1,130 件 ・ 関連度順
概要と使いどころ
Analyze mining safety management systems including incident investigation quality (ICAM, TapRooT, BowTie analysis), hazard identification and risk register completeness, critical control verification per ICMM framework, occupational health exposure monitoring (respirable dust, silica.
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Analyze fleet safety programs including driver behavior scoring, accident trend analysis, CSA BASIC score monitoring, DOT audit readiness, Hours of Service compliance, and drug and alcohol testing programs.
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Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.
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planning-review-activation-safety-check
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planning-review-activation-safety-check에 run-gate 보존 확인 추가
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Comprehensive AI safety and alignment audit framework for businesses deploying AI agents. Built around the UK AI Security Institute Alignment Project standards (2026), EU AI Act requirements, and N...
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ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) prediction for drug candidates. Use for assessing drug-likeness, PK properties, and safety risks early in drug discovery. Keywords: ADMET, PK, toxicity, drug-likeness, DILI, hERG, bioavailability
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Python code quality review lens. SOLID principles, dead code, exception handling, resource leaks, thread safety, performance. Triggered by "quality" ("품질"), "SOLID", "dead code" ("데드코드"), "exception", "resource leak", "thread safety" keywords.
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Drafts an Adverse Event Reporting Policy compliant with 21 CFR 312.32 (IND safety reporting), 21 CFR 314.80 (postmarketing), and ICH E2A, with multi-jurisdictional overlays (EMA, PMDA, Health Canada). Covers seriousness/causality frameworks, expedited reporting timelines, roles, documentation standards, training mandates, and QA mechanisms. Use when drafting or updating an adverse event reporting policy, pharmacovigilance policy, AE/SAE reporting SOP, or safety reporting framework for a pharmaceutical company, CRO, biotech, or clinical research institution.
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Plan and implement infrastructure, containers, CI/CD, deployment safety, observability, and rollback changes. Use for Docker, IaC, cloud runtime, Kubernetes, pipeline, deployment, or operational reliability work. Does not read dotenv files or approve/release deployments.
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Architecture and systems design for building always-on AI agents with episodic memory. Covers the memory hierarchy (core/recall/archival), persistence layers, agent server infrastructure, vector stores, and framework selection. Provides concrete deployment patterns for agents that maintain identity and learn across sessions. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-patterns).
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What you can build and do with an always-on AI agent that has episodic memory. Covers concrete product ideas, workflows, emergent capabilities from persistence plus memory, and real-world examples of deployed persistent agents. Helps you go from "I have the architecture" to "here's what it actually does for me." Activate on: "what can an always-on agent do", "persistent agent use cases", "agent applications", "proactive agent ideas", "what to build with episodic memory", "always-on agent product", "personal AI assistant ideas", "/always-on-agent-applications". NOT for: building the architecture (use always-on-agent-architecture), designing inputs (use always-on-agent-inputs), safety and privacy (use always-on-agent-safety).
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How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context capture (screen, audio, calendar), context window budgeting, and retrieval strategies that keep the agent grounded in what's actually happening. Activate on: "what should the agent observe", "context inputs for agent", "ambient context capture", "agent triggers", "agent input design", "screenpipe integration", "context window budget", "what data to feed my agent", "/always-on-agent-inputs". NOT for: memory architecture and storage (use always-on-agent-architecture), application ideas (use always-on-agent-applications), safety concerns (use always-on-agent-safety).
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Advises evaluating creatinine clearance or estimated glomerular filtration rate prior to intravenous bisphosphonate administration to guide dosing and ensure safety. Triggered when a clinician is planning to give zoledronic acid or pamidronate and asks about renal safety checks.
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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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Build content moderation applications using the Azure AI Content Safety SDK for Java.
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Expert-level industrial automation covering PLCs, SCADA, industrial networks, safety systems, robot integration, and Industry 4.0 technologies.
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Expert-level nuclear energy covering fission reactor physics, reactor types, safety systems, nuclear fuel cycle, waste management, and advanced reactor concepts.
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Expert-level nanomaterials engineering covering synthesis, characterization, properties at nanoscale, nanocomposites, safety, and industrial applications of nanomaterials.
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Expert-level process control covering feedback and feedforward control, PID tuning, advanced process control, control loop design, and process safety instrumentation.
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Expert-level clinical engineering covering medical equipment management, hospital technology systems, healthcare IT integration, safety standards, and health technology assessment.
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Expert-level construction management covering project planning, scheduling, cost estimation, procurement, quality control, safety, and contract administration.
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Método da Anthropic para treinar IA inofensiva através de auto-aprimoramento. Abordagem em duas fases - aprendizado supervisionado com auto-crítica/revisão, depois RLAIF (RL from AI Feedback). Use para alinhamento de segurança, redução de outputs prejudiciais sem rótulos humanos. Alimenta o sistema de segurança do Claude.
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Framework de segurança em runtime da NVIDIA para aplicações LLM. Com detecção de jailbreak, validação de entrada/saída, verificação de fatos, detecção de alucinações, filtragem de PII, detecção de toxicidade. Usa DSL Colang 2.0 para rails programáveis. Pronto para produção, executa em GPU T4.
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