「comparisons」の検索結果

Manual for the marginaleffects R and Python package, and guide to the book "Model to Meaning". Use when users ask about predictions, comparisons, slopes, marginal effects, average treatment effects (ATE/ATT/CATE), hypothesis testing, contrasts, counterfactuals, risk ratios, odds ratios, causal inference with G-computation, or need help with marginaleffects functions like predictions(), comparisons(), slopes(), hypotheses(), datagrid(), avg_predictions(), avg_comparisons(), avg_slopes(), or plot functions.

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。

記事・論文・メモを要約し、相互リンク付きのMarkdown知識集として蓄積。出典を残しながら質問への回答や比較をまとめ、矛盾やリンク切れも点検します。

• 記事や論文から調査用の知識集を作成
• 新しい資料を既存のノートに追加
• 出典付きの回答や比較を保存

Audit whether a paper's baseline comparisons are COMPLETE, FAIR, and SIGNIFICANT: a required recent SOTA baseline is missing while 'best/SOTA' is claimed (HP-MISSING-BASELINE); a baseline is undertuned / given less compute-tuning-data, run at a mismatched config, or the equal-budget ablation-as-baseline is absent (HP-WEAK-BASELINE); 'outperforms' is asserted over overlapping error bars or with no variance/seeds (HP-SIG-OVERLAP); and a cross-row 'improves over baseline by X%' is arithmetically wrong (HP-DELTA-ERROR, cross-row form only). A versioned per-domain baseline profile + a live leaderboard/recency search are assembled by the EXECUTOR as structured facts; a fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per dimension) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Works at L0 (stated comparisons) and deepens at L2 (configs/result files). A completeness question it cannot settle internally becomes needs_external_check, never a guessed missing baseline. Emits baseline-comparison-audit.findings.json; computes NO verdict. Detect-only. Triggers: "baseline audit", "missing baselines", "is the comparison fair", "weak baseline", "baseline 误报", "SOTA earned?".

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。

Run financial calculations and scenario comparisons — tax estimates, loan comparisons, retirement projections, rent vs. buy, investment scenarios, and more. Pure math, no accounts or logins needed.

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。

Convert deep research output (Claude Research, ChatGPT Deep Research, Gemini, Codex — any long-form MD/Word/text) into a consulting-quality HTML report: pyramid structure, numbers promoted to KPIs/charts, comparisons turned into framework tables, citations preserved. Use when the user provides research/investigation results and says レポートにして/HTMLにまとめて/資料にして/きれいにして, mentions ディープリサーチの結果/調査結果/リサーチ結果, or asks to research a topic AND deliver a report. NEVER invents numbers or facts not present in the source (盛らない原則).

An infographic skill for AI agents — works inside Claude Code, Codex, Hermes, OpenClaw, Cursor, and any agent host that loads skills. Create professional infographics as self-contained HTML files with PNG exports. Use when the user asks to create, design, generate, build, or make an infographic, data visualization, visual explainer, chart poster, cheatsheet, or pocket guide. Trigger phrases include "make this visual", "turn this into a graphic", "visualize this data", "infographic for X", "cheatsheet for X", or when the user provides structured data (tokenomics allocations, ecosystem/partner lists, timelines, comparisons, step-by-step processes) AND asks for a visual output. Covers tokenomics, ecosystem overviews, game overviews, cheatsheets, airdrop guides, comparisons, and process flows. Do NOT trigger for plain text answers, markdown tables, bulleted summaries, questions asking for advice or explanations about a topic (even when the topic is tokenomics, ecosystem, vesting, etc.), inline chat diagrams, or data processing with no visual output requested. The signal is "asks for a visual", not "mentions a keyword".

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。

METAINFORMANT rules for directory docs/comparisons. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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。

Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental 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。

Implements data visualization charts in Flutter using the fl_chart package (v1.2.0). Supports LineChart, BarChart, PieChart, ScatterChart, RadarChart, and CandlestickChart with implicit animations, touch interactivity, and pan/zoom transformations. Use when building any Flutter chart, graph, or data visualization — including line graphs for trends, bar charts for comparisons, pie/donut charts for proportions, scatter plots for distributions, radar charts for multi-axis comparisons, or candlestick charts for financial OHLC data. Also use when customizing chart axes, titles, tooltips, grid lines, touch callbacks, chart animations, or chart zoom/pan behavior.

