'optimizer' 검색 결과

Use when modifying or reviewing the Qwik optimizer under packages/optimizer, Rust transform code, WASM/NAPI bindings, optimizer snapshots, or optimizer-facing runtime behavior.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

既存のパイプライン、個別プロジェクトフォルダ、ドキュメント構造、またはソフトウェアスタックを改善、改修、再構築するための構造化された6ステップのプロシージャ。「pipeline optimizer」（ソフトウェア、研究、ゲーム開発パイプラインなど、トピック全体のパイプライン対象）または「project-folder optimizer」（単一のソフトウェアツールや論文プロジェクトなど、パイプライン内の個別プロジェクトフォルダ対象）として呼び出し可能。「パイプラインXを改善する」、「スタックを最適化する」、「Yを再構築する」、「改修」、「パイプラインのリファクタリング」、「プロジェクトフォルダのクリーンアップ」、「フォルダ構造の改善」、「規約の統一」、「ドキュメントの統合」、「既存システムへの統合」、または確立された構造に対する実質的な介入タスクで起動。既存資産の調査、目的の明確化、理想像のスケッチ、ギャップ計画、経験的課題の特定、および新しいサブエージェントによる再テストを提供。並行標準の発生、重複、パイプラインの破綻を防止。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze application and system performance to find bottlenecks, the points that slow processing. Use resource usage and performance data to plan improvements to resource allocation, load balancing, caching, and algorithms, then compare results after changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating slow application processing
• Reviewing CPU and memory allocation
• Improving workload distribution across nodes

AWS Cost Optimizer workflow skill. Use this skill when the user needs Comprehensive AWS cost analysis and optimization recommendations using AWS CLI and Cost Explorer and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze large matrices before solving linear systems. Check structure and numerical stability, recommend preprocessing and solver settings, estimate selected solution values without a full solve, and assess convergence, memory use, and computational cost.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking matrix properties before solving a system
• Choosing preprocessing for numerical stability
• Estimating selected values without solving the full system

Plan portfolio allocations across assets using risk, correlations, return forecasts, and trading costs. Compare five optimization methods, produce settings for config.json, and define rebalancing rules that adjust holdings back toward their target weights.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing allocations across stocks, bonds, and gold
• Choosing an optimizer based on return forecasts and correlations
• Preparing portfolio settings for config.json

Reduce GPU memory use by loading language models in 8-bit or 4-bit formats with bitsandbytes and HuggingFace Transformers. Includes configuration examples, QLoRA fine-tuning of compressed models, and optimizers that reduce memory needed during training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Load larger models within limited GPU memory
• Compare 8-bit and 4-bit model settings
• Fine-tune a model with QLoRA

Guides PyTorch model training and fine-tuning, from choosing architectures to configuring optimizers and learning rates. Provides troubleshooting steps for unstable loss and memory shortages, plus methods for improving GPU throughput and comparing experiments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose model architectures for the available data type and scale
• Configure optimizers and learning-rate schedules
• Investigate loss spikes, NaNs, and GPU memory shortages

Helps reduce GPU memory use by loading large language models in 4-bit or 8-bit formats, a technique called quantization. Covers bitsandbytes configuration, memory checks, QLoRA fine-tuning, and training with memory-saving optimizers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Load a larger model within available GPU memory
• Compare 4-bit and 8-bit quantization settings
• Fine-tune a quantized model with QLoRA

The compile-readiness gate for prompt auto-optimization. Decide whether you have earned the right to run an optimizer (DSPy MIPROv2 / GEPA / BootstrapFewShot) before spending compute. Two preconditions only — a real metric, and enough examples for the optimizer you picked. Garbage metric in, garbage prompt out. Pick the optimizer by data scale; GEPA inverts the scale assumption (~10 examples + textual feedback).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

AWS Cost Optimizer workflow skill. Use this skill when the user needs Comprehensive AWS cost analysis and optimization recommendations using AWS CLI and Cost Explorer and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Agent skill for matrix-optimizer - invoke with $agent-matrix-optimizer

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when executing admin-level cleanup in a Salesforce org: deleting unused fields, removing inactive Flow versions, deactivating legacy automation, running Salesforce Optimizer or Org Check, and performing destructive metadata deploys. Triggers: 'clean up unused fields', 'delete inactive flows', 'run Optimizer', 'org has too many custom fields', 'remove old workflow rules', 'Flow version limit'. NOT for assessing or reporting on technical debt (use architect/technical-debt-assessment). NOT for code-level refactoring or Apex cleanup (use apex/ skills).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill to design, configure, and troubleshoot the Field Service Lightning (FSL) Optimizer — including selecting optimization type (Global, In-Day, Resource Schedule), understanding the priority-score model, configuring travel mode (Aerial vs Street-Level Routing), and tuning optimizer behavior for specific scheduling objectives. NOT for work rules and service-objective weights on a scheduling policy — use admin/fsl-scheduling-policies. NOT for ESO adoption and territory sizing — use architect/fsl-optimization-architecture.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Warehouse cost reduction, auto-scaling, query optimization, and lifecycle policies for data infrastructure. Activate on: data cost, warehouse credits, cost reduction, auto-scaling, lifecycle policy, cold storage, cost monitoring, resource optimization. NOT for: query performance tuning (use data-warehouse-optimizer), batch job optimization (use batch-processing-optimizer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

