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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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Build content moderation features in Java with Azure AI Content Safety. Analyze text and images for harmful content, retrieve severity scores by category, and manage custom blocklists, which are lists of terms to flag.

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• Checking submitted text for harmful content
• Analyzing images from files or URLs
• Managing custom lists of prohibited terms

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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Build text and image checks in TypeScript with Azure AI Content Safety. The skill covers harmful content categories, severity scores, and custom text blocklists, with examples for authentication, error handling, and decisions about allowing content.

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• Implement harmful text checks for user posts
• Analyze images and retrieve category severity scores
• Create and maintain custom prohibited term lists

Integrate harmful-content checks into Python applications using Azure AI Content Safety. Analyze text and images by category and severity, and manage custom text blocklists to help review user submissions or AI-generated content before display.

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• Checking user-submitted text for harmful content
• Analyzing harmful-content categories in uploaded images
• Reviewing AI-generated text before displaying it

Configure safety checks for LLM applications with NeMo Guardrails. Covers attempts to bypass safety rules, input and output validation, masking personal information, and checking factual claims through programmable rules and integrations.

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• Detecting instructions that try to bypass safety rules
• Masking personal information before processing
• Adding factual checks to generated answers

Build safety checks for LLM prompts and responses using Meta's LlamaGuard moderation model. Classify potentially harmful content, block flagged messages, serve checks through an API, and integrate them with NeMo Guardrails.

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• Checking prompts before sending them to an LLM
• Filtering responses before displaying them
• Building a moderation API

Add safety checks to LLM applications with NeMo Guardrails. This skill guides input and output validation, detection of attempts to bypass safety rules, personal information masking, and checks for unsupported or inaccurate answers.

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• Detect attempts to bypass an AI app's safety rules
• Mask personal information before processing
• Add fact-checking to generated answers

Build safety checks for LLM prompts and responses using Meta's LlamaGuard. This skill covers content classification, batch moderation, serving a moderation API, and connecting checks to NeMo Guardrails.

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• Screening user prompts before sending them to an LLM
• Checking generated responses before displaying them
• Building an API for content safety checks

[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.

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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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Knowledge pack reconstructed from JSP 815 Defence Safety Management System V1.3 (Ministry of Defence, Open Government Licence v3.0): the SMS Framework, all 12 Elements with their Expectations and Performance Statement themes, and Annexes A to F plus H. Covers the Defence Safety Function and which JSP applies, leadership and safety culture, organisation and SEMS applicability, legislation and exemption certificates, risk assessment and safety cases, supervision and contracting, competence and letters of delegation, equipment and infrastructure safety, performance and incident management, and assurance, audit, and the ISO 45001 clause mapping. SCOPE LIMITS: single-source (JSP 815 V1.3 with Annex H V1.2); carries no DEF-STAN 00-051 or DEF-STAN 00-970 content, no JSP 375, JSP 816; acquisition safety policy is in jsp-376, no Annex G (Assurance Toolkit), and no ISO 45001 text (clause numbers only). This pack is reconstructed reference notes, not MOD guidance, and has no official status.

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Knowledge pack synthesized from JSP 376 Defence Acquisition Safety Policy V1.1 (April 2024, Ministry of Defence, Open Government Licence v3.0): acquisition safety requirements, safety artefacts, and their application across the standard acquisition lifecycle. Covers the policy frame and legislative baseline, SRO and User responsibilities, safety management and safety requirements, test and evaluation and the safety case, supervision and approvals decision support, and the seven lifecycle phases. SCOPE LIMITS: single-source (JSP 376 V1.1); MOD policy and guidance, not statute; builds on JSP 815 and cites JSP 655, neither reproduced here (use jsp-815 for the SMS); Chapter 4 (Non-Standard Acquisition) is unpublished in V1.1 and is not in this pack; no DEF-STAN text (Def Stan 00-056 is named, not quoted); no other JSP reproduced. Reconstructed reference notes, not MOD guidance, and no official status.

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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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在AI代理执行危险命令前进行检查，并将文件编辑限制在指定目录内。可单独启用或组合使用保护模式，管理生产作业、自主运行、迁移和部署时的操作。

• 检查生产作业中的危险命令
• 将编辑范围限制在指定目录
• 为自主运行组合启用保护

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

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• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

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• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses

[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.

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