「ai governance」の検索結果

Assesses AI bias risks for GDPR Art. 9 special category data and AI Act Art. 10 data governance. Covers fairness metrics, bias detection methods, mitigation strategies, and documentation requirements for protected characteristics. Keywords: AI bias, special category, fairness metrics, discrimination, Art. 9, Art. 10.

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Applies the reasoning, frameworks, and mental models of Fei-Fei Li, computer vision pioneer, ImageNet creator, and co-director of Stanford HAI. Use this skill whenever Claude encounters topics related to AI ethics, human-centered AI, spatial intelligence, embodied AI, robotics, AI governance, diversity in tech, or the societal impacts of AI. Trigger this skill when users face decisions about AI product design (augment vs. replace), dataset formulation, navigating AI regulation, or choosing audacious research directions. Channel her pragmatic optimism and focus on spatial, physical grounding over pure language models.

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。

Applies the reasoning of Stuart Russell, AI safety expert, UC Berkeley professor, and co-author of 'Artificial Intelligence: A Modern Approach'. Reach for this skill whenever evaluating AI safety, value alignment, the control problem, existential risk, AI regulation, or autonomous weapons. Use this when the user is discussing objective uncertainty, reinforcement learning risks, AI governance, or the societal impacts of AGI. Trigger this skill to apply his frameworks on provably beneficial AI, assistance games, and red-line regulation, ensuring AI systems remain deferential, uncertain of their objectives, and strictly aligned with human preferences.

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。

Applies the reasoning of Stuart Russell, AI safety expert, UC Berkeley professor, and co-author of 'Artificial Intelligence: A Modern Approach'. Reach for this skill whenever evaluating AI safety, value alignment, the control problem, existential risk, AI regulation, or autonomous weapons. Use this when the user is discussing objective uncertainty, reinforcement learning risks, AI governance, or the societal impacts of AGI. Trigger this skill to apply his frameworks on provably beneficial AI, assistance games, and red-line regulation, ensuring AI systems remain deferential, uncertain of their objectives, and strictly aligned with human preferences.

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。

Applies the reasoning, frameworks, and mental models of Fei-Fei Li, computer vision pioneer, ImageNet creator, and co-director of Stanford HAI. Use this skill whenever Claude encounters topics related to AI ethics, human-centered AI, spatial intelligence, embodied AI, robotics, AI governance, diversity in tech, or the societal impacts of AI. Trigger this skill when users face decisions about AI product design (augment vs. replace), dataset formulation, navigating AI regulation, or choosing audacious research directions. Channel her pragmatic optimism and focus on spatial, physical grounding over pure language models.

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 guide for AI Safety, Governance, and Responsible AI in production — Constitutional AI enforcement, runtime guardrails (NeMo Guardrails 2.0, Llama Guard 3), bias auditing, hallucination detection, EU AI Act compliance, model cards, and content provenance / Panduan ahli untuk Keamanan AI, Tata Kelola, dan AI Bertanggung Jawab di produksi.

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。

Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation against structured test cases, immutable prompt version history with diffs, ready-to-use marketing prompt templates (ad copy, email campaigns, social posts, landing pages, SEO meta), and an LLM-governance playbook for marketing teams (claim discipline, disclosure rules, human-review gates). Use when a marketing team relies on AI-generated content and needs prompt quality to be measurable and safe — or when the user mentions 'prompt engineering,' 'improve my prompts,' 'prompt templates,' 'prompt versioning,' 'AI content workflow,' or 'AI governance for marketing.'

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。

Guide the user through an independent iFixAi audit of their own agent, checking whether it does the job it is supposed to do given their business rules and org structure. Prefer pointing it at the user's REAL deployed agent over its HTTP endpoint (its actual tools, retrieval, and governance) with `--provider http --endpoint <url>`; only when no endpoint is reachable, fall back to replicating the model beneath as a bare stand-in (Anthropic, OpenAI, Gemini, Azure, Bedrock, etc.). Graded by the judge(s) of their choice (the same model, one independent judge, or a cross-vendor panel). You are the operator who walks them through it and explains the scorecard, running the SAME `ifixai run` engine as the guided CLI. Use when the user asks to run iFixAi or to audit an agent.

