「transparency」の検索結果

Decide when and how your product and communications must (or should) label AI-generated content, and write the disclosure policy — surface-by-surface rules, exact label wording, and the review trigger for regulations like the EU AI Act's transparency obligations. Use when asked 'do we have to label AI content', 'write our AI disclosure policy', 'are we covered for the AI Act', or when marketing/support/product start shipping AI-generated output. Produces a disclosure policy with a per-surface matrix and ready-to-use label copy. Not legal advice.

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Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.

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。

Help users design and maintain a high-performing culture by balancing radical transparency, psychological safety, and a relentless focus on customer delight.

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。

Pre-flight policy check for Meta ads. Takes ad copy plus advertiser context, resolves and fetches the relevant Meta transparency-center policy pages at runtime, and returns a Pass / Fix Required / Block verdict with cited findings and rewrites.

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Use when planning or reporting the analysis for an American Educational Research Journal (AERJ) manuscript — multilevel/HLM and growth models, IRT/measurement, quasi-experimental estimation, or qualitative coding and thematic analysis. Analysis must meet the AERA reporting standards (warrant + transparency). Strengthens analysis reporting; it does not run models for you.

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Use when targeting the Journal of Cleaner Production or deciding whether a cleaner-production/sustainability manuscript fits this venue. Encodes the journal's fit, the cleaner-production contribution and methodological-transparency bar, LCA/system-boundary expectations, house style, official-submission re-check, and desk-reject heuristics.

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Use when a single image of a character, creature, prop, vehicle, or building must be seen from new camera angles through the Scenario MCP server: turnarounds, a 360 orbit, views from behind, above, below, or straight down, consistent multi-angle renders on the same grounded background or on transparency, or novel views that go through a 3D intermediary instead of guessing.

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How humans and AI compose in content workflows. Where AI legitimately participates, where humans must own, hybrid workflow patterns, voice ownership preservation, the AI slop problem, disclosure and transparency, team calibration, and the ethics of intellectually honest AI-assisted content production. Triggers on AI content workflow, AI-assisted writing, hybrid content production, AI in editorial, AI slop, AI disclosure, AI usage policy, AI content ethics, voice preservation with AI, team AI calibration. Also triggers when content feels generic despite quality tools, when team AI usage has drifted into inconsistency, or when a regulated or trust-sensitive context requires explicit AI policy.

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。

EU AI Act (Regulation (EU) 2024/1689) compliance advisor — risk classification across all four tiers, all 9 prohibited practices (Art. 5, including the nudification/CSAM prohibition from Dec 2, 2026), all 8 Annex III high-risk use case areas, provider and deployer obligations (Arts. 9–17, 26), GPAI model obligations including the July 2025 Code of Practice (Arts. 51–55), conformity assessment and CE marking (Arts. 43–48), EU AI database registration, Art. 50 transparency (chatbots, synthetic media, AI-generated content), governance (AI Office, AI Board), penalties (Art. 99), confirmed phase-in timeline (Digital Omnibus, Reg. (EU) 2026/1744, in force July 27, 2026: Annex III deferred to Dec 2, 2027; Annex I to Aug 2, 2028), and cross-framework mapping to ISO 42001, NIST AI RMF, and GDPR. Use for any EU AI regulation, AI system classification, or AI compliance question. Current as of August 2026. GPAI enforcement powers active since August 2, 2026.

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 ISO 42001 AI Management System (AIMS) compliance advisor. Use this skill whenever a user asks about ISO/IEC 42001:2023, AI governance, AI management systems, AI risk assessment, AI system impact assessment, Annex A controls for AI, Statement of Applicability for AI systems, AI policy, responsible AI, AI lifecycle management, AI incident management, AI transparency, AI bias, AI certification readiness, or any topic related to implementing or auditing an AI Management System. Also trigger for questions like "how do I become ISO 42001 certified?", "what controls does ISO 42001 require?", "how do I assess AI risk under 42001?", "what is an AIMS?", or any request involving organisational governance of AI systems, responsible AI frameworks, or AI regulatory compliance aligned to an ISO standard.

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 NIST AI Risk Management Framework (AI RMF 1.0) advisor covering all four functions: GOVERN, MAP, MEASURE, MANAGE. Use this skill whenever a user asks about NIST AI RMF, AI risk management, AI trustworthiness, GOVERN function, MAP function, MEASURE function, MANAGE function, AI RMF Playbook, AI risk profiles, responsible AI, AI bias management, AI transparency, AI explainability, AI reliability, AI safety, NIST AI 100-1, AI risk assessment, AI incident response, or alignment to EU AI Act, ISO 42001, or NIST CSF via AI RMF. Trigger even if the user doesn't say "skill" — any NIST AI RMF or AI governance risk question should use this 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。

Validate a monthly investor update against a rubric of strong-update sections (transparency, decision-relevance, specific asks). Use before sending a monthly investor update or setting a fundraise communication cadence.

