30 件 ・ 関連度順
概要と使いどころ
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 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。
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。
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".
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".
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。
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.
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。
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.
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。
電子カルテに組み込む臨床判断支援を設計し、薬の相互作用や用量の検証、臨床スコアの計算、警告の重要度に応じた画面制御とテストを実装するスキル。
Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.
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。
Deploys Llama Guard 3 safety classification, NeMo Guardrails programmable dialogue rails, and LLM Guard input/output scanner pipelines as complementary runtime defenses that inspect and constrain LLM prompts and responses. Use when adding a production runtime safety layer to an LLM, RAG, or agent application to block jailbreaks, prompt injection (OWASP LLM01), toxic content, or sensitive-data leakage before it reaches or leaves the model.
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。
Classifies agent tasks into 4 risk tiers (GREEN/YELLOW/RED/CRITICAL). Use when assessing action reversibility before committing to an approach.
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。
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.
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。
"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'.".
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.
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。
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).
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.
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.
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。
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.
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.
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.
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.
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。
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
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。
Assess compliance with the EU General-Purpose AI (GPAI) Code of Practice under the AI Act (Regulation (EU) 2024/1689). Use when asked to determine GPAI model provider obligations, assess Code of Practice compliance, evaluate transparency or copyright requirements for GPAI models, check systemic risk obligations, build a GPAI compliance roadmap, or prepare for AI Office requests. Covers all three Code chapters (Transparency, Copyright, Safety & Security), the 12 commitments and their measures, systemic risk classification (Article 51), provider obligations (Articles 53 and 55), open-source exemptions, enforcement timeline, Commission/AI Office supervision, and DACH-specific overlays such as BSI cybersecurity and works council implications. Triggers on phrases like "GPAI compliance", "Code of Practice assessment", "GPAI model obligations", "systemic risk evaluation", "AI Act transparency requirements", "GPAI copyright policy", "general-purpose AI compliance".
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。