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Bridge layer connecting the Agentic AI Foundation (AAIF) governance structure — Linux Foundation stewardship of MCP, goose, AGENTS.md — to the plurigrid/asi skill graph. Formalizes AAIF-compatible skill registration, IPSIE identity profiles, and the cross-protocol interoperability surface. Maps the AAIF platinum member ecosystem (AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft, OpenAI) to specific ASI skill integration points.

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。

"Net-zero transition playbook: GHG inventory (ISO 14064-1, GHG Protocol Scope 1/2/3), science-based targets (SBTi), TCFD and ISSB S1/S2 climate disclosure, Taiwan carbon fee, decarbonization roadmap, internal carbon pricing, supply-chain carbon, product carbon footprint, green electricity and T-REC. Use for corporate decarbonization roadmap, carbon inventory, carbon-fee response, TCFD/ISSB reporting, supply-chain decarbonization, or responding to international customers (Apple, RE100). Triggers: 『碳盤查』『SBTi』『碳費』『TCFD』『淨零路徑』『範疇 3』『綠電』『CBAM』『ISO 14064』『ISSB』『T-REC』. For Taiwan EMBA ESG/sustainability/operations/strategy/finance cases. Focuses on Taiwan listed-company and supply-chain practice; sustainability strategy theory: use Asgard `grad-sustainability`; ERM 框架整合氣候風險: use `biz-erm`; 董事會 ESG 委員會結構設計: use `biz-corporate-governance`; 併購 ESG 盡職調查: use `fin-m-and-a`.".

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。

"Corporate-governance practice playbook: board composition, three lines of defense, audit/compensation/nomination committee operations, independent-director governance, stakeholder communication, IPO governance upgrade, and family-business professionalization. Use for board-structure design, independent-director selection, three-lines implementation, IPO governance preparation, major governance incidents (fraud, scandal, insider trading), or family-business corporatization. Triggers: 『董事會組成』『獨董沒發揮功能』『三道防線』『上櫃治理補強』『家族法人化』『委員會分工』『ESG 治理』『審計委員會』『薪酬委員會』『提名委員會』. For Taiwan EMBA corporate-governance/finance/law/ESG cases. Focuses on listed-company and IPO practice; governance theory: use Asgard `grad-governance`; 9-box/繼任規劃/接班人才盤點: use `ops-talent-strategy`; COSO ERM/風險地圖/風險委員會運作: use `biz-erm`.".

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 admins are reducing Chatter signal-to-noise across an org — covers org-level Chatter Email Settings, per-user Email Settings preferences, group-level email digest frequency (Daily / Weekly / Every post / Never), bell-icon Custom Notifications vs Chatter feed-post tradeoffs, automated-feed suppression on record types via Feed Tracking config, and the Chatter Settings org defaults that control whether new users start with everything-on. Triggers: 'chatter feed full of automated posts nobody reads', 'users complain about chatter email volume', 'turn off chatter notifications for a specific group', 'change default chatter digest frequency', 'stop process builder posts from filling the feed', 'NotificationFrequency invalid value', 'ChatterEmailsMD.settings will not deploy', 'custom notification recipient limit'. NOT for Custom Notifications API design (use apex/apex-custom-notifications-from-apex), NOT for Connect API / Chatter REST API integration patterns (use apex/apex-connect-api-chatter), NOT for Chatter group lifecycle, archiving, ownership and unlisted-group governance (use admin/chatter-group-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。

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

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Run an AI impact assessment — structured intake, risk analysis, regulatory classification per regime in scope, policy consistency diff, and recommendation with conditions. Uses the house-style structure learned from the seed impact assessment in `~/.claude/plugins/config/claude-for-legal/ai-governance-legal/CLAUDE.md`. Use when user says "impact assessment for", "assess this AI use case", "run an AIA", "generate an AIA", "we need to document this AI system", "AI risk assessment for X", or follows a conditional triage result.

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Determines whether a specific AI-agent action, output, recommendation, or proposed commitment remains admissible for execution or institutional reliance under current authority, delegated scope, evidence, facts, policy, risk, escalation, and revocation conditions. Use this Skill before an enterprise or regulated AI agent executes, updates records, triggers workflows, communicates externally, or before an institution relies on an agentic output in a way that creates consequence.

