1,642 件 ・ 関連度順
概要と使いどころ
Ongoing vendor privacy compliance monitoring program. Covers annual reassessment procedures, continuous monitoring signals, contract renewal privacy triggers, performance metrics, KPIs, and vendor governance reporting dashboards.
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 implementation of the NIST Privacy Framework IDENTIFY function covering ID.BE business environment, ID.DA data actions, ID.IM improvement, and ID.RA risk assessment subcategories. Maps NIST PF controls to GDPR requirements for dual-framework compliance. Keywords: NIST Privacy Framework, IDENTIFY function, ID.BE, ID.DA, ID.IM, ID.RA, privacy risk assessment, data actions.
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。
Assesses the GDPR Article 30(5) exemption for organisations under 250 employees. Covers the three exception conditions that negate the exemption: non-occasional processing, risk to data subject rights, and special category data processing. Activate for Art. 30(5), 250 employee exemption, small business RoPA, SME exemption, occasional processing.
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。
Implement the NIST Privacy Framework IDENTIFY function including ID.BE business environment, ID.DA data actions, ID.IM improvement, and ID.RA risk assessment subcategories. Provides control mapping, gap analysis templates, and implementation workflows for privacy risk identification.
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。
Preparation guide for ISO 31700 privacy by design for consumer goods certification. Covers the 30 requirements across design, production, and disposal phases. Includes gap assessment methodology, remediation planning, and mapping to GDPR Article 25 data protection by design obligations for consumer-facing products and services.
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 privacy impact assessment for emerging technologies including IoT, blockchain, AR/VR, quantum computing, and digital twins. Covers risk identification methodology, proportionality assessment, and technology-specific privacy challenges. Activate when evaluating new technology adoption, innovation projects, or emerging tech procurement. Keywords: PIA, emerging technology, IoT, blockchain, AR/VR, quantum computing, digital twins, innovation privacy.
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。
Provides a structured risk scoring methodology for Data Protection Impact Assessments aligned with ENISA threat taxonomy and ISO 29134. Covers likelihood and severity assessment, risk matrix construction, inherent vs residual risk calculation, and risk appetite thresholds per EDPB WP248rev.01 guidance. Keywords: risk scoring, DPIA risk matrix, likelihood, severity, ENISA, ISO 29134, residual risk, risk appetite.
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 assessment and use of the EU-US Data Privacy Framework adequacy decision for transatlantic data transfers. Covers DPF self-certification with the Department of Commerce, DPF principles compliance, Data Protection Review Court, and annual EC review. Keywords: DPF, EU-US, adequacy, Privacy Shield, transatlantic transfers.
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。
Manages the organisational DPIA register tracking all Data Protection Impact Assessments across the enterprise. Covers DPIA lifecycle management, status tracking, review scheduling, Art. 35(11) periodic reassessment, and supervisory authority reporting. Implements a centralised register linking DPIAs to RoPA entries, risk registers, and mitigation plans. Keywords: DPIA register, DPIA tracking, Art. 35(11), review schedule, DPIA lifecycle, centralised register, DPIA portfolio management.
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 the combined DPIA and AI Act conformity assessment for AI systems processing personal data. Covers EDPB-EDPS Joint Opinion 5/2021, training data lawfulness under Art. 6 and Art. 9, Art. 22 automated decision-making, algorithmic bias detection, and NIST AI RMF MAP function. Keywords: AI privacy, DPIA, AI Act, algorithmic bias, automated decision-making, Art. 22, training data, NIST AI RMF.
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。
Determines controller-processor relationships for AI services and conducts privacy due diligence. Covers SaaS AI (processor), embedded AI (joint controller), API-based AI (assessment framework), and vendor risk assessment. Keywords: AI vendor, controller-processor, due diligence, SaaS AI, joint controller, Art. 28.
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。
Comprehensive market environment analysis and reporting tool. Analyzes global markets including US, European, Asian markets, forex, commodities, and economic indicators. Provides risk-on/risk-off assessment, sector analysis, and technical indicator interpretation. Triggers on keywords like market analysis, market environment, global markets, trading environment, market conditions, investment climate, market sentiment, forex analysis, stock market analysis, 相場環境, 市場分析, マーケット状況, 投資環境.
