「optimization」の検索結果

Comprehensive SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, and GEO for AI Overviews/ChatGPT/Perplexity. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed, structured data.

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。

Local SEO analysis covering Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, location page quality, multi-location SEO, and industry-specific recommendations. Detects business type (brick-and-mortar, SAB, hybrid) and industry vertical (restaurant, healthcare, legal, home services, real estate, automotive). Use when user says "local SEO", "Google Business Profile", "GBP", "map pack", "local pack", "citations", "NAP consistency", "local rankings", "service area", "multi-location", or "local search".

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 SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, and GEO for AI Overviews/ChatGPT/Perplexity. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed.

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。

Local SEO analysis covering Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, location page quality, multi-location SEO, and industry-specific recommendations. Detects business type (brick-and-mortar, SAB, hybrid) and industry vertical. Use when user says "local SEO", "Google Business Profile", "GBP", "map pack", "local pack", "citations", "NAP consistency", "service area", or "multi-location".

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。

Generate SEO-optimized competitor comparison and alternatives pages. Covers "X vs Y" layouts, "alternatives to X" pages, feature matrices, schema markup, and conversion optimization. Use when user says "comparison page", "vs page", "alternatives page", "competitor comparison", "X vs Y", "versus", "compare competitors", or "alternative to".

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: reads Google SERPs backwards to detect page-type mismatches, derives user stories from search intent signals, and scores pages from multiple persona perspectives. Identifies why well-optimized pages fail to rank by analyzing what Google rewards for each keyword. Use when user says "SXO", "search experience", "page type mismatch", "SERP analysis", "user story", "persona scoring", "why isn't my page ranking", "intent mismatch", or "wireframe".

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。

Audit Xcode project configuration, build settings, scheme behavior, and script phases to find build-time improvements with explicit approval gates. Use when a developer wants project-level build analysis, slow incremental builds, guidance on target dependencies, build settings review, run script phase analysis, parallelization improvements, or module-map and DEFINES_MODULE configuration.

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。

Analyze Swift Package Manager dependencies, package plugins, module variants, and CI-oriented build overhead that slow Xcode builds. Use when a developer suspects packages, plugins, or dependency graph shape are hurting clean or incremental build performance, mentions SPM slowness, package resolution time, build plugin overhead, duplicate module builds from configuration drift, circular dependencies between modules, oversized modules needing splitting, or modularization best practices.

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。

Benchmark Xcode clean and incremental builds with repeatable inputs, timing summaries, and timestamped `.build-benchmark/` artifacts. Use when a developer wants a baseline, wants to compare before and after changes, asks to measure build performance, mentions build times, build duration, how long builds take, or wants to know if builds got faster or slower.

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。

Optimizes Simulink models for Embedded Coder generated code. Use when asked to optimize or improve generated code, or reduce code metrics for a Simulink model. Targets: execution time, memory footprint (RAM, ROM, stack, data copies), code size, MISRA compliance, or any semantically similar generated-code metric. Works iteratively — measures baseline, suggests changes, applies, and re-measures to confirm improvement. Triggers can be prompts similar to: optimize generated code runtime, reduce runtime, shrink code size, improve code efficiency, reduce memory usage, speed up generated code, follow MISRA compliance and so on. CAUTION: Do NOT attempt to optimize Simulink models for generated code efficiency without following this skill — the iterative measurement, gating, and rollback workflow is essential for safe 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。

Configure Simulink models for Embedded Coder (ERT), Simulink Coder (GRT rapid-prototyping), or AUTOSAR code generation. Use when the user asks to generate embedded C or C++ code, run a full build of a model, produce a code generation report, configure a model for production/ECU deployment or rapid-prototyping code, target ARM or x86 hardware, apply MISRA C/C++ compliance (ERT/AUTOSAR only — Simulink Coder does not ship MISRA profiles), or set up GRT, ERT, AUTOSAR, or shared-library targets. Handles target selection, hardware mapping, model hierarchy propagation, and constraint introspection via the configure_for_codegen function. Do NOT use for GRT shared-library variants (grt_malloc.tlc), DDS, or ROS, or for iterative optimization workflows that measure baseline metrics, apply targeted changes, and re-measure to confirm improvement.

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。

Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-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。

Resolve the architecture of focal oncogene amplifications — extrachromosomal DNA (ecDNA), breakage-fusion-bridge (BFB) cycles, homogeneously staining regions (HSR), and linear amplification — from whole-genome sequencing with AmpliconArchitect, the AmpliconSuite pipeline, and AmpliconClassifier. Covers copy-number seed selection, breakpoint-graph reconstruction, balanced-flow optimization, ecDNA classification, and the limits of depth-only amplification calls. Use when a focal amplification needs structural characterization, when distinguishing ecDNA from chromosomal amplification, suspecting ecDNA-driven oncogene amplification or therapy resistance, or selecting copy-number seeds for amplicon reconstruction.

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。

Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.

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。

Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures, scaffold-hopping vs lead-optimization regimes, activity-cliff diagnosis, and large-library nearest-neighbor methods (BulkTanimoto, MHFP6 LSH forest, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs.

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。

Performs alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda scheduling, soft-core potentials, MBAR/BAR analysis, cycle-closure validation, and protocol-appropriate enhanced sampling. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.

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。

Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.

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 an Apple-platform app feels slow, memory grows, battery drains, or ANY performance issue needs diagnosing. Covers memory leaks, profiling, Instruments workflows, retain cycles, 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 when the user mentions Swift performance audit, code optimization, or performance review — ARC issues, allocation patterns, and generic specialization.

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 the user mentions battery drain, energy optimization, power consumption audit, or pre-release energy check — timer abuse, polling patterns, continuous location, animation leaks, and background mode misuse.

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 the user mentions slow builds, build performance, or build time 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。

Audit, diagnose, or optimize website loading and interaction performance, Core Web Vitals, and Lighthouse performance scores.

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。

Microstrip, stripline, CPW, differential pairs, and crosstalk analysis for impedance-controlled PCB interconnects. TRIGGER: user asks to design or analyze a transmission line (microstrip, stripline, CPW, coplanar, differential pair), extract RLGC or per-unit-length parameters, compute trace impedance, analyze a PCB trace cross-section, or perform crosstalk/coupling analysis. Invoke BEFORE writing code — preferred over RF Toolbox analytical functions (txlineMicrostrip, txlineStripline, txlineCPW). SKIP: EM simulation/S-parameter extraction of an existing component (use matlab-analyze-em), material/stackup definition only (use matlab-manage-pcb-material), building custom non-catalog geometry (use matlab-assemble-pcb-layout), optimization sweeps (use matlab-optimize-pcb-design).

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。

Cascade PCB components, add lumped elements, export Touchstone, and bridge to eye diagram or antenna array workflows. TRIGGER: user asks to cascade or connect multiple RF PCB components, add lumped R/L/C, export S-parameters to Touchstone, or combine PCB elements into a circuit. Invoke BEFORE writing pcbcascade or circuit code — cascade rules and port matching are non-obvious. SKIP: designing individual components (use the specific matlab-design-pcb-* skill), EM analysis of a single component (use matlab-analyze-em), material/stackup setup only (use matlab-manage-pcb-material), optimization (use matlab-optimize-pcb-design).

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。