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既存の学術原稿を、事実や専門用語、根拠が示す範囲を保ちながら英語で推敲・短縮し、中国語から英語への翻訳やLaTeXのページ配置の修正にも対応します。

• 既存の英語論文を推敲したいとき
• 中国語の学術原稿を英訳したいとき
• 根拠を保って結果や本文を短縮したいとき

Pythonの入力検証、例外の使い分け、バッチ処理の部分的な失敗への対処を整理し、原因や修正方法が伝わるエラー処理をアプリやAPIに実装するスキル。

• ユーザー入力やAPI引数を検証したいとき
• 例外の種類とエラーメッセージの設計
• 一部の失敗後もバッチを続けたいとき

Pythonのクラスや関数の責任を整理し、読みやすくテストしやすい設計を支援します。巨大な処理の分割や、継承・オブジェクトの組み合わせ・共通化の判断に使えます。

• Pythonの新規部品の責任を設計したいとき
• 巨大なクラスや関数を分割したいとき
• 重複処理を共通化する時期の判断

MinecraftのPaper・Spigot・Bukkit向けプラグインを開発・修正し、コマンド追加から対戦の進行管理、プレイヤーデータ保存までを支援します。

• サーバーコマンドやイベント処理の追加
• チーム戦やアリーナ型ミニゲームの実装
• クエストや成長データの保存

WindowsアプリのMicrosoft Store公開を支援し、認証設定からパッケージ作成、申請状況の確認、ベータ配信や段階的な公開まで扱うスキル。

• Store公開の認証設定をしたいとき
• Windowsアプリのパッケージ作成と申請
• 申請状況の確認と掲載情報の更新

Copilot CLIで定義への移動や参照検索、型情報の確認を使えるよう、言語解析用のLSPサーバーを導入します。OSと言語に合わせて設定を作成し、既存設定を保ちながら動作を確認します。

• Copilot CLIで定義移動や参照検索を使いたいとき
• 既存設定への対応言語の追加
• リポジトリで共有する言語サーバー設定

.NETアプリの時差変換や夏時間への対応を支援します。地名からのタイムゾーン特定、WindowsとIANAのID対応、用途に合うC#コード例を案内します。

• 都市や住所のタイムゾーンを調べたいとき
• C#でUTCと現地時刻を変換したいとき
• WindowsとLinuxの時差処理を共通化

Dynamics 365の財務・サプライチェーン導入に向け、対話で対象範囲や設計方針を整理するスキル。判断の理由と未決事項を残し、基本設計書にまとめます。

• D365導入の基本設計書を作りたいとき
• 導入範囲と展開順序を決めたいとき
• データ移行や連携方針の比較

M&Aや資金調達など複数案件の進捗、節目、期限、担当者付きタスクをExcelに整理。遅延や直近の予定を可視化し、週次の案件レビュー資料を作成します。

• 複数の進行案件を一覧で確認したいとき
• 入札期限や取引完了予定の管理
• 案件横断の担当タスクと期限の管理

上場企業の業績や企業価値の評価倍率を同業他社と比較し、中央値や四分位数、数式、データの出典を備えたExcelの分析表にまとめるスキル。

• M&Aで上場企業の評価を比較したいとき
• 同業他社と業績を比較したいとき
• 評価倍率の高低を調べたいとき

金利スワップの期間別金利を国債利回りや予想インフレ率と比較し、金利差や実質金利、カーブの形状を分析して取引案をまとめるスキルです。

• 期間別にスワップ金利と国債を比べたいとき
• 予想インフレ率から実質金利を求めたいとき
• カーブの傾きの変化を狙う取引の検討

投資先企業の月次・四半期の財務資料から主要指標を抽出し、予算や前期との差を分析。業績の推移、注意すべき項目、融資契約の財務条件を整理するスキル。

• 月次業績を予算と比較したいとき
• 取締役会向けの財務概要作成
• 融資契約の財務条件の確認

複数セッションのログや作業記録から、エージェントが繰り返しつまずく原因を調べ、自動チェック、指示文、ツール、情報へのアクセスの改善を提案するスキル。

• 複数セッションで続くエラーの調査
• 自動チェックの不足や未接続の確認
• ログやサービス状態へのアクセス改善

CopilotKit Inspectorの画面や操作を専用の検証環境で修正し、変更ごとのスクリーンショットで表示を確かめる、UI開発向けのスキル。

• Inspectorのパネルや操作を修正したいとき
• 狭い画面や折りたたみ表示の確認
• ライト・ダークテーマの表示比較

Springの設定値や管理オブジェクトであるBeanの登録状況をArthasで確認し、設定の出所、注入候補、登録元を読み取り中心の手順で調べるスキル。

• 設定値と設定元を確認したいとき
• 見つからないBeanを調べたいとき
• 依存関係の注入候補を探したいとき

Use when creating, extending, debugging or publishing a Lightweight Charts™ plugin — a custom series, a series primitive or a pane primitive — or when turning a chart idea ("I want the chart to show/draw X") into a working plugin. Covers choosing the plugin type, scaffolding with create-lwc-plugin, building on @tradingview/lwc-toolkit instead of hand-rolled helpers, which of the official plugin packages to read as the reference implementation, where the docs are, and the autoscale, whitespace, visible-range, hit-test and conflation mistakes every plugin author makes once. Reach for it whenever the user mentions a custom series, primitive, renderer, drawing on the chart canvas, lwc-plugin, plugin-examples, the toolkit, or the plugin catalog — even if they never say the word "plugin".

