'optimization' 검색 결과

Solve optimization problems in Python with one or several competing goals. Define constraints, compare search algorithms, visualize trade-offs, and assess candidate solutions before choosing an option that fits your priorities.

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• Comparing designs with competing cost and performance goals
• Searching for solutions under explicit constraints
• Benchmarking optimization algorithms

Build AI workflows with DSPy by defining inputs and outputs and combining reusable components. Create question answering systems that use retrieved documents, tool-using agents, and automatically optimized prompts, then compare results with evaluation data.

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• Build question answering systems that use retrieved documents
• Optimize prompts using examples and evaluation metrics
• Combine AI tasks into reusable processing stages

Integrate Flash Attention into transformer models to reduce processing time and GPU memory use for long sequences. Covers PyTorch integration, advanced attention options, and benchmarks that compare speed, memory use, and output accuracy.

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• Speeding up transformer training on long sequences
• Reducing GPU memory use during attention
• Adding Flash Attention to a PyTorch model

Helps configure and troubleshoot DeepSpeed for training large models across multiple devices. Covers memory optimization, parallel training, and DeepNVMe storage transfers, with examples for tuning data movement and configuring models that divide work among specialized components.

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• Configuring distributed training for large models
• Tuning tensor transfers to NVMe storage
• Setting up models with specialized expert components

Improve app store listings through keyword research, competitor comparisons, and revised titles and descriptions. Plan A/B tests, review visual messaging, and organize launch preparations for apps on Apple App Store and Google Play Store.

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• Researching keywords for an app store listing
• Rewriting app titles and descriptions
• Comparing competitor keywords and visual messaging

Review game code for likely performance bottlenecks in processing, memory, rendering, and data loading. Compare available estimates with configured limits and produce ranked optimization recommendations, including expected impact, effort, risks, and areas needing runtime measurement.

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• Finding performance bottleneck candidates in one game system
• Reviewing performance across the whole game
• Comparing available estimates with configured performance limits

Reviews Google Cloud spending and resource usage to identify waste, recommend suitable resource sizes, compare discount options, and establish ongoing cost management using the cost optimization guidance in the Google Cloud Well-Architected Framework.

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• Reviewing Google Cloud spending by team or project
• Finding idle resources and oversized instances
• Comparing CUDs, SUDs, and Spot VMs

Answers questions about GKE spending using BigQuery billing exports and live cluster metrics. It breaks down costs by project, namespace, or workload and compares requested computing resources with actual usage to investigate cost increases.

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• Comparing GKE costs across projects
• Finding the most expensive namespace or workload
• Investigating a sudden increase in cluster costs

Audit website images for search visibility and loading performance, then prioritize fixes. Check descriptive text, file sizes, and loading settings, convert files to WebP or AVIF, and add creator and copyright metadata.

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• Audit product and article images for SEO issues
• Convert large images to WebP or AVIF
• Fix image loading settings and layout shifts

Train Mixture of Experts models, which activate selected specialist networks for each input. The skill guides architecture, input routing, workload balancing, and GPU distribution using DeepSpeed or HuggingFace, with examples for training configuration and inference optimization.

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• Designing a model that activates only selected expert networks
• Balancing workloads across expert networks
• Distributing experts across multiple GPUs

Build AI workflows with DSPy by defining inputs and outputs and combining reusable components. Create question answering systems that use retrieved documents, tool-using agents, and automatically optimized prompts, then compare results with evaluation data.

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• Build question answering systems that use retrieved documents
• Optimize prompts using examples and evaluation metrics
• Combine AI tasks into reusable processing stages

Implement and troubleshoot large-model training with DeepSpeed across multiple GPUs. The skill covers memory optimization, parallel training, and numerical precision choices, alongside examples for storage transfers and models that divide work among specialized components.

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• Review settings for distributed model training
• Tune transfers between NVMe storage and tensors
• Configure training with specialized model components

Optimize transformer attention, the calculation that relates tokens to one another, using Flash Attention. The skill guides PyTorch integration, GPU memory reduction, performance measurement, and output comparisons for training or inference with long sequences.

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• Reducing GPU memory use with long sequences
• Integrating Flash Attention into PyTorch models
• Comparing runtime and outputs before and after changes

Get guidance on fine-tuning language models with Unsloth, including LoRA and QLoRA methods for training with fewer resources. The skill points to documentation for understanding APIs, implementing training code, debugging problems, and exploring speed and memory optimizations.

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• Exploring faster training with lower memory use
• Learning about LoRA and QLoRA fine-tuning
• Checking Unsloth features and APIs

Assess a website separately for five AI search services, including ChatGPT and Google AI Overviews. Compare scores, identify gaps in content and platform presence, and receive actions organized by priority and timeframe.

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• Comparing website readiness across five AI search services
• Reviewing question headings, direct answers, and tables
• Checking brand information across external platforms

Diagnose slow loading, high memory use, and low frame rates in USD 3D scenes. Coordinate scene structure checks, validation, optimization, and before-and-after performance comparisons, then produce a report documenting findings and changes.

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• Investigating slow USD scene loading
• Diagnosing low frame rates in CAD scenes
• Investigating high GPU or system memory use

Improve a web page’s loading speed, response to user actions, and visual stability. The skill guides targeted fixes for LCP, INP, and CLS, with code examples, measurement methods, and checklists for each metric.

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• Speeding up the display of a page’s main image
• Reducing delays after clicks and keyboard input
• Preventing layout shifts from images, ads, and fonts

Improve Astro site loading with critical CSS, the styles needed for the first screen, plus file compression, font loading adjustments, main image preloading, and deferred third-party scripts. Includes integration settings and a layout example.

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• Improve an Astro site's initial loading
• Configure astro-critters and build compression
• Adjust font and main image loading

Investigate Next.js security issues across Server Actions, middleware, image optimization, and cached pages. Provides testing workflows for authentication bypass and data exposure, with checks to distinguish actual vulnerabilities from normal responses.

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• Checking authentication in Server Actions and middleware
• Testing image optimization for unintended server requests
• Inspecting page data for sensitive information

MCP server optimization and transport layer enhancement for claude-flow v3. Implements connection pooling, load balancing, tool registry optimization, and performance monitoring for sub-100ms response times.

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Provides React Native performance optimization guidelines for FPS, TTI, bundle size, memory leaks, re-renders, and animations. Applies to tasks involving Hermes optimization, JS thread blocking, bridge overhead, FlashList, native modules, or debugging jank and frame drops.

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Problem-solving strategies for constrained optimization in optimization

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Problem-solving strategies for convex optimization in optimization

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Golang performance optimization patterns and methodology - if X bottleneck, then apply Y. Covers allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, and hot-path optimization. Use when profiling or benchmarks have identified a bottleneck and you need the right optimization pattern to fix it. Also use when performing performance code review to suggest improvements or benchmarks that could help identify quick performance gains. Not for measurement methodology (→ See `samber/cc-skills-golang@golang-benchmark` skill) or debugging workflow (→ See `samber/cc-skills-golang@golang-troubleshooting` skill).

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