8.3万 件 ・ 関連度順
概要と使いどころ
コミュニケーション診断ツール（commu-checker）の各タイプ向け、90日トレーニングコンテンツを生成するスキル。 ユーザーが「D1型のトレーニングを作って」「〇〇型の90日プランを作りたい」「training-data.jsに追加したい」 「コミュ診断のトレーニングページ」などと言ったときに必ず使う。 タイプ定義・観察メモを受け取り、既存サイト（M PLUS Rounded 1c・グリーン基調・Material Icons）に合わせた インタラクティブなチェックリスト用HTMLコンテンツを training-data.js の TRAINING_PAGES オブジェクトに追加する形で出力する。 トレーニングは trainings/index.html + trainings/training-app.js + trainings/training-data.js の3ファイル構成。 個別の <type>.html は存在せず、URL の ?type=d1 などのパラメータで表示タイプを切り替える。
Use whenever the user wants to find, shortlist, vet, or enrich US engineering firms — civil, structural, MEP, mechanical, electrical, geotechnical, transportation, environmental, and manufacturing. **For real-world engineering (buildings, infrastructure, manufacturing) — NOT software engineering.** Triggers on "find civil engineering firms in Florida for transportation", "shortlist three structural engineering firms with high-rise experience", "MEP consultancy for a hospital project", or "pull contact info for these 12 engineering firm domains", even when described indirectly (PE-stamped drawings, building-permit review). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer software-dev / "engineering team" / SaaS-architecture asks to find-software-developer. Skip in-house engineering-manager hires, DIY questions, software-product comparisons (Revit, AutoCAD), non-US firms, individual freelancers.
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。
機械学習モデルを本番で使うために、データの仕様、再現可能な学習、品質評価、配備、監視、切り戻しを整理し、実装計画やレビュー項目にまとめるスキル。
Phishing campaign execution methodology for authorized red team engagements. Covers end-to-end campaign lifecycle: infrastructure provisioning (GoPhish, SMTP relay configuration, domain acquisition and aging, SPF/DKIM/DMARC alignment), payload delivery vectors (Office macro weaponization, HTA droppers, ISO/IMG container abuse, LNK shortcut hijacking, OneNote embedded payloads, HTML smuggling), email authentication bypass techniques (SPF softfail exploitation, DKIM replay attacks, display name spoofing, homoglyph and cousin domain registration), credential harvesting with MFA bypass (EvilGinx2 transparent proxy, Modlishka session relay, pixel-perfect HTML cloning), spear phishing pretext development informed by OSINT, email security gateway evasion, QR code phishing (quishing), and callback phishing for initial access. Integrates with GoPhish for campaign management, EvilGinx2 for adversary-in-the-middle credential interception, King Phisher for template design, and the Social Engineering Toolkit for payload generation. Maps to MITRE ATT&CK T1566 (Phishing) and sub-techniques T1566.001 (Spearphishing Attachment), T1566.002 (Spearphishing Link), T1566.003 (Spearphishing via Service). Assumes you have written authorization and a defined scope before any campaign execution.
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 Metronome usage-based billing integration decisions — event ingestion (single and batch, idempotency, billable metrics), contract design (rate cards, overrides, dimensional pricing, products), invoicing lifecycle (grace periods, finalization, Stripe sync), credit and commit management (prepaid, postpaid, thresholds, auto-recharge), and Stripe integration (arrears invoicing, tax providers, line item limits). Use when building, modifying, or reviewing any Metronome integration — including ingesting usage events, creating customers, contracts, or rate cards, designing billable metric group keys, managing credits and commits, configuring invoicing, syncing invoices with Stripe Billing, billing AI applications for token usage, setting up self-serve (PLG) pricing, reviewing customer health, commit burn, portfolios, or renewals, or migrating from Stripe Billing Meters to Metronome.