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。

Build a production 1:1 stablecoin swap with LI.FI Intents — an interface or backend where the amount the user sends equals the amount they receive (no visible gas or solver fees), same-chain or cross-chain, filled by LI.FI's solver network and verifiable on-chain. USE THIS SKILL WHEN USER WANTS TO: - Build a 1:1 stablecoin swap interface ("send 100 USDC, receive 100 USDC"), same- or cross-chain - Add stablecoin transfers with a guaranteed output amount to an app, backend, or bot - Build enterprise/fintech-grade transfers with no visible gas or solver fees for the end user - Implement the LI.FI Intents quote -> approve -> escrow open -> status loop for stablecoins - Move USDC / USDT 1:1 across chains under an onboarded LI.FI integrator - Understand how the LI.FI integrator ID / Partner Portal enables 1:1 quoting - Use the @lifi/intent TypeScript SDK to quote, build, open, and track an intent order This skill is self-contained and covers the integration end to end via the `@lifi/intent` SDK (the primary path), with the raw `order.li.fi` REST endpoints as a language-agnostic option. For the wider LI.FI product surface (classic swaps/bridges, Composer, Earn), use the `lifi` skill.

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。

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".

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 asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting multi-column regression tables to Word / Excel / LaTeX (Stata outreg2 / esttab / R modelsummary equivalent) and bundling an entire replication appendix into one .docx / .xlsx / .tex file. Triggers on keywords "StatsPAI", "statspai", "AER empirical analysis", "applied micro pipeline", "Table 1 balance", "event study", "first-stage F", "Oster bound", "honest_did", "spec_curve", "callaway_santanna", "dragonnet", "text as treatment", "outreg2 in Python", "regression table to Word/Excel", "sp.regtable", "sp.collect", "sp.paper_tables", "sp.feols", "summary_col", "modelsummary", "AER style table", "QJE style table", "epidemiology pipeline", "target trial emulation", "g-formula", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "TRIPOD", "公共健康", "流行病学", "DML", "double machine learning", "causal forest", "meta-learner", "CATE", "conformal causal", "policy learning", "因果机器学习", "ML causal", "decomposition", "Oaxaca-Blinder", "Kitagawa", "DiNardo-Fortin-Lemieux", "DFL", "Gelbach", "RIF decomposition", "wage gap decomposition", "sp.decompose", "sp.oaxaca".

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。

GitHub Actions CI/CD pipeline configuration for Golang projects — workflow files for test, lint, SAST, coverage and vulnerability-scan jobs, Dependabot and Renovate config files, GoReleaser release pipelines, Docker build/push, repository security settings, and AI-driven PR review. Use when setting up or improving Go project CI, writing or fixing `.github/workflows/*.yml`, adding a linter or security scanner as a pipeline job, wiring automated dependency-update bots, or adding quality gates. Covers wiring tools into a pipeline, not the analysis they perform: do NOT use for choosing or interpreting security findings (→ See `samber/cc-skills-golang@golang-security` skill) or for choosing, upgrading, or auditing dependency versions (→ See `samber/cc-skills-golang@golang-dependency-management` skill).

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。

Guides Stripe integration decisions across development and test environment planning (separate sandboxes vs the shared test mode sandbox), API selection (Checkout Sessions vs PaymentIntents), Connect platform setup (Accounts v2, controller properties), billing/subscriptions, tax and registrations (Stripe Tax, automatic_tax, product tax codes), Treasury financial accounts, integration options (Checkout, Payment Element), migrating from deprecated Stripe APIs, and security best practices (API key management, API key permissions, webhooks, OAuth). Use when planning, building, modifying, testing, or reviewing any Stripe integration, including choosing a development environment, accepting payments, building marketplaces, integrating Stripe, processing payments, setting up subscriptions, collecting sales tax, VAT, or GST, creating connected accounts, or implementing secure key handling.

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。

World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality monitoring, and DataOps. Use when designing data architectures, building batch or streaming data pipelines, optimizing data workflows, or implementing data governance.

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。

How episodic television actually gets made by a group (编剧室与制作现实) — merged from Goldberg & Rabkin (pitch as audition, leave-behind, cracking the story, beat sheet, notes discipline, production rewrites), Pamela Douglas (John Wells's room, staff-from-hell mistakes, the seven-rung ladder, step deal, the 14-day draft), Neil Landau's showrunner interviews (Lindelof, Mazzara, Katims, Bays), Evan Smith on sitcom staffing and roundtables, Richard Blum (dated), Kam Miller, and first-hand accounts by David Chase, Jesse Armstrong, Lucy Prebble, Aaron Sorkin, Julian Fellowes and Phoebe Waller-Bridge. Use when joining or running a writers' room, breaking an episode with a group, writing a leave-behind, beat sheet or outline on deadline, pitching episode ideas to an existing series, writing a spec episode, taking or giving notes, rewriting because of money, actors or locations, choosing between a staffed room and a single-author process, or asking 什么是 showrunner／编剧室怎么运作／怎么接 note.

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。

Orders and orients assembled contigs into chromosome-scale scaffolds from long-range linking data, inserting N-gap spacers (adds no sequence). Covers Hi-C/Omni-C scaffolding (YaHS, SALSA2, 3D-DNA/Juicer), Hi-C read-mapping prerequisites (map each end separately, no mate rescue, dedup, enzyme-aware), reading the contact map for misjoins/inversions/false-duplications, manual curation in Juicebox/PretextView (the VGP/DToL standard), reference-guided scaffolding (RagTag) and its karyotype-erasure hazard, genetic-map (ALLMAPS) and Bionano optical-map integration, chimera-breaking before scaffolding, gap-filling, and telomere/contig-vs-scaffold-N50 QC (tidk). Use when turning contigs into chromosomes with Hi-C, integrating a linkage map or optical map, choosing a scaffolder by available linking data, or judging whether a chromosome-scale assembly is 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。