「safety」の検索結果

Applies the reasoning style of Ilya Sutskever (deep learning pioneer, co-founder of OpenAI and Safe Superintelligence Inc.) to problems involving AI architecture, scaling laws, alignment, and research strategy. Reach for this skill whenever discussing machine learning paradigms, the limits of compute and data, AGI timelines, superintelligence safety, or deciding between hardcoding vs. learning. Trigger this skill for questions about next-word prediction, reinforcement learning efficiency, generalization gaps, and transitioning from brute-force scaling to fundamental research, even if the user doesn't explicitly name him.

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。

This skill channels the strategic and scientific reasoning of Demis Hassabis, CEO and co-founder of Google DeepMind, AlphaGo and AlphaFold, and 2024 Nobel Prize in Chemistry. Use this skill whenever you are evaluating AI for scientific discovery, tackling "root node" problems, designing reinforcement learning systems, or discussing AGI timelines, safety, and global governance. Reach for it when the user faces massive combinatorial search spaces, wants to apply AI to physical/biological sciences (like digital biology), or needs to balance rapid AI scaling with the rigorous scientific method. Apply these mental models to shift the focus from building consumer apps to using AI as the ultimate meta-solution for understanding reality.

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。

This skill channels the reasoning of Yann LeCun, Chief AI Scientist at Meta and Turing Award winner. Use this skill whenever you are evaluating AI architectures, discussing the limitations of Large Language Models (LLMs), debating AI safety and regulation (anti-doomerism), or designing autonomous machine intelligence. It is highly relevant for topics involving self-supervised learning, open-source AI strategy, world models, physical grounding versus text-based learning, and objective-driven AI systems. Trigger this skill to apply his frameworks on abstract representation learning (JEPA) and energy-based models, even if the user doesn't explicitly name him.

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。

Automatically generate TLA+ specifications from source code (C/C++, Python) for formal verification of distributed systems. Use when users need to: (1) Generate TLA+ specs from program implementations, (2) Model distributed systems, consensus protocols, or concurrent algorithms, (3) Extract state variables, actions, and invariants from code, (4) Create formal specifications for model checking with TLC, (5) Verify safety and liveness properties of distributed systems. Particularly effective for message-passing systems, replication protocols, consensus algorithms, and distributed transactions.

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。

Translate natural-language requirements or structured specification documents into formal temporal logic properties (LTL, CTL, safety/liveness properties). Use when users need to formalize requirements for model checking, formal verification, or property specification. Handles embedded/real-time systems, hardware verification, concurrent systems, and reactive systems. Resolves ambiguities, asks clarifying questions when needed, and outputs machine-checkable formulas with explanations. Supports multiple output formats (SPIN, NuSMV, Uppaal, TLA+, Maude).

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。

Automatically repair C/C++ code violations detected by TLA+ model checking. Takes a program, TLA+ specification, and TLC counterexample trace as input, then generates minimal code modifications to eliminate the violation. Use when: (1) TLC model checker reports an invariant violation, deadlock, or temporal property failure, (2) You have a counterexample trace and need to fix the corresponding code, (3) You need to understand how a TLA+ violation maps to program-level bugs, (4) You want to validate repairs by re-running TLC. Supports safety properties (invariants), liveness properties (temporal logic), and deadlock detection.

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。

Automatically derives TLA+ properties (invariants, safety, liveness) from natural-language requirements or structured requirement documents. Resolves ambiguities, asks clarifying questions for underspecified requirements, and outputs TLA+-compatible property definitions with semantic explanations. Use when translating system requirements, specifications, or behavioral constraints into formal TLA+ temporal logic properties for verification with TLC model checker.

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。

Automatically infer formal correctness properties from Verilog/SystemVerilog RTL code and generate SystemVerilog Assertions (SVA). Identifies control-flow invariants (mutual exclusion, valid-ready handshakes, pipeline ordering, safety properties), liveness expectations, and temporal properties. Use when working with RTL designs that need formal property generation, when adding assertions to existing RTL, or when users ask to infer properties, generate assertions, or create formal specifications from hardware designs.

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。

Translate Python programs to equivalent Lean4 code while preserving semantics and ensuring type safety. Use when users ask to convert, translate, or port Python code to Lean4, or when they need to verify Python algorithms using Lean4's theorem proving capabilities. Handles functions, classes, data structures, control flow, and ensures the generated Lean4 code is well-typed, executable, and can successfully run.

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。

Automatically extract abstract finite-state models in SMV/NuSMV format from source code (C/C++, Java, Python) for formal model checking. Use when users need to: (1) Generate SMV models from program code for verification, (2) Extract state-transition models from protocol implementations, (3) Analyze control flow and data flow to construct formal models, (4) Create models for checking safety and liveness properties, (5) Convert imperative code to declarative state machines. Particularly effective for protocol implementations, concurrent systems, and control logic with clear state transitions.

