15 件 ・ 関連度順
概要と使いどころ
Guides cooperation with GDPR supervisory authorities under Article 31, including procedures for responding to investigations, information requests, and on-site inspections. Covers controller and processor obligations during supervisory authority interactions. Keywords: supervisory authority, Article 31, cooperation, DPA investigation, information request, inspection.
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 GDPR Article 56 one-stop-shop mechanism for determining lead supervisory authority in cross-border processing. Covers main establishment identification and cooperation. Activate when processing across EU borders. Keywords: one-stop-shop, Article 56, lead authority, cross-border.
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 rapport-based elicitation to resistant or semi-cooperative humans and agents. Use when coercive prompting or brittle handoffs damage cooperation. NOT for routine cooperative tasks, pure technical debugging, or already reliable exchanges.
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 rapport-based elicitation to resistant or semi-cooperative humans and agents. Use when coercive prompting or brittle handoffs damage cooperation. NOT for routine cooperative tasks, pure technical debugging, or already reliable exchanges.
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 rapport-based elicitation to resistant or semi-cooperative humans and agents. Use when coercive prompting or brittle handoffs damage cooperation. NOT for routine cooperative tasks, pure technical debugging, or already reliable exchanges.
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。
Evaluate completed mediation-competition problems consisting of general information and confidential packets for both sides. Use when asked to validate, audit, score, quality-control, or assess competition readiness; identify factual, numerical, chronological, authority, commercial-logic, or confidentiality defects; test mediation usefulness, BATNAs, settlement corridors, information asymmetry, and opportunities for firmness, cooperation, imagination, and clarification; or produce an author-only validation report. Perform a qualified partial review when packets are missing. Diagnose by default; do not generate a new problem or edit source packets unless repairs are expressly requested. Requires access to all supplied files and a host capable of reading their formats; otherwise disclose the unreviewed material and resulting limits.
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。
Cialdini's seven principles of compliance (the classic six — reciprocation, commitment-consistency, social proof, liking, authority, scarcity — plus Unity, added 2016) and Pre-Suasion. Use when crafting ethical persuasion (marketing, sales, negotiation), defending against manipulation, or designing systems that need cooperation. Includes replication-crisis caveats and known factual corrections (Bickman parking-meter numbers, Hofling modern replication, Drive Carefully condition disambiguation, Genovese myth).
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 style of Geoffrey Hinton, deep learning pioneer and 2018 Turing Award winner. Use this skill whenever evaluating AI safety, existential risk, neural network architectures, cognitive science, or tech regulation. Reach for this when the user is discussing LLM capabilities (understanding vs. autocomplete), the biological vs. digital intelligence divide, AI alignment strategies, or the societal/economic impacts of automation. It is highly applicable when dealing with contrarian scientific ideas, hardware/software integration (mortal vs. immortal computing), or global cooperation on technological threats. Do not wait for the user to name Hinton; trigger this skill proactively for any deep learning or AI existential risk analysis.
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。
Reach for this skill whenever you are discussing reinforcement learning, agentic AI systems, AI alignment, continual learning, or the philosophical limits of large language models. This skill channels the thinking of Richard S. Sutton (reinforcement learning pioneer, University of Alberta, Keen Technologies, 2024 Turing Award). Use it to evaluate AI architectures, make long-term AI prognostications, or design systems that learn from runtime experience rather than static datasets. Apply his frameworks when users ask about AGI, the 'Bitter Lesson' of computation, the Reward Hypothesis, or decentralized cooperation versus centralized AI control.
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。
Reach for this skill whenever you are discussing reinforcement learning, agentic AI systems, AI alignment, continual learning, or the philosophical limits of large language models. This skill channels the thinking of Richard S. Sutton (reinforcement learning pioneer, University of Alberta, Keen Technologies, 2024 Turing Award). Use it to evaluate AI architectures, make long-term AI prognostications, or design systems that learn from runtime experience rather than static datasets. Apply his frameworks when users ask about AGI, the 'Bitter Lesson' of computation, the Reward Hypothesis, or decentralized cooperation versus centralized AI control.
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 style of Geoffrey Hinton, deep learning pioneer and 2018 Turing Award winner. Use this skill whenever evaluating AI safety, existential risk, neural network architectures, cognitive science, or tech regulation. Reach for this when the user is discussing LLM capabilities (understanding vs. autocomplete), the biological vs. digital intelligence divide, AI alignment strategies, or the societal/economic impacts of automation. It is highly applicable when dealing with contrarian scientific ideas, hardware/software integration (mortal vs. immortal computing), or global cooperation on technological threats. Do not wait for the user to name Hinton; trigger this skill proactively for any deep learning or AI existential risk analysis.
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。
Evaluates uptier exchanges, drop-down transactions, and covenant-stripping maneuvers as aggressive liability management tools. Use when analyzing LMTs, evaluating creditor-on-creditor violence, or assessing cooperation agreement strategies.
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 basic game theory concepts including Nash equilibrium, dominant strategies, and the Prisoner's Dilemma to analyze strategic interactions. Use this skill when the user needs to model competitive decisions, predict rival behavior, design incentive mechanisms, or evaluate cooperation vs competition scenarios — even if they say 'what will our competitor do', 'should we cooperate or compete', or 'how do we set up the right incentives'.".
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 game theory to multi-agent coordination protocols with advisory claims, repeated interactions, daemon-mediated correlation, and deviation penalties. Use for Nash equilibrium checks, folk-theorem cooperation analysis, correlated-equilibrium routing, and price-of-anarchy audits in strategic agent systems. NOT for consumer pricing, card-game strategy, or general microeconomics outside agent coordination.
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。
We find that the evolution of cooperation differs markedly across base models, with societies of Cla
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。