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。
半导体芯片制造工艺 (Semiconductor Process Engineering / 半导体芯片制造工艺 — 以「物理化学极限 + 良率经济双重约束」为特征的集成电路前道/后道制造工艺认知操作系统：从「器件架构与微缩路线（planar→FinFET→GAA nanosheet→CFET、等效微缩 DTCO/STCO、背面供电 BSPDN）→ 工艺整合（FEOL/MOL/BEOL 模块协同、PDK、流片 tape-out）→ 单元工艺（光刻 litho（DUV 浸没式 193i / EUV 13.5nm / 多重曝光 SADP-SAQP / OPC / mask）、刻蚀 etch（RIE/ICP/原子层刻蚀 ALE）、薄膜沉积 depo（CVD/PECVD/LPCVD/ALD/PVD/外延 epi/电镀 Cu）、掺杂 doping（离子注入 ion implant / 退火 RTA-laser anneal）、CMP 化学机械抛光、清洗 cleaning）→ 量测检测 metrology（CD-SEM / OCD / overlay / 椭偏 ellipsometry / 缺陷检测 defect inspection / e-beam）→ 良率工程 yield（缺陷密度 / SPC 统计过程控制 / FDC / APC / DOE 实验设计 / 良率爬坡 yield ramp）→ 先进封装 advanced packaging（2.5D/3D、CoWoS、HBM、混合键合 hybrid bonding、chiplet、TSV）」的完整器件-工艺-良率-量产决策链。覆盖 (a) 第一性张力 — **物理/化学/材料的硬约束（量子隧穿、短沟道效应、RC 延迟、热预算、缺陷物理、ppb 级污染控制）⇄ 良率/成本/产能的经济约束（每片晶圆成本、良率是主经济指标、D0 缺陷密度、wafer-per-hour、折旧）**；**持续微缩/摩尔定律（dimensional + equivalent scaling、节点路线图 N5/N3/N2/A16/A14、high-NA EUV）⇄ 物理极限/微缩放缓（原子尺度、漏电、功耗墙、成本墙、'摩尔定律已死'之争）**；**协同优化/整合（DTCO 设计-工艺协同、STCO 系统-工艺协同、模块间 trade-off）⇄ 单模块极致优化（单一 unit process 工艺窗口）**；**工艺配方的 tacit 手艺（process recipe 调试、工艺窗口、缺陷根因分析、量产 ramp 的经验）⇄ 可建模/可仿真/数据驱动（TCAD 仿真、ML 良率分析、APC/FDC、数字孪生）**；**性能-功耗-面积-成本 PPAC 四角权衡 ⇄ 良率/可制造性 DFM 优先**；**自主可控/国产替代（卡脖子、EUV 出口管制、设备/材料/EDA 国产化、SMIC/长江存储/长鑫/中微/北方华创/上海微电子）⇄ 全球高度分工（ASML 光刻垄断、AMAT/Lam/TEL/KLA 设备、TSMC/Samsung/Intel 代工、全球供应链）**。(b) 核心工作流 / pipeline（最标准、最易蒸高质量 + CLI 化）— 新节点/新工艺导入：器件目标与 PPAC 定义 → 工艺整合方案与 PDK → 单元工艺开发与配方调试（DOE 找工艺窗口）→ 量测建立与 SPC → 良率爬坡（缺陷分类 + 根因分析 + 工艺改善）→ 量产移交与持续改善。具体单元工艺 SOP：光刻（曝光条件 / OPC / overlay 控制 / 多重曝光分解）、刻蚀（选择比 / profile / loading effect / ALE）、沉积（台阶覆盖 / 应力 / 均匀性）、CMP（去除速率 / 碟形 dishing / 侵蚀 erosion）、量测与缺陷分析、良率分析与 FA 失效分析。(c) 工具栈 — 设备（光刻 ASML/上海微电子 SMEE；刻蚀+沉积 AMAT/Lam/TEL/中微 AMEC/北方华创 Naura；离子注入 AMAT/Axcelis；CMP AMAT/荏原 Ebara；量测检测 KLA/AMAT/中科飞测/上海精测）、EDA/TCAD/仿真（Synopsys Sentaurus/Silvaco/Coventor SEMulator3D、OPC Calibre/Synopsys）、良率与制造软件（SPC、MES、FDC/APC、缺陷分析 yield management、PWS）、材料（光刻胶 photoresist、CMP 浆料 slurry、特气、靶材、掩膜 mask blank）。(d) 知识正典 — 半导体工艺 canon 横跨器件物理与制造工程：核心教材（Plummer/Deal/Griffin《Silicon VLSI Technology》、Sze《Physics of Semiconductor Devices》《VLSI Technology》、Wolf & Tauber《Silicon Processing for the VLSI Era》、Campbell《Fabrication Engineering at the Micro and Nanoscale》、May & Spanos《Fundamentals of Semiconductor Manufacturing and Process Control》、Quirk & Serda《Semiconductor Manufacturing Technology》、肖国勇/施敏中文教材）、路线图（ITRS/IRDS、IEEE 国际器件与系统路线图）、顶会论文（IEDM、Symposium on VLSI Technology、IRPS 可靠性、SPIE Advanced Lithography、ISSCC、DAC）、行业分析（SemiAnalysis、TechInsights、IC Knowledge）。(e) figures/流派 — 学术与器件奠基（Gordon Moore 摩尔、Robert Dennard 登纳德微缩、胡正明 Chenming Hu FinFET/UTB-SOI 发明人 UC Berkeley、刘明 Tsu-Jae King Liu Berkeley、Krishna Saraswat Stanford、Mark Bohr Intel 工艺架构）、产业领袖与工艺大师（张忠谋 Morris Chang TSMC、林本坚 Burn-Jeng Lin 浸没式光刻、Martin van den Brink ASML、米玉杰/梁孟松 SMIC/三星工艺、Mark Liu/魏哲家 TSMC）、行业分析师（Dylan Patel SemiAnalysis、Scotten Jones IC Knowledge、TechInsights 拆解、Handel Jones）、科普与拆解（Asianometry、The Chip Letter、半导体行业观察）。流派分歧：等效微缩派 vs 摩尔定律已死派、前道微缩 vs 先进封装 chiplet 异构集成、dimensional scaling vs DTCO 协同优化、IDM vs 纯代工 foundry 模式、自主可控/国产替代路线 vs 全球分工。