「safety」の検索結果

Knowledge base from the FAA System Safety Handbook (Dec 30, 2000). Use for system safety as a specialty within systems engineering: the five-step safety risk management process (mandated by FAA Order 8040.4 and the AMS), the severity-by-likelihood risk matrix and acceptance bands, the Safety Order of Precedence, system-description models (5M, SHEL(L)), the hazard-analysis activities (PHL/PHA/RHA/SSHA/SHA/O&SHA/HHA), integrated system hazard analysis, the analysis techniques (FTA, FMEA/FMECA, Fault Hazard, Common Cause, Sneak Circuit, Energy Trace), closed-loop hazard tracking (Safety Action Record), and the specialty domains the handbook covers — acquisition-lifecycle safety and contracting (OSA/CSA/SSPP/ISSPP), software safety (control-capability ranking, SHCM, DO-178B), test & evaluation safety, facilities safety, commercial launch safety (AST licensing, expected casualty, MPL/DAMP), system safety training, and Operational Risk Management (ORM) with human/organizational factors. Scope limits: this is a year-2000 FAA how-to; it predates the modern FAA/ICAO Safety Management System (SMS) and Just Culture regulatory frameworks, and many cited orders, advisory circulars, and launch-licensing rules have since been restructured. It is FAA/NAS-centric, leans on MIL-STD-882C, and does not define the SE life-cycle processes themselves (use nasa-npr-7123 / dau-se-guidebook) or reproduce third-party standard text (MIL-STD-882, DO-178B, ISO/IEC/IEEE).

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Knowledge base from NASA System Safety Handbook Volume 2 (NASA/SP-2014-612). Use for risk-informed safety cases (RISC), integrated safety analysis (ISA), adequate safety concepts, safety objectives and requirements, acquirer/provider roles, graded safety analysis, claims-evidence-argument structure, SSMP development, and RISC evaluation. Does not cover nuclear or chemical safety domains, occupational health regulations, or NASA-specific programmatic requirements beyond system safety.

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Generates BYO custom safety policies for NVIDIA Nemotron content-safety guardrails — Nemotron-Content-Safety-Reasoning-4B (text) and multimodal Nemotron-3-Content-Safety. Produces a Markdown policy, JSON taxonomy, and drop-in inference prompts. Maps rough words or an existing policy to V2 categories, adding custom categories or topic-following rules.

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Use this skill when the user asks how to build, measure, or improve safety culture in a motor carrier — leading vs lagging safety indicators, blame-free reporting, near-miss programs, safety committee structure, monthly safety meetings, ATA Safety Management Program guidance, and how to evaluate cultural maturity. Reference ATA SMP + DOT safety culture research.

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[omh] Agent or automation safety risks: review prompt, tool, secret, dependency, destructive-action, and explicit local plugin risks before agent or code execution. Use when the user says: security-safety-review, security safety review, ai coding safety, agent safety review, prompt injection review, tool permission review, secret exposure review, destructive action review.

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。

Azure AI Content Safety SDK for Python workflow skill. Use this skill when the user needs Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Safety, privacy, cost management, and frank advice for building always-on AI agents with episodic memory. Covers data hygiene, privacy risk surfaces, runaway cost prevention, scope creep, psychological effects of persistent AI companions, and responsible deployment patterns. This is the skill that tells you what can go wrong and how to prevent it. Activate on: "agent safety", "always-on agent privacy", "agent cost control", "persistent agent risks", "AI companion safety", "agent data hygiene", "runaway agent costs", "parasocial AI risk", "/always-on-agent-safety". NOT for: architecture design (use always-on-agent-architecture), input design (use always-on-agent-inputs), application brainstorming (use always-on-agent-applications), healthcare compliance specifically (use hipaa-compliance).

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Use when a safety director, operations manager, or fleet owner asks for templates, topics, or structure for recurring safety meetings — weekly driver meetings, monthly safety committee meetings, quarterly all-hands safety reviews, or post-incident safety stand-downs.