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。

Checks whether a new version of a repository preserves the behavior observed by tests on the old version. Use this skill when comparing two versions of code to detect regressions, verify refactoring safety, validate bug fixes don't break existing functionality, or ensure backward compatibility. Detects differences in function outputs, exceptions, observable states, and performance between versions. Generates reports highlighting potential regressions (critical, high, medium, low severity), improvements, and areas requiring verification. Triggers when users ask to check for regressions between versions, compare test behavior across versions, verify behavior preservation, or validate that changes don't break existing tests.

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。

Automatically repair code violations of temporal properties using model-checking counterexamples as guidance. Use when working with formal verification results, temporal logic property violations (LTL, CTL), model checking counterexamples, or when asked to repair property violations, fix counterexamples, repair temporal properties, fix model checking violations, or repair code based on counterexamples. Applicable to concurrent systems, state machines, synchronization issues, safety/liveness properties, and resource management violations.

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。

Performs abstract interpretation over source code to infer possible program states, variable ranges, and data properties without executing the program. Reports potential runtime errors including out-of-bounds accesses, null dereferences, type inconsistencies, division by zero, and integer overflows. Use when analyzing code for potential runtime errors, performing static analysis, checking safety properties, or verifying program behavior without execution.

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。

Create ACSL (ANSI/ISO C Specification Language) formal annotations for C/C++ programs. Use this skill when working with formal verification, adding function contracts (requires/ensures), loop invariants, assertions, memory safety annotations, or any ACSL specifications. Supports Frama-C verification and generates comprehensive formal specifications for C/C++ code.

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。

Forecast inventory needs using demand analysis, safety stock calculations, reorder point optimization, and seasonal adjustment

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。

Execute read-only SQL queries against multiple PostgreSQL databases. Use when: (1) querying PostgreSQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.

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。

TanStack Router type-safe file-based routing for React. Use for SPAs, TanStack Query integration, Cloudflare Workers, or encountering devtools, type safety, loader, Vite bundling errors.

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。

ROS 2 engineering: rclcpp/rclpy, colcon/ament, launch, QoS/DDS, tf2/URDF, ros2_control, Nav2, MoveIt 2, sensors, runtime/artifact provenance, and hardware safety. Use for development, review, debugging, and ROS 1 migration to ROS 2. Not for general C++/Python, unrelated middleware, or web/mobile tasks.

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。

当需要为 AI 定义工具接口、设计调用规范、实现工具发现与编排机制时调用此 skill。典型场景包括：设计 AI agent 的工具集、定义 JSON Schema/XML/TypeScript 格式的工具描述、实现工具权限控制与并行调度、设计子代理委托架构。 不适用于：定义 AI 身份（persona-design）、安全规则（safety-guardrails）、记忆存储（memory-system）。 关键 trigger 信号：AI 需要调用外部 API 或服务、工具数量超过 10 个需要发现机制、需要区分工具权限层级、需要子代理并行执行、工具调用涉及敏感操作需要审批。

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。

当需要为 AI 设计记忆存储、检索、应用和更新机制时调用此 skill。典型场景包括：设计持久化记忆架构（用户偏好、历史上下文、项目知识）、定义记忆的创建/读取/更新/删除生命周期、实现静默记忆应用（不在回复中透露记忆内容）、管理敏感记忆边界。 不适用于：定义工具接口（tool-specification）、定义安全规则（safety-guardrails）、定义人格风格（personality-system）。 关键 trigger 信号：AI 需要跨会话记住用户信息、记忆内容可能敏感、需要在回复中隐式应用记忆而非显式引用、用户要求"记住这个"。

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。

当需要为 AI 产品定义核心身份、角色声明和能力边界时调用此 skill。典型场景包括：设计新 AI 产品的 system prompt 首段、为不同场景创建差异化角色（如教学助手 vs 编程代理）、重新定义 AI 与用户的关系框架。 不适用于：纯人格风格调优（应使用 personality-system）、安全规则制定（应使用 safety-guardrails）、工具集成（应使用 tool-specification）。 关键 trigger 信号：产品需要 AI 有明确自我认知、多个角色共享底层能力但身份不同、需要限制 AI 在特定领域内运作、用户会问"你是谁"。

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。

当需要在基础身份之上叠加可切换的人格风格层时调用此 skill。典型场景包括：为同一产品提供多种人格选项（如 GPT-5.1 的 friendly/professional/quirky 模式）、设计人格切换机制、防止人格泄露到用户内容中。 不适用于：定义 AI 的核心角色定位（应使用 persona-design）、设计安全规则（应使用 safety-guardrails）。 关键 trigger 信号：产品需要多种语气风格、用户可切换 AI 性格、需要防止 AI 人格污染用户文本、存在 "personality" 或 "tone" 配置项。

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。

Think and work like an expert HVAC Engineer. Use when a task calls for HVAC Engineer judgment. Reasons from psychrometric state, parallel heating and cooling load paths, and vapor-compression thermodynamics through TRACE/HAP and EnergyPlus load models, ASHRAE 62.1/55/90.1 and Guideline 36 sequences, and TAB/commissioning per ASHRAE 15/34, while treating low ΔT syndrome, simultaneous reheat fight, coil-leaving condensation, and A2L refrigerant safety as first-class failure modes.

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。