(f) 行业话术/黑话 — 制程节点 node / 流片 tape-out / 工艺窗口 process window / 良率 yield / 缺陷密度 D0 / 关键尺寸 CD / 套刻 overlay / 掩膜 reticle-mask / 光刻胶 PR / 浸没式 immersion / EUV/DUV / 多重曝光 multi-patterning SADP-SAQP / OPC / 刻蚀选择比 selectivity / profile / loading effect / 原子层刻蚀 ALE / 台阶覆盖 step coverage / 外延 epi / 离子注入 implant / 退火 anneal / RTA / CMP / dishing-erosion / FEOL-MOL-BEOL / FinFET / GAA / 纳米片 nanosheet / CFET / 背面供电 BSPDN / high-NA / PPAC / DTCO-STCO / DFM / SPC / FDC-APC / DOE / WAT-PCM / 良率爬坡 yield ramp / FA 失效分析 / particle 颗粒 / 洁净室 cleanroom-级别 / 12寸-8寸 wafer / chiplet / CoWoS / HBM / 混合键合 hybrid bonding / TSV / PDK / 等效微缩. (g) 争议/批判 — 「摩尔定律是否已死 vs 等效微缩继续」、「节点命名营销化（N3/N2 ≠ 物理 3nm/2nm，gate pitch/metal pitch 才是真尺度）」、「前道微缩 vs 先进封装异构集成谁是未来」、「自主可控/国产替代的现实（EUV 卡脖子、设备/材料/EDA 国产化进度、出口管制 entity list、良率与成熟度差距）」、「良率/成本/性能三角权衡」、「人才稀缺与工艺 know-how 的 tacit 性」、「中国大基金/产能扩张是否过剩」、「制裁与技术封锁」。(h) 大量隐性工艺手艺（process recipe 调试与工艺窗口寻找、缺陷根因分析与 FA、量产良率爬坡的经验判
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。
Your competitor just launched. You have no idea how they grew so fast. Should you reverse-engineer their website? Track their social media? Map their growth flywheel? This gives you the complete SOP — from Wayback Machine snapshots to X/Twitter propagation analysis to growth flywheel scoring. Built from 150+ AI startup competitive analyses including the Lovable case study (4.3M views, 229K impressions launch day). By @WeiYipei. What's inside: • 4-step research framework (website teardown → social media → traffic sources → ad spend) • 3-version website evolution analysis (V1 / Beta / Launch via Wayback Machine) • X/Twitter propagation chain mapping (4-phase model + single-post teardown template) • Growth flywheel 6-stage scoring system (Activation → Referral → Acquisition → Retention → Revenue → Product) • KOL identification framework (Traffic King / Topic Starter / Mindset Shifter) • Content effectiveness ranking (validated: Visual Demo > User Story > Competitor Comparison) • ICP deep analysis template + Freemium pricing teardown • Complete self-check checklist 🇨🇳 你的竞品刚刚爆了。你完全不知道他们怎么做到的。该拆官网？盯社媒？画增长飞轮？这份 SOP 给你从 Wayback Machine 快照到 X/Twitter 传播链路还原到飞轮六阶段评分的完整方法论。基于 150+ AI 创业公司竞品分析实战，含 Lovable 完整案例。 🇯🇵 競合が突然バズった。どうやって成長したか全く分からない。このSOPは、Waybackスナップショットからツイッター伝播チェーン分析、フライホイール6段階スコアリングまでの完全な競合調査フレームワークを提供します。150以上のAIスタートアップ分析から構築。 🇰🇷 경쟁사가 갑자기 폭발적으로 성장했습니다. 어떻게 그렇게 빨리 성장했는지 모릅니다. 이 SOP는 Wayback 스냅샷부터 X/Twitter 전파 체인 분석, 플라이휠 6단계 스코어링까지 완전한 경쟁사 조사 프레임워크를 제공합니다. 150개 이상의 AI 스타트업 분석에서 구축. Triggers: "competitor research" | "competitive analysis" | "competitor analysis" | "growth flywheel" | "market research" | "竞品调研" | "竞品分析" | "增长飞轮" | "传播链路" | "競合分析" | "경쟁사 분석" | "social media teardown" | "Twitter propagation" | "content strategy analysis"
