「zat」の検索結果

CLI modernization and hooks system enhancement for claude-flow v3. Implements interactive prompts, command decomposition, enhanced hooks integration, and intelligent workflow automation.

日本語の概要は準備中です。原文の説明を表示しています。

Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

日本語の概要は準備中です。原文の説明を表示しています。

Optimize code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.

日本語の概要は準備中です。原文の説明を表示しています。

Search Experience Optimization (SXO), the bridge between SEO and UX/CRO. Audits the journey from the SERP click to the on-page goal: click-through factors (title, meta, rich results), then post-click signals that keep users and drive conversions: above-the-fold relevance and intent match, page speed / Core Web Vitals, readability, clear CTAs, mobile usability, and what causes pogo-sticking back to Google. Use when the user has decent rankings but poor CTR or poor on-page conversion, wants better dwell time / engagement, less bounce, or a page experience aligned with search intent. Trigger on: "SXO", "search experience optimization", "good rankings but low CTR", "high bounce rate from search", "pogo-sticking", "improve dwell time", "SEO and conversion", "ranking but not converting", "engagement signals", "make this page convert search traffic". For pure landing-page conversion in ads use /google-ads-landing; for content quality use /seo-page.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill whenever the user asks for a chart, graph, plot, or visualization of data — 'chart this by month', 'show me a breakdown by category', 'visualize the trend'. Covers picking the right chart type for the question being asked, labeling/readability rules, and which plotting library to reach for. Pair with data-analysis for the underlying data prep.

日本語の概要は準備中です。原文の説明を表示しています。

Use when writing, reviewing, or refactoring SwiftUI code for iOS or macOS, including state and `@Observable` data flow, view composition, resizable layouts, safe areas, display scale, performance, lists, environment, localization, animation, Liquid Glass, and API migration. Also use for iPhone Duo, foldable, or large-display layouts (`NavigationSplitView` on large displays, iPhone tab sidebar, two-column reflow, foldable grids, `ArrangementView`, `ReservedRegion`), hinge effects, vertical bars, `@State` initialization or synthesized-property diagnostics, `@ContentBuilder` ambiguity, `reorderable` drag/drop, custom `AsyncImage` `URLSession`, swipe actions outside List, item-bound `alert`/`confirmationDialog`, `ToolbarOverflowMenu`, `AnimatableValues`, Document APIs (`Document`/`DocumentReader`), and Instruments `.trace` capture or analysis.

日本語の概要は準備中です。原文の説明を表示しています。

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit

日本語の概要は準備中です。原文の説明を表示しています。

Review chart choice, labeling, accessibility, and storytelling quality for existing visualizations. Use as an explicit/manual helper for critique and cleanup, not as the main owner for scientific figure production or general chart generation.

日本語の概要は準備中です。原文の説明を表示しています。

Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

日本語の概要は準備中です。原文の説明を表示しています。

Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.

日本語の概要は準備中です。原文の説明を表示しています。

⚠️ CRITICAL USER EXPERIENCE-BASED SKILL - ALWAYS CONSULT BEFORE DATA PREPROCESSING ⚠️ Prevents catastrophic errors (88.9% error rate in V1.0 case study) through multi-level feature analysis, data leakage detection, and semantic validation. MANDATORY for: data preprocessing, feature engineering, standardization, normalization, interpolation, missing value handling, feature selection, or ANY data transformation task. Covers grouped time-series, cross-sectional, panel data. Detects: time travel leakage, causal inversion, ID misuse, semantic-numeric fallacies, distribution blindness. User's hard-won lessons from real project failures.

日本語の概要は準備中です。原文の説明を表示しています。

World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.

日本語の概要は準備中です。原文の説明を表示しています。

Produce a prioritized performance-optimization roadmap across frontend, backend, and infrastructure. Use as an explicit/manual helper after bottlenecks are known or suspected, not as the owner of regression detection, profiling capture, or test execution.

日本語の概要は準備中です。原文の説明を表示しています。

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

日本語の概要は準備中です。原文の説明を表示しています。

Domain routing and boundary guidance for authorized insecure deserialization testing, including Java, PHP, Python, .NET, and gadget-chain analysis. Use when a task belongs to the deserialization testing domain and needs scope, evidence, pivot, or exit criteria.

日本語の概要は準備中です。原文の説明を表示しています。

Create or update GoClaw agent skills with eval-driven iteration. Use for new skills, skill scripts, references, benchmark optimization, description optimization, eval testing, extending agent capabilities.

日本語の概要は準備中です。原文の説明を表示しています。

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

日本語の概要は準備中です。原文の説明を表示しています。

Top-level workflow skill for USD performance diagnosis and optimization. Handles slow loading, high memory, low FPS, and broad scene-optimization requests; delegates auth/runtime setup to Phase 0 owners.

日本語の概要は準備中です。原文の説明を表示しています。

Use when a user asks to build, optimize, backtest, rebalance, or analyze a stock portfolio with Mean-CVaR, Mean-Variance/SOCP variance caps, efficient frontiers, scenario generation, or NVIDIA cuOpt.

日本語の概要は準備中です。原文の説明を表示しています。

Use for agent-assisted Auto-FL optimization of an existing NVFLARE job in simulation, POC, or production. Do not use for code conversion, diagnosis-only work, or deployment setup.

日本語の概要は準備中です。原文の説明を表示しています。

Evidence-gated workflow for MoE performance optimization in Megatron Bridge. Covers measurement contracts, the Three Walls framework, parallel folding, profiling, matched A/B tuning, and final validation.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill for MolMIM, NVIDIA's BioNeMo NIM microservice for small-molecule latent-space generation and optimization. Invoke for MolMIM, molecular embeddings, hidden states, latent decoding, sampling around a seed SMILES, CMA-ES guided molecule generation, QED or plogP optimization, hosted NVIDIA API calls, or local Docker deployment.

日本語の概要は準備中です。原文の説明を表示しています。

[omh] Translated product or content release: make a product or content release locale-ready with terminology, cultural-fit, and quality-review guidance. Use when the user says: localization review, translation QA, locale glossary, 현지화 검토, 번역 QA, 용어집.

日本語の概要は準備中です。原文の説明を表示しています。