60-Second Ad Readiness Snapshot — quick assessment without subagents
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。
Identify, assess, and mitigate operational risks. Trigger with "what are the risks", "risk assessment", "risk register", "what could go wrong", or when the user is evaluating risks associated with a project, vendor, process, or decision.
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。
Framework for competitive landscape analysis across any industry. Use when creating competitor analysis, market positioning assessments, investment memos, strategic reviews, or any analysis requiring systematic evaluation of competitive dynamics. Triggers include requests for competitive landscape decks, peer comparisons, market structure analysis, strategic positioning assessments, and investment recommendations.
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。
Risk analysis, regulatory risk assessment, competitive risk, macro risk, technology risk, litigation risk, financial risk assessment, enterprise risk, operational risk, geopolitical risk exposure
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。
技術記事の内容から読者の理解度をチェックする4択クイズを指定のYAML形式で生成するスキル。ユーザーが「この記事のクイズを作って」「理解度チェックを作成して」「記事を読んだ人向けの問題を作って」「selfAssessment形式でクイズを作って」と依頼したときに必ず使う。ブログ記事、技術ドキュメント、チュートリアル、解説記事、技術書の章など、読者の学習内容を確認する問題を作る必要があるあらゆる場面で使う。記事のURL・マークダウン・テキスト・ファイルパスのいずれを渡されても動作する。
Produce a risk matrix or heatmap that quantifies what could break by business impact × probability, runs failure mode analysis on the top items, and maps test coverage to risk zones. Includes stakeholder interview frameworks and continuous reassessment. Run this BEFORE test-strategy or test-planning. Use when: "risk assessment," "risk matrix," "risk heatmap," "what could break," "critical paths," "failure modes," "where to focus testing." Not for: multi-quarter QA direction — use test-strategy. Not for: a single sprint/release test plan — use test-planning. Not for: hands-on session-based bug hunting — use exploratory-testing. Related: test-strategy, test-planning, release-readiness, qa-metrics.
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。
Comprehensive security auditing framework for LLM applications covering OWASP Top 10 for LLMs, threat modeling, penetration testing, and compliance with NIST AI RMF and ISO 42001Use when "security audit, llm pentest, ai security assessment, compliance audit, vulnerability assessment, security, audit, compliance, penetration-testing, owasp, llm" mentioned.
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。
Expert in applying AI to education - AI tutors, personalized learning paths, content generation, automated assessments, and adaptive learning systems. Covers practical implementation of AI to enhance (not replace) human instruction. Use when "ai tutor, ai for learning, personalized learning, adaptive learning, ai assessment, generate course content, ai education, " mentioned.
