データ分析、機械学習、LLMアプリ、プロンプト設計（8,870 件）
Search the live web and read full pages with You.com. Use whenever a task needs information newer than training data or from outside the codebase: current library docs and API references, changelogs and release notes, breaking changes, error messages, package versions, pricing, news, or any claim to verify with cited sources. Works keyless through the free MCP profile when no You.com credentials are configured.
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Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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Investigate and remediate data quality alerts using Monte Carlo MCP tools. Runs root cause analysis, assesses blast radius, discovers available tools (MCP/CLI/API), proposes and executes fixes, or escalates with full context when uncertain.
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Orchestrate incident response — triage, root cause, remediate, prevent recurrence. USE WHEN active alerts, data broken, stale, pipeline failure, or investigate and fix a data incident.
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Analyze a Monte Carlo monitor and recommend config changes to reduce alert noise. Supports metric, custom SQL, validation, table, and agent (metric, evaluation, trajectory, validation) monitors. Fetches the report, identifies patterns, and suggests tuning.
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Shift-left safety net for dbt/SQL model edits. Runs change impact assessment before edits, generates SQL validation queries after, and executes them via `/mc-validate run`. Delegates health and monitor creation to peer skills.
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 SQL validation notebooks for dbt changes. Pass a GitHub PR URL or local dbt repo path.
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 a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.
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Build a library of metacognitive prompts targeting planning, monitoring, or evaluation for a specific task. Use when developing students' thinking-about-thinking during independent work.
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Create self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying principles.
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Generate structured reflection prompts for a specific teaching experience or professional learning challenge. Use when debriefing lessons, journaling, or preparing for coaching conversations.
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Interpret learning analytics data and translate dashboard findings into actionable teaching decisions. Use when reviewing LMS data, quiz patterns, or engagement metrics.
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Explain and configure individual spacing algorithms using student performance data and forgetting curves. Use when personalising retention schedules in adaptive learning platforms.
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Generate elaborative interrogation prompts that deepen encoding through targeted why and how questions. Use when students memorise without understanding or need deeper processing of content.
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。
Execute a plan or concrete work prompt end-to-end. Use when implementing from docs/plans, a spec path, or a clear build request; use ce-debug for open-ended bugs.
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Run metric-driven optimization loops. Use when improving measurable outcomes such as search relevance, clustering quality, build performance, prompt quality, or scored behavior through experiments.
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。
When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' 'data hygiene,' 'leads aren't getting to sales,' 'pipeline management,' 'lead qualification,' or 'when should marketing hand off to sales.' Use this for anything involving the systems and processes that connect marketing to revenue. For cold outreach emails, see cold-email. For email drip campaigns, see emails. For pricing decisions, see pricing.
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When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," "in-app pricing," "free users won't upgrade," "trial to paid conversion," or "how do I get users to pay." Use this for any in-product moment where you're asking users to upgrade. Distinct from public pricing pages (see cro) — this focuses on in-product upgrade moments where the user has already experienced value. For pricing decisions, see pricing.
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When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking plan," "how do I measure this," "track conversions," "attribution," "Mixpanel," "Segment," "are my events firing," or "analytics isn't working." Use this whenever someone asks how to know if something is working or wants to measure marketing results. For A/B test measurement, see ab-testing.
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Lightweight, script-driven variant of data2motion for turning data into a smooth, on-brand animated chart (one self-contained HTML) — built for a TEXT-ONLY model that cannot see its own output. You never write HTML/CSS/SVG/animation; you extract the data, pick a chart, fill a small JSON spec, and run one build script that owns 100% of the look and the motion, so the result cannot drift from the house template style or lose its smoothness. Use to turn a number/stat/table/CSV/paragraph into a quick animated chart, data reveal, or KPI when you want guaranteed template fidelity with minimal reasoning and no image feedback.
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統計的推論（線形回帰 / 重回帰 / OLS / Lasso / Ridge / 分位点回帰、ロジスティック回帰 / ポアソン / GLM / オッズ比、 混合効果 / マルチレベル / パネル固定効果、仮説検定 / t 検定 / χ² / ANOVA / 有意差、生存時間分析 / Kaplan-Meier / Cox、 ベイズ推定 / MCMC / Stan）を実行したら必ずセットで出す図と値のルーター。sm.OLS, smf.ols, LinearRegression, LassoCV, QuantReg, sm.Logit, sm.GLM, LogisticRegression, mixedlm, PanelOLS, ttest_ind, mannwhitneyu, chi2_contingency, pingouin, lifelines, CoxPHFitter, cmdstanpy, arviz がコードに現れたとき、またはユーザーが「回帰して」「検定して」 「有意差はあるか」「生存曲線を描いて」「ベイズで推定して」と言ったときに使う。診断・残差・図に言及がなくても適用する。 SKILL.md のルーティング表で手法を特定し、対応する references/<手法>.md を読んでから実行する。A/B テスト・因果推論は causal-inference-diagnostics、予測 ML・時系列は predictive-modeling-diagnostics を使う。
予測モデリング（教師あり機械学習 / 予測モデル / 精度評価 / 交差検証 / リーク、決定木 / ランダムフォレスト / GBDT / LightGBM / XGBoost / CatBoost、モデル解釈 / 特徴量重要度 / SHAP / PDP / ICE、時系列 / 季節性 / ARIMA / SARIMA / Prophet / VAR / 変化点 / 時系列予測）を実行したら必ずセットで出す図と値のルーター。train_test_split, cross_val_score, GridSearchCV, Pipeline, TimeSeriesSplit, DecisionTree, RandomForest, lgb., xgb., CatBoost, dtreeviz, shap., permutation_importance, PartialDependenceDisplay, statsmodels.tsa, SARIMAX, STL, adfuller, plot_acf, Prophet, ruptures がコードに現れたとき、または ユーザーが「予測モデルを作って」「精度を出して」「どの変数が効いているか」「来月を予測して」「季節性を見て」と 言ったときに使う。診断・リーク・ベースライン・残差に言及がなくても適用する。SKILL.md のルーティング表で手法を特定し、 対応する references/<手法>.md を読んでから実行する。回帰係数の推論・検定は statistical-inference-diagnostics、 効果推定は causal-inference-diagnostics を使う。
Use this when reviewing statistical analysis, experiments, model evaluation, or ML work done by someone else (a notebook, PR, or report) — checking that the required context is stated and that every method applied comes with the figures and values required by the matching *-diagnostics skill. When running an analysis yourself, apply the matching *-diagnostics skill directly.
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予測モデリングのワークフローを実装・評価する。引数: <dataset_path> <target> <task>