“safety classification”的搜索结果

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

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

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

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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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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Supports preparation for EU medical device regulation reviews by organizing device classification, technical documentation, clinical evidence, and post-market monitoring. Includes a tool to identify documentation and compliance gaps and prioritize the next steps.

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• Documenting a device classification rationale under MDR
• Checking technical documentation before a review
• Planning clinical evaluation and identifying evidence gaps

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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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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Add checks to LLM prompts and responses using Llama Guard 3, NeMo Guardrails, and LLM Guard. Configure content screening, conversation boundaries, and sensitive-data filtering, then measure blocking and false-positive rates with labeled test prompts.

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• Adding runtime safety checks to LLM, RAG, or agent apps
• Screening prompts for injection and jailbreak attempts
• Filtering sensitive or toxic model responses

Modelo de moderação especializado 7-8B do Meta para filtragem de entrada/saída de LLM. 6 categorias de segurança - violência/ódio, conteúdo sexual, armas, substâncias, automutilação, planejamento criminal. Precisão de 94-95%. Deploy com vLLM, HuggingFace, Sagemaker. Integra com NeMo Guardrails.

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Proposes the instrumentation and control content a P&ID adds to an accepted PFD process model: instruments drawn carried over as facts, measurement points and control loops the control philosophy requires, tag structure per the project instrument numbering procedure, system interfaces, and alarms, interlocks, trips and fail actions as quoted. Never assigns safety functions, ranges or setpoints. Use when the user asks to "run the instrumentation and control enrichment", "add the instrument section to the P&ID draft", "list the control loops the philosophy requires" or "carry the PFD instruments into the P&ID" after the piping section. Do not use for safety flags, SIL or trip classification, use process-safety-flags instead; do not use to assign tag numbers, use tagging-and-numbering. Drafts for human review; never approves, authorises or signs off.

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用于开发嵌入电子病历的临床决策支持，涵盖药物相互作用、剂量验证和临床评分，并按警报严重程度设计界面行为及检测漏报警的测试。

• 实现药物相互作用与过敏检查
• 根据患者信息验证剂量
• 实现NEWS2等临床评分

Check untrusted text before passing it to a model or executing it. Using AIDefence, it provides safety checks, detailed threat classifications, confidence scores, and scanning for attempts to override instructions or extract data.

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• Checking user submissions before model processing
• Scanning API payloads and webhook data
• Inspecting tool outputs for hidden instructions

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

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• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

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• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

Classifies agent tasks into 4 risk tiers (GREEN/YELLOW/RED/CRITICAL). Use when assessing action reversibility before committing to an approach.

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Plan a hiking tour with trail selection by difficulty (SAC/UIAA), time estimation using Munter's formula, elevation analysis, and safety assessment. Covers multi-day hut-to-hut tours, day hikes, and alpine routes with terrain classification and group fitness considerations. Use when planning a day hike or multi-day trekking tour, selecting trails appropriate for a group's fitness and experience, estimating realistic hiking times, or planning hut-to-hut tours with overnight logistics.

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"Analyze inventory health using turnover ratios, ABC classification, safety stock calculations, and stockout vs overstock diagnostics. Use this skill when the user needs to optimize inventory levels, reduce carrying costs, prevent stockouts, or classify products by inventory priority — even if they say 'we have too much stock', 'we keep running out of bestsellers', 'how much safety stock do we need', or 'which products should we focus on'.".

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Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

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Knowledge base from NASA NPR 7150.2D — NASA Software Engineering Requirements. Use for NASA's Agency-wide software-engineering requirements: the software classification scheme (Classes A–F), the Requirements Mapping Matrix and class-driven applicability, tailoring and Technical Authority governance, the SWE-### 'shall' requirements across software management (Ch 3) and the engineering life cycle (Ch 4), the supporting disciplines (configuration management, risk, peer reviews/inspections, measurement, non-conformance — Ch 5), safety-critical and cybersecurity requirements, IV&V applicability, traceability, and the Appendix A definitions. Does not cover detailed software-assurance/safety procedures (see NASA-STD-8739.8), implementation how-to and document content (see NASA-HDBK-2203), the systems-engineering process framework (see NPR 7123.1), or program/project management (see NPR 7120.5).

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Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

中文简介正在准备中。当前显示原文说明。

Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

中文简介正在准备中。当前显示原文说明。

Chemical safety and toxicology assessment integrating ADMET-AI predictions, CTD toxicogenomics, PubChemTox experimental data, GHS/IARC hazard classification, and exposure-context analysis. Use for chemical hazard identification, occupational/consumer-product toxicity, dose-response evaluation, and acute (LD50) vs chronic toxicity assessment. Distinguishes drug toxicity from environmental chemical toxicity.

中文简介正在准备中。当前显示原文说明。

Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

中文简介正在准备中。当前显示原文说明。