「in」の検索結果

複数の配信元から脅威情報をMISPへ集め、侵害の手掛かりとなるIPアドレスやドメインを一元管理します。自動同期や情報同士の関連付け、監視基盤への連携を支援します。

• 複数の脅威情報を一元管理したいとき
• DockerでのMISP導入
• 脅威情報の同期を自動化したいとき

TLS証明書の公開発行記録を検索し、ブランド名に似せたドメインや想定外の発行元を確認して、フィッシングや未把握のIT利用の兆候を調べます。

• ブランドを装うドメインの調査
• 想定外の証明書発行元の確認
• 未把握のドメインやIT資産の調査

OSの再インストール後も残る感染を調べるため、起動領域やUEFIファームウェア、メモリを解析し、ブートキットやルートキットの痕跡と復旧方針を整理するスキル。

• OS再インストール後も残る感染の調査
• Secure Bootの整合性異常の確認
• 隠れたプロセスや不審なドライバーの調査

開発アイデアを、構成図からファイル単位の実行手順まで具体化する計画用スキル。調査の根拠や設計判断をMarkdownの知識ベースに残し、矛盾の点検や次回の再開を支援します。

• 実装前にシステム全体を設計したいとき
• ファイルと依存関係を含む作業分解
• 設計の抜けや矛盾を点検したいとき

検索からの流入を増やすために、キーワード選び、記事構成、ページ内の設定や表示速度を見直すスキル。コンテンツと技術の両面から改善案を整理します。

• キーワード選定と関連記事の企画
• タイトル・概要文・見出しの見直し
• 構造化データや検索登録設定の確認

LLMを8-bit・4-bitに量子化し、GPUメモリの使用量を抑えます。モデルの読み込みからQLoRAによる追加学習、学習時のメモリ節約まで支援します。

• 限られたGPUメモリでモデルを動かしたいとき
• 8-bitと4-bitの設定比較
• QLoRAで追加学習したいとき

LLMを用途に合わせて追加学習するために、Unslothの導入やAPIの利用、コードの問題解決を支援。LoRA・QLoRAによる学習の高速化と省メモリ化も扱います。

• Unslothで追加学習を始めたいとき
• LoRA・QLoRAの学習方法を確認したいとき
• UnslothのAPIを使って実装したいとき

LLMを用途に合わせて追加学習するために、LLaMA-Factoryの画面操作や省メモリ手法の利用、機能の確認、実装と不具合調査を支援するスキル。

• 画面操作でLLMの追加学習を試したいとき
• LoRAやQLoRAの設定を確認したいとき
• LLaMA-Factoryの不具合を調べたいとき

会話履歴を扱うチャットボットや、文書検索を組み合わせて回答するAIアプリの開発を支援します。モデルの切り替え、外部ツールの呼び出し、実行状況の確認まで具体例で案内します。

• 会話履歴を使うチャットボット開発
• 文書に基づく質問応答を作りたいとき
• 外部ツールを使うエージェント開発

cogneeで人物や役割などの情報と関係を独自に定義し、同じ対象の重複を防ぐ設定や検索対象の指定、関係が保存されない原因の調査を支援するスキル。

• 独自の情報と関係を設計したいとき
• 同じ対象の重複登録を防ぎたいとき
• 検索漏れや関係の欠落を調べたいとき

資金調達の段階や事業分野に合わせ、課題・市場・実績・チーム・調達額を投資家に伝えるPowerPoint資料の構成と図表を整えます。

• Seedの資金調達資料を作りたいとき
• Series Bの成長指標を説明したいとき
• 先端技術の開発実績を伝えたいとき

CSVや表データから、数式で集計するKPIカード、複数のグラフ、条件付き書式を備えたExcelダッシュボードを作り、主要指標を一画面にまとめます。

• 月次売上と推移をまとめたいとき
• 経営指標を一つのシートに集約
• 予算と実績を比較したいとき

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。

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。

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。

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。

Estimates time-scaled phylogenies, molecular-clock rates, effective reproduction number R_e, and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict/relaxed clocks; coalescent, Bayesian-Skyline, Skygrid, Birth-Death-Skyline, and sampled-ancestor priors; structured coalescent via MASCOT). Covers root-to-tip clock QC via TempEst, date-randomisation tests, recombination masking via Gubbins/ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction, multi-chain convergence diagnostics, and reconciling phylodynamic R_e with case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline models, diagnosing temporal-signal failure, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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。

Analyzes base-editing screens for variant function. Covers library design (Hanna 2021 ClinVar-scale CBE screen benchmarked on BRCA1/2, Cuella-Martin 2021 DDR saturation), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end; 4-7 for ABE7.10), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.

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。

Outside counsel guidelines (OCGs) — drafting, review, gap analysis, and targeted updates for in-house legal teams. Build new OCGs calibrated to maturity level (light, standard, comprehensive), review existing OCGs against 15-component best practice framework, add or update specific sections (AI policy, AFA terms, D&I, rate review), and produce GC briefing notes for internal buy-in. Trigger on: 'draft billing guidelines', 'outside counsel guidelines', 'OCG', 'billing expectations', 'review our guidelines', 'update the AI clause', 'what should our billing guidelines cover', 'we don't have billing guidelines', 'firms aren't following our guidelines', 'rate review terms', 'GC briefing on billing guidelines', 'AFA policy', 'block billing', 'prohibited fees', 'staffing expectations for outside counsel'.

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。