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。

Applies the reasoning, AI safety frameworks, and deep learning principles of Yoshua Bengio (Turing Award winner, Mila). Reach for this skill whenever you are discussing AI safety, existential risk, deep learning architecture, representation learning, or AI governance. Trigger this skill when the user asks about mitigating AI risks, designing safe-by-design systems, evaluating frontier models, international AI coordination, or the fundamental mechanisms of intelligence (like compositionality and distributed representations). Use it to shift the focus from agentic reward-maximization to non-agentic 'Scientist AI', apply the precautionary principle to catastrophic risks, and emphasize mathematically rigorous guardrails.

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 preliminary, evidence-based EU AI Act readiness assessment across AI-system inventory, territorial scope, operator roles, prohibited-practice screening, risk classification, transparency, high-risk controls, general-purpose AI obligations, governance, and implementation milestones. Use when an organization needs to triage an AI use case, vendor, model, product, or portfolio for Regulation (EU) 2024/1689; prepare an AI inventory, gap register, implementation roadmap, or counsel briefing; assess provider, deployer, importer, distributor, product-manufacturer, authorised-representative, or GPAI-provider responsibilities; or re-check readiness after regulatory or product changes.

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Comprehensive expertise in decentralized autonomous organization governance systems, including Snapshot off-chain voting, OpenZeppelin Governor on-chain execution, treasury multi-sigs, proposal lifecycles, delegation, and governance attack prevention. Use when "DAO governance, on-chain voting, Snapshot, Governor contract, governance proposal, treasury management, multi-sig, token voting, delegation, quorum, timelock, governance attack, " 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。

Applies the reasoning, AI safety frameworks, and deep learning principles of Yoshua Bengio (Turing Award winner, Mila). Reach for this skill whenever you are discussing AI safety, existential risk, deep learning architecture, representation learning, or AI governance. Trigger this skill when the user asks about mitigating AI risks, designing safe-by-design systems, evaluating frontier models, international AI coordination, or the fundamental mechanisms of intelligence (like compositionality and distributed representations). Use it to shift the focus from agentic reward-maximization to non-agentic 'Scientist AI', apply the precautionary principle to catastrophic risks, and emphasize mathematically rigorous guardrails.

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 designing or reasoning about an Agentforce Grid worksheet — the spreadsheet-like Agentforce workspace where rows are jobs and columns are sequential Data / AI / Action steps that process left to right — to run bulk record updates, generate insights, or test multi-turn AI conversations over CRM or Data Cloud (Data 360) data without Apex or Flow. Trigger keywords: Agentforce Grid, workbook, worksheet, grid column, data column, AI column, action column, bulk AI update, Flex Credits per row, metered compute. NOT for building the agent, its topics, or its actions themselves (use agentforce/* action and topic skills), NOT for single-record prompt design (use the prompt-builder skills), NOT for deployable metadata/Apex (Grid is a no-code Studio tool, not a metadata type), and NOT for AI governance/ethics policy (use admin/ai-ethics-and-governance-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。

Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, co...

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。

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

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 CSRD (Corporate Sustainability Reporting Directive, EU 2022/2464) compliance advisor. Use this skill whenever a user asks about CSRD, European Sustainability Reporting Standards (ESRS), double materiality assessment, sustainability reporting obligations, ESG disclosure, CSRD scope and thresholds, value chain reporting, XBRL digital tagging, third-party assurance, CSRD gap assessments, CSRD implementation timelines, ESRS E1–E5 environmental standards, ESRS S1–S4 social standards, ESRS G1 governance, the 2026 Omnibus scope change (Directive (EU) 2026/470, 1,000+ employees) and revised/simplified ESRS (July 2026), CSRD vs GRI/TCFD/SASB alignment, or any EU corporate sustainability reporting question. Trigger even if the user only mentions "ESG reporting Europe", "sustainability disclosure EU", or "non-financial reporting".

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 CSRD (Corporate Sustainability Reporting Directive, EU 2022/2464) compliance advisor. Use this skill whenever a user asks about CSRD, European Sustainability Reporting Standards (ESRS), double materiality assessment, sustainability reporting obligations, ESG disclosure, CSRD scope and thresholds, value chain reporting, XBRL digital tagging, third-party assurance, CSRD gap assessments, CSRD implementation timelines, ESRS E1–E5 environmental standards, ESRS S1–S4 social standards, ESRS G1 governance, CSRD vs GRI/TCFD/SASB alignment, or any EU corporate sustainability reporting question. Trigger even if the user only mentions "ESG reporting Europe", "sustainability disclosure EU", or "non-financial reporting".

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。