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。
AWS Security Audit workflow skill. Use this skill when the user needs Comprehensive AWS security posture assessment using AWS CLI and security best practices and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
AWS Security Audit workflow skill. Use this skill when the user needs Comprehensive AWS security posture assessment using AWS CLI and security best practices and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
供应链与生产运营 (供应链与生产运营 (Supply Chain & Production Operations) — 面向制造业与消费品实体供应链的『计划-制造-交付』运营判断,不是采购谈价技巧,也不是跨境电商发货物流。覆盖: (a) 第一性张力 — 这行的核心世界观分歧: **流动 > 局部效率(用有效产出/流动时间看系统,而不是用设备利用率/人均产出看工位;非瓶颈上的效率是幻觉) ⇄ 精益拉动 vs 推式预测(TPS/JIT 按下游消耗拉动、小批量平准化 vs MRP 按预测展开净需求推动;DDMRP 解耦缓冲是第三条路) ⇄ 精益/丰田 TPS 派 vs 约束理论 TOC 派(消除七大浪费、全局改善 vs 五步聚焦瓶颈、鼓-缓冲-绳 DBR 排产;两派互相指责对方浪费时间/只治标) ⇄ 变异是敌人(Deming/六西格玛/Factory Physics 的 VUT 排队论: 利用率越靠近满载排队时间越爆炸,交期长不是产能不够而是变异+高负荷) ⇄ 库存是负债 vs 库存是缓冲(会计把库存记成资产、精益视其为掩盖问题的水位、TOC 与韧性派视其为吸收变异的必要缓冲;真问题是缓冲放在哪一层而不是有没有) ⇄ 精益 JIT vs 韧性冗余(2020-2023 断链后 just-in-case、多源、近岸/友岸的回摆;精益派反驳崩的是『假精益』——单一来源+砍库存不砍变异) ⇄ 服务水平 vs 库存成本(安全库存/报童模型/ABC-XYZ 的经典取舍,牛鞭效应放大上游波动) ⇄ 成本导向压价 vs 供应商共生开发(Kraljic 分级、丰田供应商协会式长期共生 vs 年降指标与竞价) ⇄ 一个数 vs 各部门各算各的(S&OP/IBP 的本质是跨职能对齐到一套数字与一份产销计划))**; (b) 工具栈(此行的工具是『系统+方法』的混合) — ERP/MRP(SAP S/4HANA PP·MM、Oracle、用友·金蝶、Odoo、NetSuite)、APS 高级计划排程(Kinaxis RapidResponse、o9、Blue Yonder、SAP IBP、OMP、Siemens Opcenter APS)、MES/车间执行(Siemens Opcenter、Rockwell FactoryTalk、黑湖智造)、WMS/TMS、需求预测(统计模型 + Nixtla StatsForecast/Prophet、FVA 预测增值分析)、运筹优化与仿真(OR-Tools、Gurobi、Pyomo、AnyLogic、FlexSim、SimPy)、供应链风险与多级可视(Resilinc、Everstream、Interos)、分析层(SQL/Python pandas/Power BI)、以及纸笔工具(价值流图 VSM、A3、看板、标准作业票)—— 选型决策树的关键分岔是 MTS/MTO/ATO/ETO 履行模式与需求变异度,而非厂商功能清单; (c) 心智模型层 — 利特尔法则(在制品 = 产出率 × 流动时间)、瓶颈与非瓶颈、缓冲三态(库存/时间/产能缓冲)、解耦点(客户订单渗透点 CODP)、牛鞭效应、takt time 节拍与产线平衡、OEE 与六大损失、muda·mura·muri 三种浪费、自働化 jidoka 与安灯、防错 poka-yoke、SMED 快速换模换出小批量、平准化 heijunka、PDCA 与丰田改善型 Toyota Kata、DMAIC 与 Cpk 过程能力、报童模型与安全库存公式、ABC-XYZ 分级; (d) 工作流/SOP — 跑一轮月度 S&OP(需求评审→供应评审→预 S&OP→执行 S&OP)、做需求预测与预测误差复盘(MAPE/WMAPE/bias/FVA)、设定库存分级与安全库存、MRP 跑批与例外消息处理(缺料齐套与 MRP 神经质)、瓶颈识别与 DBR 排产、价值流图现状图→未来图→改善计划、新品导入 NPI 与多级 BOM/ECN 工程变更、供应商选择与开发(RFQ→审厂→APQP/PPAP→量产→年度绩效)、断料应急与交期承诺(ATP/CTP)、循环盘点与库存准确率; (e) 知识正典(en/ja 一手极厚,中译齐全) — 《目标 The Goal》Goldratt(TOC 小说体奠基)、《丰田生产方式》大野耐一、《改变世界的机器》Womack·Jones·Roos(MIT IMVP 研究)、《精益思想 Lean Thinking》Womack & Jones、《丰田模式 The Toyota Way》Liker、《工厂物理学 Factory Physics》Hopp & Spearman(排队论视角的运营科学)、《学习观察 Learning to See》Rother & Shook(VSM)、《丰田套路 Toyota Kata》Rother、Spear & Bowen 的 HBR 1999《Decoding the DNA of the Toyota Production System》、Lee/Padmanabhan/Whang 1997《The Bullwhip Effect in Supply Chains》、Ptak & Smith 的 DDMRP、Orlicky 的 MRP、Chopra & Meindl 的供应链管理教材、Deming《转危为安 Out of the Crisis》、Sheffi《The