🇺🇸 KOL Outreach & Influencer Marketing Playbook — Complete SOP from discovery to ROI tracking. Battle-tested with 200+ KOL campaigns at AFFiNE (60k stars). Includes pricing benchmarks, email templates, content packages, platform algorithm guides, and data-driven evaluation frameworks. 🇨🇳 KOL 外联与红人营销实战手册 — 从发现筛选到 ROI 追踪的完整 SOP。基于 AFFiNE（60k stars）200+ 次 KOL 合作实战验证。包含报价基准、邮件模板、内容包模板、各平台算法指南、数据评估体系。 🇯🇵 KOLアウトリーチ＆インフルエンサーマーケティング実践ガイド — 発見からROI追跡までの完全SOP。AFFiNE（60k stars）での200回以上のKOLキャンペーン実績。価格ベンチマーク、メールテンプレート、コンテンツパッケージ、プラットフォームアルゴリズムガイド。 🇰🇷 KOL 아웃리치 & 인플루언서 마케팅 플레이북 — 발굴부터 ROI 추적까지 완전한 SOP. AFFiNE(60k stars) 200+ KOL 캠페인 실전 검증. 가격 벤치마크, 이메일 템플릿, 콘텐츠 패키지, 플랫폼 알고리즘 가이드. Triggers: "KOL outreach" | "influencer marketing" | "KOL campaign" | "influencer outreach" | "creator partnership" | "KOL strategy" | "red人" | "红人营销" | "KOL合作" | "达人合作" | "influencer pricing" | "KOL ROI" | "content creator outreach" | "KOL筛选"
Sales enablement, technical presales and deal acceleration for B2B SaaS revenue teams. Use this skill when planning outbound campaigns, running discovery calls, building deal strategy, creating battle cards, writing proposals, responding to RFPs, preparing a product demo, scoping a proof of concept or pilot, answering a security questionnaire or the AI annex buyers now attach to it, executing sales sequences, coaching qualification, or deciding what the website's AI chat agent is allowed to say. Also triggers on: outbound, cold outreach, reply handling, reply classification, out-of-office replies, auto-replies counted as replies, our reply rate is inflated, prospect replied remove me, opt-out by reply, is this list ready to send, launch readiness, CRM segment outbound, we emailed a current customer, outbound hit an open opportunity, ownerless leads, prospects engaging our competitors, competitor connections, displacement list, is this personalization creepy, can we mention they visited our site, signal freshness, deal strategy, pipeline, battle card, objection handling, common objections, why do we keep hearing this objection, mine call transcripts for objections, objection register, proposal, RFP, discovery call, MEDDPICC, sales sequence, qualification, sales engineer, solutions engineer, presales, demo plan, demo script, POC, proof of concept, pilot success criteria, technical evaluation, technical win, security questionnaire, vendor security review, SOC 2 questionnaire, AI security questionnaire, AI annex, AI vendor due diligence, the buyer is asking if we train on their data, do you train on customer data, which model do you use, model provider sub-processor, AI subprocessor disclosure, where does inference run, can the customer turn the AI feature off, AI-CAIQ, CSA AI Controls Matrix, ISO 42001, ISO/IEC 42001, NIST AI RMF, EU AI Act Article 50, are we a provider or a deployer, AI feature disclosure, our model vendor changed and nobody told the buyer, technical objection, mutual action plan, MAP, close plan, mutual close plan, buyer enablement, the deal is stalled, deal stalled after the demo, how do I get this deal to close, close date keeps slipping, JOLT, buyer indecision, no-decision loss, website chatbot, chat widget, AI chat agent, AI SDR, inbound AI SDR, conversational marketing, live chat, what should our chatbot be allowed to say, our chatbot quoted the wrong price, the bot promised a discount, do we have to disclose it is AI, EU AI Act chatbot disclosure, chatbot handoff to sales, chat transcript review, prompt injection on our chatbot, Drift, Qualified, Intercom.
