「eu」の検索結果

审计网站是否符合阿塞拜疆《个人数据法》（Law on Personal Data No. 998-IIIQ），并在适用时符合欧盟 GDPR 以及 ePrivacy/饼干同意规则。盘点存在的隐私文件（隐私政策、饼干政策、饼干横幅、同意流程、控制者和 DPO 联系方式、数据主体权利渠道、跨境转移披露、AZ 运营者登记引用），并对照适用的法定要求逐项评分。产出双层报告：面向企业主的通俗语言红绿灯摘要，外加面向律师的逐条款发现表（带条款级引用）。仅评估 — 不起草。每当用户分享针对阿塞拜疆或面向阿塞拜疆网站的 URL 或隐私/饼干政策文本时使用；当用户提及 .az 域名、Law 998、AZ 国家登记簿、ePrivacy、第 27 条欧盟代表，或询问"is my site GDPR compliant"、"do I need to register as an operator in Azerbaijan"或"is our cookie banner lawful"时也使用 — 即使没有"audit"一词。

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。

Drafts an Adverse Event Reporting Policy compliant with 21 CFR 312.32 (IND safety reporting), 21 CFR 314.80 (postmarketing), and ICH E2A, with multi-jurisdictional overlays (EMA, PMDA, Health Canada). Covers seriousness/causality frameworks, expedited reporting timelines, roles, documentation standards, training mandates, and QA mechanisms. Use when drafting or updating an adverse event reporting policy, pharmacovigilance policy, AE/SAE reporting SOP, or safety reporting framework for a pharmaceutical company, CRO, biotech, or clinical research institution.

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。

欧盟 AI 法案知识引擎——以 70 份官方欧盟来源文件为基础的权威监管问答（包括 2026 年委员会关于 Art. 6 高风险分类的指南草稿——一般原则、附件 I、附件 III）。基于完整条例文本、委员会指南、EDPB/EDPS 意见、《实践守则》、协调标准、FRIA 指南、事件模板和行业特定指引，以条款级引用回答关于 AI 法案的任何问题。当用户询问"EU AI Act"、"AI Act regulation"、"Regulation 2024/1689"、"KI-Verordnung"、要求"explain an AI Act article"、"what does Article X say"、"AI Act requirements"、"AI Act penalties"、"AI Act timeline"、"GPAI obligations"、"high-risk AI"、"prohibited AI practices"、"AI Act and GDPR"、"fundamental rights impact assessment"、"AI Act standards"、"AI Act national implementation"、"AI literacy"、"serious incident reporting"、"Digital Omnibus"、"AI regulatory sandbox"，或任何关于欧盟人工智能监管的问题时，应使用本技能。德语术语同样触发："KI-Gesetz"、"KI-Verordnung"、"Hochrisiko-KI"、"KI-Kompetenz"、"Grundrechte-Folgenabschaetzung"、"GPAI-Verhaltenskodex"。对于产出分类决定而非知识答案的评估工作流，提议在会话内运行结构化风险等级分类。

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。

Design Kafka consumer groups that survive rebalances, hit the right delivery guarantee (at-most/at-least/exactly-once), and handle poison messages without stalling the topic. Use when picking a partition assignment strategy (Range vs Sticky vs Cooperative-Sticky vs the new KIP-848 protocol), tuning heartbeat / session / max.poll.interval timeouts, choosing manual vs auto offset commits, designing dead-letter or retry topics, or migrating from Classic to the next-gen Consumer protocol. NOT for Kafka cluster ops (broker tuning, partition reassignment), schema design (Avro/Protobuf/Schema Registry), or stream processing topology (use a kafka-streams 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。

Monetization strategies for indie developers, solopreneurs, and small teams. Covers freemium models, SaaS pricing, sponsorships, donations, email list building, and passive income for developer tools, content sites, and educational apps. Activate on 'monetization', 'make money', 'pricing', 'freemium', 'SaaS', 'sponsorship', 'donations', 'passive income', 'indie hacker'. NOT for enterprise sales, B2B outbound, VC fundraising, or large-scale advertising (use enterprise/marketing 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。

Generate hand-drawn style diagrams and infographics for recovery education articles. Creates anatomist's notebook aesthetic visuals - brain diagrams, timelines, social comparisons, and process flows using continuous line art, semantic color coding, and margin annotations.