Resilient Enterprise》、ASCM/APICS 的 SCOR 参考模型与 CPIM/CSCP 知识体系; (f) 行业大佬/figures — 大野耐一(TPS 立法者)、新乡重夫(SMED/防错)、Eliyahu Goldratt(TOC 创立者)、W. Edwards Deming(变异与系统观)、James Womack & Daniel Jones(把 TPS 译成西方可学的『精益』)、Jeffrey Liker(丰田模式学术化)、Mike Rother(VSM 与改善型)、Wallace Hopp & Mark Spearman(工厂物理学)、Hau Lee(斯坦福,牛鞭效应与 Triple-A 供应链)、Yossi Sheffi(MIT,韧性)、Carol Ptak & Chad Smith(DDMRP)、David Simchi-Levi(MIT,风险敞口指数)、Steven Spear(TPS 的 DNA)、以及中文圈的供应链实务作者(刘宝红等); (g) 争议/批判(必须保留不洗白) — JIT 是不是 2021 全球断链的元凶(精益派反驳:崩的是单一来源+砍库存不砍变异的『假精益』);西方学精益的高失败率(只搬工具不搬思维,精益在很多工厂沦为裁员的代号);六西格玛黑带官僚化与扼杀创新之争;TOC 派与精益派互相攻击(Goldratt 讽刺精益是等浪费自己浮现);预测无用论(需求驱动派说预测必然错、要靠缓冲吸收)vs 统计预测派;控制塔/数字孪生/AI 预测的过度承诺与高项目失败率;中国制造的年降与内卷式压价 vs 供应商共生;近岸·友岸外包是真实产能转移还是政治叙事; (h) 流派/思想谱系 — **精益/TPS 派(丰田血统:大野-新乡→Womack/Jones/Liker/Rother,信全局消除浪费与人的改善能力)** vs **约束理论 TOC 派(Goldratt:系统产出只由瓶颈决定,聚焦五步、DBR、缓冲管理)** vs **统计质量/六西格玛派(Deming-Juran-Shewhart→GE/Motorola,信减少变异与过程能力)** vs **运营科学/工厂物理学派(Hopp & Spearman:用排队论与数学定律解释,认为精益与 TOC 都是特例)** vs **需求驱动/DDMRP 派(Ptak & Smith:预测不可靠,靠战略解耦缓冲)** vs **韧性/风险派(Sheffi、Simchi-Levi:效率之外必须为断链设计)**。不含: 跨境电商卖家的发货与头程物流(已由 cross-border-ecommerce 覆盖)、纯采购的商务谈判技巧、企业级 ERP 的代码实施与二次开发、通用项目管理。) Master OS — automated mastery of 供应链与生产运营 (Supply Chain & Production Operations) — 面向制造业与消费品实体供应链的『计划-制造-交付』运营判断,不是采购谈价技巧,也不是跨境电商发货物流。覆盖: (a) 第一性张力 — 这行的核心世界观分歧: **流动 > 局部效率(用有效产出/流动时间看系统,而不是用设备利用率/人均产出看工位;非瓶颈上的效率是幻觉) ⇄ 精益拉动 vs 推式预测(TPS/JIT 按下游消耗拉动、小批量平准化 vs MRP 按预测展开净需求推动;DDMRP 解耦缓冲是第三条路) ⇄ 精益/丰田 TPS 派 vs 约束理论 TOC 派(消除七大浪费、全局改善 vs 五步聚焦瓶颈、鼓-缓冲-绳 DBR 排产;两派互相指责对方浪费时间/只治标) ⇄ 变异是敌人(Deming/六西格玛/Factory Physics 的 VUT 排队论: 利用率越靠近满载排队时间越爆炸,交期长不是产能不够而是变异+高负荷) ⇄ 库存是负债 vs 库存是缓冲(会计把库存记成资产、精益视其为掩盖问题的水位、TOC 与韧性派视其为吸收变异的必要缓冲;真问题是缓冲放在哪一层而不是有没有) ⇄ 精益 JIT vs 韧性冗余(2020-2023 断链后
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。
Suede-owned operations-assessment discipline that maps how work actually happens before anyone designs a system: floor-level interviews with the people who do the work, an inventory of every silo including spreadsheets and inboxes used as databases, friction quantified in the requester's own numbers, opportunities ranked by annual value against build complexity, and adoption watched for the thirty days after launch. Use when nobody can answer what to automate, when an operation needs auditing before a build, when tribal-knowledge and key-person risk has to surface, or when a shipped system is quietly being routed around. NOT FOR: deciding what to build and in what order once the map exists (use suede-ops-architecture); researching external customers rather than internal operators (use suede-customer-research); lead lifecycle, scoring, and CRM routing rules (use suede-revops); product instrumentation and dashboards (use suede-analytics); getting a new user to first value (use suede-onboarding).
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。