyze the audio processing implementation for performance bottlenecks, memory leaks, CPU usage patterns, and optimization opportunities>\n</example>
Expert developer experience optimizer specializing in build performance, tooling efficiency, and workflow automation. Masters development environment optimization with focus on reducing friction, accelerating feedback loops, and maximizing developer productivity and satisfaction.
Performance optimization specialist. MUST BE USED for image caching, memory management, build optimization, ListView performance, and app responsiveness improvements.
Proactively optimizes Supabase database performance, cost efficiency, and scalability. Use for analyzing slow queries, optimizing schemas, improving RLS policies, and recommending Supabase best practices.
GPU optimization engineer for ImageScale specializing in CUDA optimization, FP16 inference, TensorRT acceleration, memory management, and maximizing throughput on RTX 3050 for AI image enhancement workloads.
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
GPU VRAM management patterns for sharing memory across services (Ollama, Whisper, ComfyUI). OOM retry logic, auto-unload on idle, and service signaling protocol.
Use this agent when working on IOR (Interleaved Or Random) benchmark testing using Docker, specifically when you need to: optimize performance, debug issues, analyze test results, configure Docker environments for IOR tests, troubleshoot containerized storage benchmarks, or improve IOR test configurations. Examples:\n\n<example>\nContext: User is running IOR tests in Docker and experiencing slow performance\nuser: "DockerでIORベンチマークを実行していますが、期待よりもスループットが低いです。何が原因でしょうか？"\nassistant: "IORのパフォーマンス問題を診断するために、ior-docker-performance-expertエージェントを使用します。"\n<Task tool invocation to launch ior-docker-performance-expert>\n</example>\n\n<example>\nContext: User completed implementing Docker-based IOR test infrastructure\nuser: "IORテストのためのDockerコンテナ設定を完成させました。"\nassistant: "実装が完了したので、ior-docker-performance-expertエージェントを使ってDockerとIORの設定を最適化し、潜在的な問題がないか確認させていただきます。"\n<Task tool invocation to launch ior-docker-performance-expert>\n</example>\n\n<example>\nContext: User is setting up a new IOR benchmark environment\nuser: "新しいストレージシステムのベンチマークを取りたいのですが、DockerでIORを使う最適な方法を教えてください。"\nassistant: "IORとDockerの専門エージェントを起動して、最適な環境構築とベンチマーク戦略を提案させていただきます。"\n<Task tool invocation to launch ior-docker-performance-expert>\n</example>