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。

Heuristic search-based temporal planning and scheduling for complex multi-constraint environments

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。

Wave-based parallel scheduling for DAG execution. Manages execution order, resource allocation, and parallelism constraints. Activate on 'schedule dag', 'execution waves', 'parallel scheduling', 'task queue', 'resource allocation'. NOT for building DAGs (use dag-graph-builder) or actual execution (use dag-parallel-executor).

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。

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

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 a parallel agent team for COMPLEX tasks: creates worktree structure, an ownership map, and an initialized task queue. Use when a task is decomposed into parallel subtasks.

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。

JIRA Service Management via jira-as: service desks, requests, customers, orgs, queues, SLAs, approvals. Requires JSM tenant + agent role. NOT core issue ops (mk:jira-issue) or admin (mk:jira-admin).

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。

Heuristic read-only story-point estimation for one Jira ticket via jira-estimator agent. NOT complexity analysis (mk:jira-evaluator) or full RCA (mk:jira-analyst).

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。

Fetch external sources (web / arXiv / GitHub) and turn them into scanner-gated wiki CANDIDATES — never canonical pages. Every fetched byte is DATA; url-guarded, size-capped, redirect-re-validated, injection-scanned, and secret-scrubbed before a candidate is even created. Requires network. NOT for capturing local knowledge (see mk:wiki); NOT for one-shot page-to-markdown (see mk:web-to-markdown).

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。

Heuristic story-point estimation for a single Jira ticket via the jira-estimator agent. Read-only. Triggers: 'estimate KEY', 'story points for KEY', 'how complex is KEY'. NOT for complexity-only analysis (mk:jira-evaluator); NOT for full RCA (mk:jira-analyst).

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。

Browser automation CLI for AI agents using agent-browser. Use for navigating websites, clicking/filling pages, screenshots, data extraction, web app testing, exploratory QA, dogfooding, Electron apps, Slack automation, Vercel Sandbox browser runs, AWS AgentCore cloud browsers, auth-heavy flows, and long autonomous browser sessions. Prefer over generic browser tools when a fresh/tool-managed Chrome session is fine. NOT for the user's real Chrome cookies/profile (see mk:chrome-profile); NOT for reusable Playwright specs (see mk:qa-manual).

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。

DraftForge PR lifecycle workflow. Use when the user runs /pr, opens or pushes a pull request, or asks to monitor CI checks and Copilot review on a branch. Creates the PR (or reuses an existing one), polls GitHub Actions and Copilot up to ~15 min, then analyses CI failures and Copilot comments and reports findings grouped by file and 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。

Analyze task trajectories to propose reusable SKILL.md candidates from successful patterns

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。

Parameterized prompt templates using Jinja2 patterns for reusable, dynamic agent prompts

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。

Diagnose and optimize timing-critical Chisel RTL for FPGA implementation while preserving cycle semantics. Use for Vivado timing bottlenecks, long ready or admission paths, queue and issue selection, free-list bank mapping, priority encoders, one-hot arbitration, wide muxes, late-arriving forwarding or override data, high-fanout controls, cross-module predicates, register-boundary changes, emitted-Verilog inspection, or routed-DCP A/B analysis. Also use when a source-level simplification needs proof that it changes timing without changing architectural state.

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。

Audit an existing UI for usability, hierarchy, accessibility, and conversion problems. Reviews a live URL, screenshot, or codebase against heuristics and reports prioritized issues with fixes. Use when the user says "audit this UI", "what's wrong with this screen", "UX review", "find the problems", "why is this confusing", or has existing interface to critique. Part of the Product Design set. Can drive a browser for live audits.

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 whenever the user asks to generate, edit, inpaint, restyle, or create bitmap images through OpenAI's Responses API image_generation tool from Codex. This skill calls the Responses API, saves generated base64 image results to files, uses no npm or pip dependencies, works on Linux/macOS/Windows with either Node.js 18+ or Python 3, and reuses Codex's configured API base URL, model, and key instead of asking the user for credentials.

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。

Apply or adapt a dual-theme neo-glow control board for UI kits, design-system previews, settings surfaces, admin panels, control rooms, and dense operational interfaces. Use when Codex should build or restyle interface work with bright white or dark control surfaces, compact neumorphic cards, vertical icon rails, blue/cyan halo accents, glowing active states, luminous toggles, and a board focused on control states, inputs, and specification details.

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。

Record measured or directly seen security-research behavior as cited Observations, classify it against reusable security concepts, and decide whether it remains an observation, becomes a Lead, or supports a Finding. Use after passive analysis, a tool run, a probe, a crash, a timing anomaly, or a user-supplied behavioral report produces noteworthy evidence.

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。

Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.

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。