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。
PyTorchのモデルや学習コードを作成・点検し、データ読み込み、実験の再現性、学習状態の保存、GPUメモリや処理速度の改善を支援するスキル。
Kotlinのコード作成・レビュー・改修を支援します。nullの扱い、不変データ、非同期処理、型安全な設定記述を見直し、読みやすく保守しやすい構成に整えます。
Complete bug bounty workflow — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation gates, human-tone writing, templates by vuln class, CVSS 3.1, PoC generation, always-rejected list, conditional chain table, submission checklist). Use for ANY bug bounty task — starting a new target, doing recon, hunting specific vulns, auditing source code, testing AI features, validating findings, or writing reports. 中文触发词：漏洞赏金、安全测试、渗透测试、漏洞挖掘、信息收集、子域名枚举、XSS测试、SQL注入、SSRF、安全审计、漏洞报告
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。
Complete bug bounty workflow — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation gates, human-tone writing, templates by vuln class, CVSS 3.1, PoC generation, always-rejected list, conditional chain table, submission checklist). Use for ANY bug bounty task — starting a new target, doing recon, hunting specific vulns, auditing source code, testing AI features, validating findings, or writing reports. 中文触发词：漏洞赏金、安全测试、渗透测试、漏洞挖掘、信息收集、子域名枚举、XSS测试、SQL注入、SSRF、安全审计、漏洞报告
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。
One full-bleed launch hero in two inks from the project's DESIGN.md: the base color and exactly one brand color, as a slowly folding ordered-dither (Bayer) field — a print/riso look, not a generic gradient. Two flat inks never blend, so there is no mud. The copy sits on a hard-edged base-ink knockout, so headline and sub copy both clear 4.5:1 by construction, and a verify script measures it across the whole timeline. Reads Core v2 tokens (and Google-compatible frontmatter), refuses to invent a palette, ships a CSS halftone poster fallback, respects prefers-reduced-motion, ~7 KiB gzip of library JS. Use for a launch page, product hero, waitlist, event or rebrand first screen that needs motion in brand color. Triggers: '셰이더 히어로', '런칭 히어로', '디더링 배경', '리소 느낌 히어로', '브랜드 컬러 배경 애니메이션', 'dither hero', 'shader hero from DESIGN.md', 'launch hero background', 'riso print hero', 'シェーダーのヒーロー', '著陸頁主視覺 shader'. Whole scroll-native landing → omd-landing / omd-aphrodite. CJK headline ink effect → omd-cjk-type-shader. Brand tokens missing → omd-init first.
One full-bleed launch hero in two inks from the project's DESIGN.md: the base color and exactly one brand color, as a slowly folding ordered-dither (Bayer) field — a print/riso look, not a generic gradient. Two flat inks never blend, so there is no mud. The copy sits on a hard-edged base-ink knockout, so headline and sub copy both clear 4.5:1 by construction, and a verify script measures it across the whole timeline. Reads Core v2 tokens (and Google-compatible frontmatter), refuses to invent a palette, ships a CSS halftone poster fallback, respects prefers-reduced-motion, ~7 KiB gzip of library JS. Use for a launch page, product hero, waitlist, event or rebrand first screen that needs motion in brand color. Triggers: '셰이더 히어로', '런칭 히어로', '디더링 배경', '리소 느낌 히어로', '브랜드 컬러 배경 애니메이션', 'dither hero', 'shader hero from DESIGN.md', 'launch hero background', 'riso print hero', 'シェーダーのヒーロー', '著陸頁主視覺 shader'. Whole scroll-native landing → omd-landing / omd-aphrodite. CJK headline ink effect → omd-cjk-type-shader. Brand tokens missing → omd-init first.
Configures deep linking in Flutter using Android App Links (assetlinks.json) and iOS Universal Links (apple-app-site-association) integrated with GoRouter v17.x navigation. Use this skill when setting up HTTPS domain association, handling magic links or email verification links, routing marketing campaign URLs (Google Ads, Facebook Ads) into the app, implementing password reset or invite flows via deep links, fixing deep link verification failures, troubleshooting GoRouter deep link integration, or any time the words 'deep link', 'universal link', 'app link', 'assetlinks', 'AASA', 'magic link', or 'deeplinking' appear in the request. Avoids deprecated custom URL schemes (myapp://).
