'optimization' 검색 결과

Write, review, and refactor React and Next.js code using performance guidelines from Vercel Engineering. Prioritize improvements to data fetching, downloaded code size, server processing, and unnecessary rendering, with rule files providing explanations and code examples.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing React code for performance issues
• Reducing delays in Next.js data fetching
• Reducing downloaded code size and load times

Plan social selling on LinkedIn through profile improvements, relevant posts, personalized direct messages, and follow-ups. Build a clear path from audience engagement to sales conversations, with metrics to track which activities generate meetings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improving a LinkedIn profile for sales
• Drafting personalized direct message sequences
• Turning post engagement into sales meetings

Write, review, test, and deploy Rust smart contracts on NEAR Protocol using near-sdk v5.x. Covers stored data, calls between contracts, security checks, upgrades, and gas optimization to reduce the computation costs of contract operations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a NEAR contract in Rust
• Reviewing access control and storage handling
• Testing calls between contracts

Guide a development task through requirements research, solution comparison, planning, implementation, optimization, and review. The workflow asks for approval at key stages and saves plans and completed task records in the project.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clarifying requirements before development
• Comparing alternative implementation approaches
• Reviewing a detailed plan before coding

Write, review, and refactor React and Next.js code using Vercel's performance guidelines. Apply prioritized rules to reduce sequential data fetching, shrink downloaded code, improve server processing, and avoid unnecessary updates to the interface.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing React and Next.js code for performance issues
• Improving data fetching that waits on sequential requests
• Reducing downloaded JavaScript and page load times

Provide other skills with tools for generating and editing images, analyzing image content, and improving text through external APIs. This foundation passes inputs to backend services without preprocessing and returns results as text or JSON.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding image generation to another skill
• Editing reference images with written instructions
• Analyzing the contents of multiple images

Analyze token usage and AI costs in GitHub Agentic Workflows, identify expensive steps and budget or timeout failures, and develop prioritized improvements checked against the original workflow’s output quality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating runs that exceed their AI budget
• Reducing costs in frequently scheduled workflows
• Reviewing workflows that hit time or turn limits

Guides performance improvements when writing, reviewing, or refactoring React and Next.js applications. Prioritized rules cover data fetching, bundle size—the amount of code delivered to browsers—and rendering, with explanations and code examples.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing React components for performance issues
• Improving data fetching in Next.js pages
• Reducing code delivered to browsers

Start the interactive Claude Octopus setup wizard to install and authenticate providers, configure RTK token optimization, choose a work mode, and complete onboarding. This skill directs configuration requests to the existing setup process.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up Claude Octopus for the first time
• Installing and authenticating providers
• Configuring RTK token optimization

Improve vllm-mlx performance through research, small experiments, and benchmarks on real models. The skill records results, keeps changes that improve measured performance, and carries successful work through implementation, testing, review, and pull request creation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reducing the time before a model starts responding
• Improving token generation speed
• Exploring memory and cache optimizations

Audit the journey from a search result to a page’s intended action. Review titles, search intent, speed, readability, calls to action, and mobile usability to identify problems behind low click rates or weak conversions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating low click rates despite good rankings
• Reviewing pages that attract search visits but few conversions
• Finding reasons visitors return to Google

Choose a mathematical approach for a problem you do not yet know how to model. Guided questions clarify change, uncertainty, optimization goals, and required precision, leading to a recommended framework, starting equations, tools, and related skills.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a mathematical approach for a real-world problem
• Deciding how to model discrete or continuous change
• Selecting methods for uncertainty or optimization

Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a lower-cost cloud for GPU training
• Run long training jobs with recovery after interruptions
• Configure distributed training across multiple machines

Automates computational chemistry through Rowan's Python API, including molecular property prediction, structure optimization, and protein–ligand binding predictions. Submits cloud calculations, handles batches of molecules, tracks workflow status, and retrieves results without a local compute cluster.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Batch prediction of molecular pKa values in Rowan
• Optimizing molecular structures and comparing conformers in Rowan
• Predicting protein–ligand binding and complex structures in Rowan

Design and improve prompts and structured responses for AI applications. The skill also guides evaluation, retrieval augmented generation, which uses retrieved material to inform answers, and agent architecture, with references and scripts for production workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improving prompts and response formats
• Evaluating answers based on retrieved documents
• Designing AI agent systems

Use pymoo in Python to optimize competing goals such as cost and performance under constraints. Find and visualize trade-off solutions, compare algorithms, and select candidates according to preferred priorities.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing designs with competing cost and performance goals
• Finding solutions that satisfy constraints
• Comparing algorithms on standard benchmark problems

Measure indicator calculation speed with BenchmarkDotNet, a benchmarking tool for .NET. Compare batch, incremental, and streaming implementations, detect slowdowns against saved results, and evaluate changes intended to reduce processing time and memory allocations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing batch, incremental, and streaming performance
• Detecting performance regressions after code changes
• Adding benchmarks for a new indicator

Choose a mathematical approach for a problem you do not yet know how to model. Guided questions clarify change, uncertainty, optimization goals, and required precision, leading to a recommended framework, starting equations, tools, and related skills.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a mathematical approach for a real-world problem
• Deciding how to model discrete or continuous change
• Selecting methods for uncertainty or optimization

Choose how to distribute tokens, the units a model processes, across MoE model experts during training. The guide compares alltoall, DeepEP, and HybridEP by GPU hardware, parallelism scale, memory needs, and optimization stage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a MoE dispatcher for specific GPU hardware
• Establishing an alltoall baseline for initial setup
• Comparing DeepEP and HybridEP under matching conditions

Improve MoE training throughput in Megatron Bridge. MoE models divide work among expert networks; this skill identifies memory, communication, and compute bottlenecks, guides controlled configuration comparisons, and validates speed alongside training health.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate training slowdowns after code or configuration changes
• Find memory, communication, and compute bottlenecks
• Compare parallelism, precision, and dispatcher settings

Generate small-molecule candidates from a starting structure, optimize supported molecular properties, and save results for validation. Local deployment also supports embeddings, numerical representations of molecules, and workflows that convert between molecular structures and learned internal representations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate molecular candidates around a seed structure
• Optimize candidates for QED or plogP
• Obtain molecular embeddings with a local deployment

Model and solve optimization problems with cuOpt through Python, C/C++, or the command line. Choose linear, mixed-integer, or quadratic methods based on the decisions involved, configure stopping limits, and investigate infeasible or unbounded results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Modeling resource allocation problems
• Representing whole-number and yes/no decisions
• Minimizing squared error or variance

Start a cuOpt REST server and build Python or curl clients to submit routing and optimization problems. Follow result polling, translate Python API field names into REST fields, and diagnose rejected requests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Start a cuOpt server locally or in Docker
• Submit a routing problem from Python
• Retrieve optimization results by polling

Turn written planning problems into mathematical optimization models. Separate fixed inputs, constraints, decisions, and objectives, then choose linear, mixed-integer, or quadratic formulations for tasks such as production planning, inventory management, and material allocation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Formulate production plans with capacity and cost constraints
• Model inventory limits across purchasing and sales periods
• Define multiple goals in priority order