「metric」の検索結果

Analyze Arccos Golf performance data including club distances, strokes gained metrics, scoring patterns.

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-level topology knowledge. Use when working with topological spaces, continuity, compactness, connectedness, metric spaces, homeomorphisms, fundamental groups, homology, manifolds, or algebraic topology. Also use when the user mentions 'open set', 'closed set', 'compactness', 'connectedness', 'homeomorphism', 'homotopy', 'fundamental group', 'covering space', 'manifold', 'homology', 'Euler characteristic', or 'topological invariant'.

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-level optics knowledge. Use when working with geometric optics, wave optics, interference, diffraction, polarization, lasers, fiber optics, optical instruments, or photonics. Also use when the user mentions 'reflection', 'refraction', 'Snell law', 'lens', 'mirror', 'interference', 'diffraction', 'polarization', 'laser', 'fiber optic', 'holography', 'aberration', or 'optical instrument'.

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-level real analysis knowledge. Use when working with limits, continuity, differentiability, Riemann and Lebesgue integration, sequences and series of functions, metric spaces, measure theory, or functional analysis foundations. Also use when the user mentions 'epsilon-delta', 'uniform continuity', 'uniform convergence', 'Lebesgue integral', 'measure theory', 'sigma-algebra', 'Banach space', 'Hilbert space', 'completeness', 'compactness', 'Heine-Borel', or 'dominated convergence'.

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。

Creates ad copy, Google Ads RSA headlines, A/B test plans, and campaign analysis for SME performance marketing. Handles paid search, paid social, email campaigns, and SEO content. Use when user asks about ads, campaigns, Google Ads, paid media, SEO, conversion optimization, or marketing performance 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。

Creates marketing analytics reports, KPI dashboards, ROI calculations, and performance summaries for SME marketing teams. Handles weekly/monthly reporting, campaign analysis, content performance reviews, and data-driven recommendations. Use when user asks about marketing metrics, reporting, KPIs, dashboards, ROI analysis, performance review, or data-driven marketing decisions.

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。

Design leakage-safe machine learning experiments that mirror real deployment and support fair model comparisons. Use when defining prediction timing, feature eligibility, train-validation-test splits, baselines, metrics, or controlled model iterations; not for auditing whether raw labels are trustworthy.

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。

Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance 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 when working with Laravel queues in production — Horizon dashboard, worker supervision, job metrics, balancing strategies — even when the user just says 'my jobs are piling up'.

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 to drive a scalar metric down or up across bounded iterations — keep on strict improvement, revert otherwise, state on disk. Triggers 'minimize X', 'optimize until it stops improving'.

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 métricas de treinamento, depure modelos com histogramas, compare experimentos, visualize grafos de modelos e perfil de desempenho com TensorBoard - kit de visualização de ML do Google

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。

[Project Management] Use when you need to enforce quality gates, verify compliance with standards, track quality metrics, and generate audit trails.

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。

Plan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment definitions.

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。

Compare model candidates using weighted metrics and deterministic ranking outputs. Use for benchmark leaderboards and model promotion decisions.

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 editing Chinese web fiction, narrative prose, or story drafts that sound AI-generated, overly polished, formulaic, explanatory, or too symmetrical; use to detect AI-writing fingerprints and revise only wording while preserving plot, facts, characters, names, and intent.

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。

Author a placeable layout block that looks right at ANY pane size. The one hard rule — size to the PANE with container queries (@container), never the viewport — plus list/form playbooks and the sizing contract (minWidth etc.) the viability metric reads. Use when adding/converting a croutonLayoutBlocks block, or when a block overflows/breaks in a narrow pane.

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。

System reference for the crouton layout engine (@fyit/crouton-layout) — the LayoutTree/layout_configs data model, the croutonLayoutBlocks registry and sizing contract, the viability metric, the deterministic placer (#709), and how layouts persist and seed. Use when reasoning about WHY a layout renders/composes/persists the way it does, when a question is shaped like "what is a LayoutTree / where do layouts live / why did the placer pick this arrangement / why won't this pane shrink / where does minWidth get read", or when a generated POC boots with the wrong (or no) default layout. Trigger phrases: "layout tree", "layout_configs", "viability check", "composeDefaultLayout", "block sizing contract", "why is my layout not viable". For authoring a block component, use the block-authoring skill instead.

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。

AID Phase 0 - Research & discovery. Use for validating problem spaces, identifying stakeholders, defining success metrics, deciding whether to proceed.

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 adding logging, metrics, tracing, or alerting to production code, or when a past incident lacks diagnostic 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。

Default material, information-density hierarchy, clean-rendering, negative-prompt hygiene, and evidence-preserving photo-repair layer for IM2 / GPT Image 2 / gpt-image-2 / image_gen. Use for clean generation/redraws, artifact cleanup, focal-detail control, atmospheric depth, physically coherent light paths, air scattering, volumetric light, light ratio, material realism, architecture/terrain/hard-surface scene mass and structural grounding, and 企业/公司活动/门店/团队合影/商品/餐饮/证件文档照片修复. Preserve identity, headcount, placement, product facts, text, logo, QR, date, price, and scene evidence; prefer residual blur over invented restoration detail.

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。

Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code spe...

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。

Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.

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。

This skill calculates key financial ratios and metrics from financial statement data for investment 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。

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimi...

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。