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。

コードの構造や処理の流れ、性能比較を対話型の図で説明し、OpenClawのダッシュボードに指標や表を継続して表示するスキルです。

• コード構造やデータの流れを説明したいとき
• 実測した性能を比較したいとき
• UIの試作を操作して確認したいとき

プロジェクトで使う業務用語や概念の境界を、具体例とコードの照合で明確にします。決まった定義を用語集に記録し、重要な設計判断の理由も必要に応じて残すスキルです。

• 曖昧な業務用語を整理したいとき
• GLOSSARY.mdの作成・更新
• 業務上の定義とコードの整合確認

候補の探索や比較を繰り返し、試行の根拠と判断を追記型の台帳に残すスキル。前回との整合性や再検証の結果を確認し、実取引や本番操作へ進む条件を明確にします。

• 試行を重ねて候補を比較したいとき
• 判断の根拠を記録したいとき
• 新情報で前回の採用候補を見直したいとき

最新の公開情報と手元の資料を照合し、事実・推測・提案を分けて調査結果をまとめます。単発の確認、選択肢の比較、人物や企業の調査に合わせて調べ方を選ぶスキルです。

• 手元の資料を最新情報で検証したいとき
• 意思決定のために候補を比較したいとき
• 相談先の人物や企業を調べたいとき

市場規模の推計、競合比較、投資家や技術の調査を、出典付きでまとめるスキル。事実と推測を分け、反証やリスクも含めて事業判断につながる提案を整理します。

• 市場参入前に規模を推計したいとき
• 競合の特徴や立ち位置の比較
• 相談前の投資家調査

売上・返金・重複課金を請求データから整理し、チーム課金や利用権限の実装まで確認するスキル。料金表示と実際の動作を照合し、運営の判断材料をまとめます。

• 売上と契約状況を把握したいとき
• 重複課金や返金の原因調査
• チーム課金の実装を確認したいとき

処理の高速化を、現状の計測、仮説ごとの変更案の比較、正しさの検証へ分解し、時間や費用の上限内で最も速い安全な案と再実行できる手順を残すスキル。

• 遅い処理を計測しながら高速化したいとき
• 同じ入力条件で実装案を比較したいとき
• 応答時間・処理量・実行費用の評価

将来のキャッシュフローを現在価値に換算し、企業価値と1株当たりの評価額を算出するExcelを作成します。前提を変えた再計算やシナリオ比較、感度分析も扱います。

• 企業評価用のExcelを作りたいとき
• 弱気・基本・強気のシナリオ比較
• 前提変更による評価額の変化確認

類似企業の売上・利益率・評価倍率を比較するExcelを作成します。計算式、中央値や四分位、データの出典をまとめ、投資や買収の検討に使う比較資料を整えます。

• 同業他社との企業価値比較
• 買収検討用の比較資料作成
• 業界の成長性・収益性比較

Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex.

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 when the user wants a systematic literature review, survey, or synthesis across multiple academic papers on a topic. Also covers annotated bibliographies and cross-paper comparisons. Searches arXiv and outputs reports in APA, IEEE, or BibTeX format. Not for single-paper tasks — use academic-paper-review for reviewing one paper.

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 Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex.

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 Seaborn statistical visualizations with pandas integration for distributions, relationships, categorical comparisons, regression displays, pair plots, and heatmaps. Supports function and objects interfaces with explicit aggregation, uncertainty, and missing-data handling. Best suited to static exploratory plots; plotly covers interactive figures and scientific-visualization covers publication styling.

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 and reviews infographics with Nano Banana 2 via OpenRouter. Use for statistical summaries, timelines, comparisons, processes, and visual explanations with supplied data or optional Sonar research. Supports ten layouts, eight style presets, reference images, and accessible palette starting points.

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。

Applies the DHDNA framework as an exploratory rubric for reasoning and writing patterns in supplied text. Used for explicit requests for DHDNA, cognitive-style reflection, a thinking-pattern profile, or comparisons of textual reasoning. Scores describe evidence in the sample, not validated psychological traits or personal identity.

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。