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。

Draft GDPR/DSGVO-compliant privacy notices as .docx for any EU/EEA jurisdiction and audience. Use when user asks to create a privacy policy/notice, mentions "Datenschutzerklärung", "politique de confidentialité", "privacy notice", needs Art. 13/14 disclosures, AI Act transparency, cookie policy, or notices for applicants ("Bewerber-Datenschutz"), employees ("Beschäftigten-Datenschutz"), B2B partners, or B2C customers. Covers DE (DSGVO+BDSG+TDDDG), FR (RGPD+LIL+LCEN), AT, IT, ES, NL, BE, IE, UK GDPR. Five notice types: Website/App, Applicant, Employee, Business Partner, B2C Customer.

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 NIST AI Risk Management Framework (AI RMF 1.0) advisor covering all four functions: GOVERN, MAP, MEASURE, MANAGE. Use this skill whenever a user asks about NIST AI RMF, AI risk management, AI trustworthiness, GOVERN function, MAP function, MEASURE function, MANAGE function, AI RMF Playbook, AI risk profiles, responsible AI, AI bias management, AI transparency, AI explainability, AI reliability, AI safety, NIST AI 100-1, AI risk assessment, AI incident response, or alignment to EU AI Act, ISO 42001, or NIST CSF via AI RMF. Trigger even if the user doesn't say "skill" — any NIST AI RMF or AI governance risk question should use this 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。

Expert ISO 42001 AI Management System (AIMS) compliance advisor. Use this skill whenever a user asks about ISO/IEC 42001:2023, AI governance, AI management systems, AI risk assessment, AI system impact assessment, Annex A controls for AI, Statement of Applicability for AI systems, AI policy, responsible AI, AI lifecycle management, AI incident management, AI transparency, AI bias, AI certification readiness, or any topic related to implementing or auditing an AI Management System. Also trigger for questions like "how do I become ISO 42001 certified?", "what controls does ISO 42001 require?", "how do I assess AI risk under 42001?", "what is an AIMS?", or any request involving organisational governance of AI systems, responsible AI frameworks, or AI regulatory compliance aligned to an ISO standard.

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。

EU AI Act (Regulation (EU) 2024/1689) compliance advisor — risk classification across all four tiers, all 9 prohibited practices (Art. 5, including the nudification/CSAM prohibition from Dec 2, 2026), all 8 Annex III high-risk use case areas, provider and deployer obligations (Arts. 9–17, 26), GPAI model obligations including the July 2025 Code of Practice (Arts. 51–55), conformity assessment and CE marking (Arts. 43–48), EU AI database registration, Art. 50 transparency (chatbots, synthetic media, AI-generated content), governance (AI Office, AI Board), penalties (Art. 99), confirmed phase-in timeline (Digital Omnibus adopted June 29, 2026: Annex III deferred to Dec 2, 2027; Annex I to Aug 2, 2028), and cross-framework mapping to ISO 42001, NIST AI RMF, and GDPR. Use for any EU AI regulation, AI system classification, or AI compliance question. Current as of July 2026. GPAI enforcement powers activate August 2, 2026.

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。

Privacy manifest (PrivacyInfo.xcprivacy) implementation including required reason APIs, tracking domains, third-party SDK declarations, and App Tracking Transparency. Use when preparing apps for App Store privacy requirements.

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。

Generates GDPR/CCPA/DPDP privacy consent flows with granular category preferences, consent state persistence, audit logging, and ATT (App Tracking Transparency) integration. Use when user needs privacy consent UI, cookie/tracking consent, or compliance management.

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Expand a hard-to-build niche list from ~10 known-good seed companies into a full qualified TAM. Seed fingerprinting (how do good-fit companies ACTUALLY show up in the company database's filters) → lookalike generation (Prospeo company_lookalike, Exa findSimilar, Parallel.ai entity search) → filter mining with a precision/volume scorecard → wide pull with auto-sharding → cheap-AI qualification → live-website verification → client transparency report. Use when a vertical list feels "too small" (medical groups, hedge funds, Medicare brokerages), when known good-fit companies don't show up in keyword searches, or when someone says "expand this list", "the TAM should be bigger", "find more companies like these".

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。

Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

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。

Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical 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。

Explain analysis methodology to diverse audiences. Use when documenting 'how we did this' sections, building trust through transparency, or teaching analytical approaches to stakeholders.

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 reviewing, designing, or modifying Java enterprise systems that may support EU Digital Markets Act gatekeeper-platform concerns, core platform services, interoperability, business-user data access, consent-dependent data combination, ranking, self-preferencing, advertising transparency, or anti-circumvention controls. This should trigger for requests such as Review a Java platform for DMA controls; Design interoperability and business-user data access evidence; Add ranking, consent, preference, or anti-circumvention audit controls; Assess gatekeeper-platform engineering evidence before production release. 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。

Assess company readiness for construction industry uberization. Analyze data transparency, process automation, and competitive positioning against open data platforms.

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。