'plc' 검색 결과

Assess industrial control systems by mapping devices, network zones, and exposed protocols. The workflow prioritizes observing traffic and read-only checks, then organizes evidence about configuration weaknesses, firmware vulnerabilities, and potential effects on physical processes.

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• Map industrial devices and network zones
• Inspect captured industrial protocol traffic
• Review control system configurations offline

Develop attack detection for SCADA systems that monitor and control industrial equipment. Compare network traffic and process measurements with normal behavior to identify unauthorized commands, altered sensor data, communication disruption, and indicators of known industrial malware.

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• Introducing intrusion detection in a SCADA environment
• Investigating suspected attacks on industrial control systems
• Creating detection rules for industrial protocol abuse

AI-PLC Stage 4 Operation。Backlog（Agent定義があればそれ）に従ってタスクを実行し、成果物生成、検証、ステータス更新、Propagationを行う。

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ai_plc_inception - AI-PLC Stage 2。Goalを分析し、BacklogとSub-Agent Registryを生成する。

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This skill covers detecting sophisticated cyber-physical attacks that follow the Stuxnet attack pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation from operators. It addresses PLC logic integrity monitoring, physics-based process anomaly detection, engineering workstation compromise indicators, USB-borne attack vectors, and multi-stage attack chain detection spanning IT-to-OT lateral movement through to process manipulation.

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This skill covers detecting sophisticated cyber-physical attacks that follow the Stuxnet attack pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation from operators. It addresses PLC logic integrity monitoring, physics-based process anomaly detection, engineering workstation compromise indicators, USB-borne attack vectors, and multi-stage attack chain detection spanning IT-to-OT lateral movement through to process manipulation.

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This skill covers detecting sophisticated cyber-physical attacks that follow the Stuxnet attack pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation from operators. It addresses PLC logic integrity monitoring, physics-based process anomaly detection, engineering workstation compromise indicators, USB-borne attack vectors, and multi-stage attack chain detection spanning IT-to-OT lateral movement through to process manipulation.

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Plan and document studies that assess whether an analytical procedure suits its intended use. Select the governing framework, evaluate calibration, accuracy, precision, and detection limits, and compare methods using acceptance criteria defined before data collection.

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• Planning analytical validation studies and acceptance criteria
• Evaluating calibration, accuracy, and precision
• Estimating and confirming detection and quantitation limits

Compares two or more band-tagged frameworks and produces a framework-neutral topic matrix showing coverage and gaps across all inputs, plus an optional reference-centric PLC crosswalk document when a reference framework is supplied.

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Operational Technology and industrial control system security — Purdue model segmentation, industrial protocol analysis (Modbus, DNP3, S7, EtherNet/IP), PLC/HMI exposure, IEC 62443 alignment, and MITRE ATT&CK for ICS, for authorized and safety-conscious assessments

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ICS/SCADA/OT exploitation - Modbus (502), S7comm (102), EtherNet/IP (44818), DNP3, OpenPLC, Node-RED SCADA, PLC/HMI/coil/holding-register attacks. Use when a target exposes industrial protocols or the goal is to drive a plant to a dangerous state (over-pressure/over-speed/disable interlock) and read the flag the HMI/CCTV reveals.

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Compares two or more band-tagged frameworks and produces a framework-neutral topic matrix showing coverage and gaps across all inputs, plus an optional reference-centric PLC crosswalk document when a reference framework is supplied.

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AI-PLC Stage 2 Inception。IntentとContextからGoalを分析し、BacklogとSub-Agent Registryを生成する。

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AI-PLC Stage 1 Collection。GoalとModeからExecution Contextを確立し、Context Store、intent.yaml、context.yaml、backlog.yamlを生成する。

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Develop a high-performance liquid chromatography method: define separation goals, select column chemistry and mobile phase, optimize gradient and flow conditions, choose the appropriate detector, and evaluate initial method performance for target analytes in solution or complex matrices.

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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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Check industrial process data servers for unauthorized clients and suspicious data patterns using a Python detector. Provides alerts for missing or constant values and investigation checks for attackers moving between IT and operational technology networks.

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• Checking industrial historian servers for unauthorized clients
• Investigating missing or suspiciously constant process data
• Reviewing signs of movement between IT and OT networks

Assess whether a product is mature or declining, then compare extending it, replacing it, or retiring it. Use diagnostic questions, a product portfolio worksheet, and a replacement risk register with backup plans to document the next move.

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• Choosing between a product refresh, replacement, or retirement
• Checking whether a mature product is entering decline
• Setting consistent plans across an aging product line

Build, manage, and search spectral libraries for proteomics. Use when creating or working with spectral libraries for DIA analysis. Covers DDA-based library generation, predicted libraries (Prosit, DeepLC), and library formats.

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Build machine vision inspection systems with MATLAB Visual Inspection Toolbox. Covers the full arc from problem selection through deployment: anomaly detection (Student-Teacher, PatchCore, FastFlow, FCDD), object detection (YOLOX), counting (CounTR, shape matching), gauging and measurement (caliper, fitcircle, fitangle), blob analysis (regionprops, calibrated measurement), shape matching for fixturing and localization, synthetic data generation for training augmentation, and deployment to standalone apps, embedded C/C++, NVIDIA GPUs (TensorRT), NPUs (ONNX), and Beckhoff PLCs. Routes to rule-based tools when problems are geometric and deterministic, AI-based tools when defect appearance varies, and hybrid pipelines combining both.

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Use for authorized OT/ICS security assessment covering Purdue model zoning, PLC/SCADA exposure, industrial protocol discovery, and safe passive-first evaluation.

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Think and work like an expert Food Chemist. Use when a task calls for Food Chemist judgment. Reasons from food matrix effects, aw and lipid oxidation, AOAC-validated HPLC/GC-MS/LC-MS/MS, FoodData Central/FNDDS, and trained sensory panels while treating matrix suppression, accelerated-shelf-life misuse, and untrained-taster data as first-class failure modes.

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Think and work like an expert Analytical Chemist. Use when a task calls for Analytical Chemist judgment. Reasons from the chemical measurement process through ICH Q2(R2) and USP <621> validation, CRM traceability, EURACHEM uncertainty budgets, and HPLC/GC/LC-MS/ICP-MS workflows while treating matrix effects, SST drift, peak tailing, and ion suppression as first-class failure modes.

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Story/Spec と対象リポジトリを起点に、画面UIの「現状」と「変更後」を ASCII Art（AA）ワイヤフレームで描き、Storyに設計決定ログを追記する。target_repoのcomponentを調査して実装状況にground。抽象仕様を"見える画面"まで収束させる可視化フェーズに使う。特定ツール連携の専用版スキルのローカル/汎用翻案。

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