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。
Facilitate DDD domain modeling sessions via EventStorming conversation and DML (Domain Modeling Language). Use whenever the user wants to model a business domain by discovering domain events, commands, aggregates, policies, read models, and bounded contexts through dialogue. Produces incrementally-built DML as the primary artifact, plus a Markdown session report. Also invoke for refining existing DML, mapping out a new feature's domain model, or when the user says "ドメインモデリングしたい", "イベントストーミング", "DDDで整理したい", "DMLを育てたい".
予約済みのItô GPU環境で機械学習の訓練が利用可能かを確認するスキル。現状は訓練を実行せず、未提供の機能と将来の引き渡し条件を整理します。
This skill is designed to help users automatically extract business data from Google Maps search results. The Agent should proactively apply this skill when the user makes the following requests searching for coffee shops in a specific city, finding dentists or medical clinics nearby, tracking competitors' locations in a certain area, extracting business leads from Google Maps lists, gathering restaurant data for market research, finding hotels or accommodation options in a region, locating specific services like coworking spaces or gyms, monitoring new business openings in a neighborhood, collecting contact information and addresses for sales prospecting, analyzing price ranges and cuisines of local eateries, getting ratings and review counts for a list of businesses, exporting local business data into a CRM or database.
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。
Scrape job listings from Indeed.com by keyword, location, and country. Returns job title, company, salary, rating, description, benefits, and apply links. Use when user mentions Indeed, Indeed scraper, Indeed jobs, scrape Indeed, job search Indeed, Indeed job listings, extract Indeed data, Indeed job data, get jobs from Indeed, Indeed employment data, job market research Indeed, Indeed salary data, bulk job extraction Indeed, Indeed job scraper, monitor Indeed listings, Indeed hiring data, job postings Indeed, Indeed career search. Also applies to: job market analysis, salary benchmarking from Indeed, competitor hiring monitoring, recruitment data collection, building job databases from Indeed search results.
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。
Create LinkedIn ad campaigns, write sponsored content, define professional audiences, and plan B2B advertising budgets. Use when the user asks about LinkedIn Ads, sponsored content, LinkedIn Campaign Manager, InMail ads, lead gen forms, B2B advertising, professional targeting, or LinkedIn retargeting. Trigger phrases include "LinkedIn Ads", "LinkedIn campaign", "sponsored content", "InMail ad", "LinkedIn lead gen", "B2B ads", "LinkedIn retargeting", "LinkedIn audience", "LinkedIn ad copy", "LinkedIn Campaign Manager".
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 deploying a trained Einstein Discovery model to production records declaratively — activating a prediction definition, mapping output fields to page layouts, adding the Einstein Discovery Action to a Flow, running bulk predict jobs, and monitoring model health via Model Manager UI. Trigger keywords: prediction definition activation, bulk predict job, Einstein Discovery Flow action, Model Manager, prediction field mapping, model refresh activation, scoring job, Einstein Discovery recommendations on record. NOT for authoring the story in CRM Analytics Studio — use admin/einstein-discovery-setup. NOT for programmatic scoring through the API — use agentforce/einstein-discovery-development.
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。
Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.
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。
Detects acquired antimicrobial-resistance determinants and chromosomal point-mutation resistance in bacterial assemblies using AMRFinderPlus, ResFinder 4.0 (acquired + PointFinder), CARD-RGI, abritAMR, staramr, and species-specific callers (TB-Profiler, Mykrobe). Harmonises cross-tool output via hAMRonization, contextualises determinants with mobile-genetic-element annotation (MOB-suite, PlasmidFinder, MobileElementFinder, ICEberg), predicts phenotype against EUCAST or CLSI breakpoints, and translates calls into WHO GLASS reporting categories. Use when screening clinical or surveillance isolates for AMR, distinguishing acquired vs intrinsic vs point-mutation resistance, calling rpoB / katG / pncA / gyrA / mgrB mutations, reconciling AMRFinderPlus vs RGI vs ResFinder disagreement, contextualising carbapenemases or mcr alleles on plasmids, predicting susceptibility from genotype against the WHO Mtb 2nd-edition catalogue, or building a hAMRonized multi-lab AMR surveillance pipeline.
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。