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商品を紹介するクリエイターとの提携を、候補選びから報酬・利用権の整理、制作依頼、広告表示、効果測定まで設計します。単発の協賛や継続的なアンバサダー施策に使えます。

• 提携するクリエイターを選びたいとき
• 報酬や納品物、利用権を整理したいとき
• 制作依頼書と広告表示を確認したいとき

起動中のiTerm2にコマンドを送り、実行状況や過去の出力を確認するほか、ウィンドウ・タブ・分割ペインの配置や設定変更をコマンドから行うスキル。

• 全ペインの実行状況を把握したいとき
• コマンドを送り出力履歴を読みたいとき
• 複数ペインの配置を作成・復元したいとき

機械学習モデルの予測に各入力項目がどう寄与したかを計算・可視化します。SHAPの手法や基準データを選び、個別の予測とモデル全体の傾向を検証しながら説明します。

• 一件の予測に寄与した項目の説明
• 評価データ全体の寄与傾向を可視化
• 多クラスモデルの説明対象を選びたいとき

実験動物の体重・体温・臨床スコアなどを基準集団に対する負担スコアへまとめ、個体ごとの推移、指定時点の探索的な予測、注意域の候補を分析するスキル。

• 動物の観測指標を日別スコアにまとめたいとき
• 共通の基準で群間の負担を比較したいとき
• 指定観測時点の負担を探索的に予測したいとき

タンパク質発現やRNA合成などの実験をGinkgo Cloud Labへ依頼するために、手法の選択から入力資料の準備、料金確認、ブラウザでの注文まで支援するスキル。

• タンパク質発現・精製手法の選択
• mRNA・circRNA合成の入力準備
• 熱安定性試験や測定法導入の検討

公開された遺伝子変異の証拠や関連論文、遺伝子と疾患の関係を調べ、出典と検索結果の状態を保ちながら、専門家による確認に向けて整理するスキル。

• 単一変異の公開証拠を確認したいとき
• 特定した変異の関連論文を探したいとき
• 遺伝子と疾患の関係の調査

炭素13の標識測定から細胞内の代謝反応速度を推定し、原子の対応関係や入力条件を点検して、測定データでどの経路の流量まで絞り込めるかを調べるスキル。

• 炭素13の測定から代謝流量を推定したいとき
• 原子対応表と測定入力の整合性確認
• 仮定した反応速度から標識分布を予測

経歴や希望条件に合う新着求人を複数の求人サイトから探し、既出・応募済みの重複を除いて適合度順に整理し、応募期限や確認すべき条件を一覧で示すスキル。

• 経歴に合う新着求人を探したいとき
• 既出・応募済み求人を除いた比較
• 特定分野や全カテゴリでの求人探し

GitHub Actionsで動くAIエージェントに外部入力が届く経路や権限設定を調べ、攻撃につながる問題を根拠・影響・修正案とともに報告するスキルです。

• GitHub ActionsのAI連携を監査したいとき
• IssueやPR本文の流れを追いたいとき
• サンドボックスとツール権限の点検

AirflowでJavaやKotlinなどのタスクを動かすSDKの開発を支援し、公開APIの境界、JARの構成、通信仕様の更新、テスト手順を案内します。

• Java SDKの機能追加や不具合修正
• 公開APIと内部実装の境界を確認したいとき
• JARの検出やJVMの起動失敗を調べたいとき

Airflowに新しい言語の実行環境を追加するため、SDKの構成や通信方式、実行用ファイルの形式、テスト、PRに必要な変更を案内するスキルです。

• 新しいプログラミング言語を追加したいとき
• 言語の実行方式に合う構成を選びたいとき
• 通信処理や実行用ファイルを実装したいとき

LLMを学習例で追加学習するLoRA・QLoRAの設定を作成・点検します。学習対象の層やランク、学習率を整理し、GPUメモリに応じた方式選択も支援します。

• 教師あり追加学習のLoRA設定作成
• ランク・alpha・学習率の見直し
• GPUメモリに応じた方式選択

数学・コード・構造化出力など、正誤をプログラムで判定できる課題向けに、GRPOによる強化学習の設定と報酬関数を整え、学習の不調や報酬の抜け穴を点検するスキル。

• 自動で正誤判定できる課題のGRPO設定
• 出力形式と正確さの報酬設計
• 学習開始前の報酬スコア点検

メモリの保存データからプロセスや通信、不審なコードを調べ、侵害調査を支援。RAMの取得からVolatilityによる解析、証拠の抽出と記録まで扱います。

• 調査対象の電源を切る前にRAMを保存したいとき
• 不審なプロセスや通信を調べたいとき
• マルウェア解析用のファイルや文字列の抽出

Design and execute a social engineering penetration test combining OSINT-driven target profiling with phishing, vishing, smishing, and physical pretexting campaigns using tools like GoPhish, the Social Engineer Toolkit (SET), and Evilginx to measure human security resilience. Use when scoping or running an authorized human-attack-surface test and reporting results to identify security awareness training gaps.