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Safety-case design for always-on agents with episodic memory. Covers data hygiene, privacy and security risk surfaces, cost controls, scope, user dependency concerns, retention, incident containment, and responsible deployment evidence. It identifies hazards and asks for named controls, tests, owners, and residual uncertainty; it does not provide legal, clinical, youth-safety, or jurisdictional determinations. Activate on: "agent safety", "always-on agent privacy", "agent cost control", "persistent agent risks", "AI companion safety", "agent data hygiene", "runaway agent costs", "parasocial AI risk", "/always-on-agent-safety". NOT for: architecture design (use always-on-agent-architecture), input design (use always-on-agent-inputs), application brainstorming (use always-on-agent-applications), healthcare compliance specifically (use hipaa-compliance).

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AIエージェントの危険なコマンドを実行前に確認し、ファイル編集を指定ディレクトリ内に制限します。単独または併用のモードで、本番作業や自律実行時の操作を管理します。

• 本番作業で危険なコマンドを確認
• 特定ディレクトリだけ編集したいとき
• 自律実行時に保護を併用したいとき

[omh] Agent control of a robot, 3D printer, or IoT relay: gate robots, 3D printers, IoT relays, sensors, and lab hardware before trials; use external-connector-readiness for provider or connector adoption and toolbelt-readiness for missing control tools. Use when the user says: physical-device-readiness, physical device readiness, device safety readiness, physical device safety, hardware safety gate, 3d printer readiness, 3D printer safety, snapmaker printer safety.

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Runs environment, health and safety on an operating site — hazard controls in order of effectiveness, incident recording and reporting obligations, and safety measurement that does not reward silence. Use this to build or audit a safety program, decide what controls a hazard actually needs, work out whether an injury is recordable or reportable, investigate an incident, or fix safety metrics that have stopped producing reports.

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Create, populate, and manage safety analysis spreadsheets (FMEA, FHA, HARA, custom) and fault trees (FTA) in Safety Analysis Manager. Use when the user asks to perform FMEA, hazard analysis, fault tree analysis, safety analysis, or work with Safety Analysis Manager documents. Requires Simulink Fault Analyzer.

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。

Azure AI Content Safety SDK for Python workflow skill. Use this skill when the user needs Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Azure AI Content Safety SDK for Java workflow skill. Use this skill when the user needs Build content moderation applications using the Azure AI Content Safety SDK for Java and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Safety guardrails for destructive operations. Wraps workflows and agent actions in configurable safety modes: CAREFUL (warns before destructive commands), FREEZE (restricts file edits to one directory), GUARD (both modes combined). Use when working in production, running destructive scripts, or debugging in sensitive areas. Voice triggers: "be careful", "safety on", "guard mode", "freeze scope". Also used automatically by the workflow engine when a workflow preset has safety: "guard" enabled.

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Flags where a P&ID draft raises a process safety question and points to the candidate evidence an engineer would review (HAZOP register, relief study, SIL assessment, isolation philosophy), as questions and quoted passages with UNKNOWN for missing documents, plus safety functions, relief devices and isolation requirements carried over as stated. Never rates, sizes, classifies, resolves or declares protection adequate. Use when the user asks to "write the process safety flags", "produce the process safety section of the P&ID enrichment", "list which protection questions this P&ID raises" or "map the HAZOP and relief study evidence to the draft" after gate G2, once the piping and instrumentation sections exist. Do not use for proposing loops, alarms or trips from the control philosophy, use instrumentation-and-control-enrichment instead; do not use for checking identifiers, use tagging-and-numbering. Drafts for human review; never approves, authorises or signs off.

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医療アプリの公開前に、診療判断の支援ロジック、患者情報の漏えい、データ整合性などを検証するスキル。重大なテストの失敗でデプロイを止める仕組みを示します。

• 電子カルテの公開前に検証したいとき
• 薬物相互作用や投与量の変更を検証
• 権限変更後の患者情報漏えいを確認

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo 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。

Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety 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。

Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety 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。

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo 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。

Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification. Triggers: "azure-ai-contentsafety", "ContentSafetyClient", "content moderation", "harmful content", "text analysis", "image analysis".

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Expert knowledge for Content Safety in Foundry Control Plane development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Content Safety APIs, Docker containers, text blocklists, media provenance, or groundedness checks, and other Content Safety in Foundry Control Plane related development tasks. Not for Azure Content Understanding in Foundry Tools (use azure-content-understanding), Azure Security (use azure-security), Azure Sentinel (use azure-sentinel), Azure Defender For Cloud (use azure-defender-for-cloud).

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。