「metric」の検索結果

Rigorous methodology for evaluating ML models on established benchmarks. Covers proper train/val/test splits, baseline verification from original papers, exact metric formula discrepancies, data-leak detection checklist, multi-seed robustness, and honest reporting templates. Use when claiming to beat published baselines, writing methods papers, or auditing existing results.

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。

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-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。

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, Annoy MHFP6, 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。

Query aggregate Langfuse cost, volume, latency, and model telemetry efficiently with Metrics API v2. Use for dashboards, rollout checks, usage summaries, and cost or latency comparisons; use Observations API v2 instead when individual rows are actually required.

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。

visionOS / iOS 空間コンピューティングリファレンス。RealityKit、ARKit、visionOS SwiftUI API。 Entity, ModelEntity, AnchorEntity, Component, RealityView, MeshResource, Material, System, EntityAction, GaussianSplatComponent, ComputeGraphComponent, PhysicsJointsComponent, ForceEffectComponent, ClothBodyComponent, NavigationComponent, NavigationMeshComponent, NavigationController, ImmersiveSpace, ImmersionStyle, Model3D, WindowGroup, VolumetricWindowStyle, ObjectTrackingProvider, RoomTrackingProvider, ARSession, ARWorldTrackingConfiguration, ARKitSession, WorldTrackingProvider, HandTrackingProvider, SceneReconstructionProvider。

Rory Sutherland's 'psycho-logic' approach to marketing, product, and business problems — solve the human perception, not just the engineering or economic metric. Use when the obvious 'logical' fix is expensive, slow, or already done by every competitor; when the user asks 'how do we make people want X', 'the product/price/funnel is fine but nobody converts', 'how do we stand out', 'is there a cheaper way to fix this', or mentions behavioral economics, nudges, irrational behavior, perception, or 'it doesn't make sense but it works'. This is the ROUTER skill: it diagnoses the problem in human terms, then dispatches to reframing, costly-signaling, counterintuitive-tests, or experience-design. Inspired by Rory Sutherland's book *Alchemy*. 日本語の相談（「商品はいいのに売れない」「値下げしかない？」「競合と差別化できない」）にも使う。中文咨询（“产品很好却卖不动”“只能降价吗？”“无法差异化”）也适用。For a catalogue of individual biases, use marketing-psychology (separate plugin) instead.

Fix the psychologically significant moment, not the logically significant metric — Rory Sutherland on experience. Use when the outcome is fine but the journey feels bad: complaints about waiting, anxiety, uncertainty, or 'it's technically fast/cheap/correct but people still hate it'; when the user asks 'how do we reduce complaints', 'how do we make waiting/onboarding/checkout feel better', 'people are anxious during X', or is about to spend big to shave a number (wait time, load time, steps) when the real pain is how the moment feels. Covers uncertainty reduction, idle vs occupied waiting, and the peak-end rule. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic. 日本語の相談（「待ち時間のクレームが多い」「手続きの途中で離脱される」「お客様を不安にさせている」）にも使う。中文咨询（“等待时间投诉多”“办理中途流失”“让客户感到不安”）也适用。

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。

Add p-value brackets, significance asterisks, and effect-size annotations to distribution plots using ggpubr, ggsignif, and statannotations with correct test selection (parametric vs non-parametric vs paired), multiple-testing adjustment, and rendering of negative results. Use when a boxplot/violin/raincloud needs in-figure statistical comparisons between groups.

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, Annoy MHFP6, 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。

Provides patterns to configure Spring Boot Actuator for production-grade monitoring, health probes, secured management endpoints, and Micrometer metrics across JVM services. Use when setting up monitoring, health checks, or metrics for Spring Boot applications.

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。

Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.

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。

Define auditable QA metrics with explicit numerators, denominators, populations, windows, and treatment of every result state.

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。

Implement comprehensive monitoring, logging, metrics, tracing, and alerting for production applications to ensure reliability and quick incident response. Use when setting up application monitoring, implementing structured logging, creating metrics and dashboards, setting up alerts, implementing distributed tracing, monitoring performance, tracking errors, or building observability into applications.

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 a concise, executive-ready performance summary covering key metrics, trends, and what's driving movement. Use this skill when someone needs to produce a briefing, executive summary, performance narrative, or stakeholder readout — for example, "write an exec summary of last week's performance," "create a performance briefing for our leadership team," "produce a monthly business review summary," "what should I tell executives about our metrics," or "generate a performance narrative." Also trigger for "QBR summary," "weekly business review," or "stakeholder briefing."

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。

Identifies which items (pages, campaigns, products, channels, regions) had the biggest increases or decreases for a key metric between two time periods. Use this skill when someone asks "what's up and what's down," "which campaigns moved the most," "top gainers and losers," "what pages are trending," "show me what changed by channel," or any variation of identifying the biggest movers and decliners for a metric.

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。

Produces a compact KPI digest showing how key metrics changed over a period and what's driving the movement. Use this skill when someone asks for a performance summary, a weekly recap, a morning briefing, a KPI update, or any variation of "how did we do this week/month." Also trigger for requests like "give me a performance overview," "what moved in the last 7 days," "pull our KPI report," or "summarize our metrics."

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。

Produces a compact KPI digest showing how key metrics changed over a period and what's driving the movement. Use this skill when someone asks for a performance summary, a weekly recap, a morning briefing, a KPI update, or any variation of "how did we do this week/month." Also trigger for "give me a performance overview," "what moved in the last 7 days," "pull our AA KPI report," or "summarize our metrics."

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。

Identifies which items (pages, campaigns, products, channels, regions) had the biggest increases or decreases for a key metric between two time periods. Use this skill when someone asks "what's up and what's down," "which campaigns moved the most," "top gainers and losers," "what pages are trending," "show me what changed by channel," or any variation of identifying the biggest movers and decliners for a metric.

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。

Write a rigorous, decision-grade experiment spec — falsifiable hypothesis, primary metric, guardrails, randomisation unit, exposure definition, method (A/B, holdout, switchback, quasi-experiment, MAB), MDE/duration plan, peek policy, validity threats, and pre-committed decision rule. Platform-agnostic. Load when the user has a candidate experiment and needs to spec it before launch, or says "spec this experiment", "write the test plan", "design this A/B test", "what's the hypothesis", "how big a sample do we need", "how long should we run this", "define the metrics for this test", or when the experimentation orchestrator routes here.

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。

Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.

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。

Add p-value brackets, significance asterisks, and effect-size annotations to distribution plots using ggpubr, ggsignif, and statannotations with correct test selection (parametric vs non-parametric vs paired), multiple-testing adjustment, and rendering of negative results. Use when a boxplot/violin/raincloud needs in-figure statistical comparisons between groups.

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, Annoy MHFP6, 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。