CI/CD caching optimizer for dependency caching, Docker layer caching, and build speed improvements. Activate on: CI cache, build speed, dependency caching, Docker layer cache, turbo remote cache, GitHub Actions cache, pnpm store cache. NOT for: CI/CD pipeline creation (use github-actions-pipeline-builder), deployment strategy (use blue-green-deployment-orchestrator), Docker image building (use docker-multi-stage-optimizer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

结构化的6步骤流程，用于改进、改造或重构现有的流水线（pipeline）、独立项目文件夹、文档结构或软件技术栈。可调用为“pipeline optimizer”（针对整个主题流水线，例如软件、研究或游戏开发流水线）或“project-folder optimizer”（针对流水线内的独立项目文件夹，例如单个软件工具或论文项目）。触发于诸如“改进流水线X”、“优化技术栈”、“重构Y”、“改造”、“流水线重构”、“清理项目文件夹”、“改进文件夹结构”、“统一规范”、“文档整合”、“集成到现有系统”或针对既有结构的任何实质性干预等任务。提供既有资产调查、目的明确化、理想蓝图草图、差距规划、实证痛点识别以及使用全新的子智能体进行重新测试。防止并行标准、重复建设和流水线中断。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Procedimiento estructurado de 6 pasos para mejorar, renovar o reconstruir pipelines existentes, carpetas de proyectos individuales, estructuras de documentación o stacks de software. Direccionable como "pipeline optimizer" (para pipelines temáticos completos, p. ej. de software, investigación o desarrollo de videojuegos) o "project-folder optimizer" (para carpetas de proyectos individuales dentro de un pipeline, p. ej. una herramienta de software o proyecto de artículo académico). Se activa ante tareas como "mejorar pipeline X", "optimizar el stack", "reconstruir Y", "renovación", "refactorización de pipeline", "limpiar carpeta de proyecto", "mejorar estructura de carpetas", "unificar convenciones", "consolidación de documentación", "integrar en sistema existente" o cualquier intervención sustancial en estructuras establecidas. Proporciona análisis del estado actual, aclaración del propósito, boceto del estado ideal, plan de brechas, identificación empírica de puntos de dolor y reevaluación con subagentes limpios. Previene estándares paralelos, duplicación y rupturas en el pipeline.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Структурированная 6-шаговая процедура для улучшения, реновации или перестройки существующих пайплайнов, отдельных папок проектов, структур документации или стек-технологий. Вызывается как "pipeline optimizer" (для целых тематических пайплайнов, например, пайплайнов разработки ПО, исследований или создания игр) или "project-folder optimizer" (для отдельных папок проектов внутри пайплайна, например, единичного инструмента ПО или проекта статьи). Активируется при выполнении таких задач, как "улучшить пайплайн X", "оптимизировать стек", "перестроить Y", "реновация", "рефакторинг пайплайна", "навести порядок в папке проекта", "улучшить структуру папки", "унифицировать конвенции", "консолидация документации", "интегрировать в существующую систему" или при любом существенном вмешательстве в устоявшиеся структуры. Обеспечивает анализ существующего фонда, уточнение целей, эскиз идеального состояния, план устранения разрывов, эмпирическое выявление проблемных мест и повторное тестирование с помощью свежих субагентов. Предотвращает появление параллельных стандартов, дублирование и сбои в пайплайне.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Structured 6-step procedure for improving, renovating, or rebuilding existing pipelines, individual project folders, documentation structures, or software stacks. Addressable as "pipeline optimizer" (for whole topic pipelines, e.g. a software, research, or game-dev pipeline) or "project-folder optimizer" (for individual project folders within a pipeline, e.g. a single software tool or paper project). Triggers on tasks like "improve pipeline X", "optimize the stack", "rebuild Y", "renovation", "pipeline refactoring", "clean up project folder", "improve folder structure", "unify conventions", "documentation consolidation", "integrate into existing system", or any substantial intervention in established structures. Delivers building-stock analysis, purpose clarification, ideal sketch, gap plan, empirical pain-point identification, and retests with fresh subagents. Prevents parallel standards, duplication, and pipeline breaks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Reduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Warehouse cost reduction, auto-scaling, query optimization, and lifecycle policies for data infrastructure. Activate on: data cost, warehouse credits, cost reduction, auto-scaling, lifecycle policy, cold storage, cost monitoring, resource optimization. NOT for: query performance tuning (use data-warehouse-optimizer), batch job optimization (use batch-processing-optimizer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

CI/CD caching optimizer for dependency caching, Docker layer caching, and build speed improvements. Activate on: CI cache, build speed, dependency caching, Docker layer cache, turbo remote cache, GitHub Actions cache, pnpm store cache. NOT for: CI/CD pipeline creation (use github-actions-pipeline-builder), deployment strategy (use blue-green-deployment-orchestrator), Docker image building (use docker-multi-stage-optimizer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

개발 목표에 맞는 ECC 명령 그룹을 골라 실행 순서와 중단 조건을 안내합니다. 현재 파일을 읽어 용도별 목록을 정리하며 명령은 실행하지 않습니다.

• 버그 수정에 필요한 명령 순서 확인
• MVP 개발용 명령 선택
• ECC 작업 절차를 용도별로 조회