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。

Opinionated Rails conventions: rich models, concerns, CRUD-everything, state-as-records, minimal dependencies, Minitest with fixtures. Load this skill BEFORE any code-level thinking, not only before editing a file. It is required the moment a task touches Rails code in ANY way: designing or even just discussing a data model, schema, migration, entity, association, field, validation, class, or method name; writing, planning, reviewing, analyzing, testing, debugging, or refactoring; or proposing any model, table, column, route, or code snippet inline in chat. If you are about to name a model or sketch a column you are already in scope, even in an exploratory back-and-forth where no file is written yet. Do not let a "we're just discussing" framing defer it. Do NOT use for non-Rails backends, NestJS, or general architecture (use nestjs-modular-monolith or coding-guidelines).

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 Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **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 Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `teffects ipw` / `teffects ipwra` / `teffects aipw` / `eltmle`, Mendelian randomization via `mrrobust` (IVW / Egger / weighted median) and `mregger` / `mrpresso`, KM / Cox / AFT / RMST survival via `sts` / `stcox` / `streg` / `strmst2`, E-value sensitivity via `evalue` (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `ddml` / `pdslasso`, S/T/X/R/DR meta-learners via `crforest` and `ddml interactive`, causal forest via `crforest` / `cforest`, BART/BCF via `bart` / `bartCause`-style externals, CATE distribution + policy tree via `crforest`, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via `pcalg` / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".

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。

Configures AWS Transit Gateway: creating a hub and attaching VPCs, segmenting traffic with route tables, centralizing egress and inspection through a hub (appliances or a Gateway Load Balancer endpoint), forcing east-west traffic between VPCs through AWS Network Firewall, connecting on-premises networks over the transit-gateway side of a Site-to-Site VPN or Direct Connect attachment (including ECMP to aggregate bandwidth across multiple VPN tunnels), peering transit gateways across Regions, migrating from a VPC peering mesh, and routing IP multicast. Applicable when connecting many VPCs through one router, isolating environments, forcing VPC-to-VPC traffic through a central Network Firewall, reaching on-premises over the hub, linking Regions, or moving off a peering mesh. Not applicable for single-VPC routing, VPC peering between two VPCs (vpcpeering skill), Direct Connect gateway or virtual interface setup (directconnect skill), or Route 53 DNS work.

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。

MANDATORY for Flink or Amazon Managed Service for Apache Flink (MSF) questions. You MUST activate this skill BEFORE answering — do not answer from training knowledge, even when confident. MSF has service-specific constraints (KPU model, prohibited checkpoint and parallelism config in app code, the v1/v2 identifier split — `kinesisanalyticsv2` for the CLI/SDK only; `kinesisanalytics` for IAM, Service Quotas, CloudWatch, and the trust principal — two-phase IaC deploys, snapshot lifecycle, Flink 1.x→2.x migration) that override generic Flink knowledge. Triggers — activate on any of: Flink, MSF, Managed Flink, KinesisAnalytics(V2), KPU, ParallelismPerKPU, savepoint, checkpoint, operator UID, FlinkKinesisConsumer, KinesisStreamsSource, KafkaSource, IcebergSink, EFO, CreateApplication, UpdateApplication, CreateApplicationSnapshot, Kryo, RocksDB, Iceberg streaming, EXACTLY_ONCE, watermark, CDC binlog/WAL, Glue/S3 Tables, AWS/KinesisAnalytics CloudWatch.

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。

Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.

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。

Set up signal capture for a Simulink Real-Time application using `slrealtime.Instrument`. Use when choosing which signals a real-time application captures and where the data goes — test-pointing or canvas-badging a signal before build (`TestPoint`, `DataLogging`), constructing an Instrument from an `.mldatx`, calling `addSignal`, `addInstrumentedSignals`, `addFileLogSignals`, `validate`, `connectCallback`, `copy`, or `generateScript`, attaching an instrument to `tg.Instruments` via `addForFileLog`, `addForSDI`, or `addForMATLAB`, setting up live-streaming signals, dynamic file-log workflows, or persisting an instrument as M-code. Selects and routes signals only — not for configuring, building, running, or connecting applications onto targets.

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。

Designs pegRNAs and nicking guides for prime editing (PE) -- choosing the nick/strand, tuning the primer-binding site (PBS) and reverse-transcription template (RTT) as a per-locus panel, selecting the PE system (PE2/PE3/PE3b/PE4/PE5/PEmax/PE7), adding MMR-evading and PAM-disrupting silent edits, appending epegRNA 3' motifs (tevopreQ1/mpknot), and ranking with PRIDICT/DeepPrime. Covers twinPE/PASTE for large insertions and the prime-vs-base-editing decision. Use when designing a scarless point mutation, small insertion/deletion, or any of the 12 base conversions without a double-strand break, when efficiency is low and MMR inhibition or pegRNA stabilization is needed, or when routing a large insertion to an integrase method. Generic guide scoring and base editing are separate 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。

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。

Use when you need to apply Java secure coding best practices — including validating untrusted inputs, defending against injection attacks with parameterized queries, minimizing attack surface via least privilege, applying strong cryptographic algorithms, handling exceptions securely without exposing sensitive data, managing secrets at runtime, avoiding unsafe deserialization, and encoding output to prevent XSS. This should trigger for requests such as Review Java code for secure coding; Find input validation risks in Java code; Review Java code for injection vulnerabilities; Improve secure error handling in Java services; Harden Java code against common security flaws. Part of Plinth Toolkit

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。