「physica」の検索結果

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.

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。

This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

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。

Design and execute a social engineering penetration test including phishing, vishing, smishing, and physical pretexting campaigns to measure human security resilience and identify training gaps.

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。

Hardware Security Modules (HSMs) are tamper-resistant physical devices that safeguard cryptographic keys and perform cryptographic operations in a hardened environment. Keys stored in an HSM never lea

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。

Read-only measurement of an agent's autonomy debt from its approvals log: how many times it asked the human for permission in a window, how many asks expired unanswered, and which classes they fall into (machine noise, login or 2FA, physical UAC, money). Use to find asks the agent should have decided itself and to cut needless interruptions. Triggers: /ask-debt, autonomy debt, how often do I ask, expired asks.

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。

Delegate real-world tasks to human workers via the OpenHumancy API. Use when the user needs physical actions (field verification, photography, deliveries, mystery shopping), human judgment, or any task that requires a person. Manages the full lifecycle — worker search, task creation, chat, payments on the TON blockchain.

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。

Pull design principles from architecture, fashion, film, industrial design, and physical spaces into screen-based design. Use when seeking inspiration beyond digital, expanding creative range, or building cross-disciplinary design vocabulary.

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。

Hardware Security Modules (HSMs) are tamper-resistant physical devices that safeguard cryptographic keys and perform cryptographic operations in a hardened environment. Keys stored in an HSM never lea

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。

3D print models generated with Meshy AI. Handles printability analysis, slicer integration, multi-color printing guidance, and print-optimized download workflows. Use when the user mentions 3D printing, slicing, Bambu, OrcaSlicer, Prusa, Cura, Creality Print, or wants to print a figurine, miniature, or physical 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。

Create, edit, or debug parametric 3D CAD parts in CadQuery (Python) that export to STEP for SolidWorks. Use when the request involves CadQuery, .step/.stl export, or modelling a physical part — enclosure, bracket, PCB, motor mount, cam, gear. Also use when a STEP will not open in CAD, or geometry looks correct in STL but is invalid as a solid. Applies a verification discipline before export and documented OCC pitfalls.

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。

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.

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。

This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

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。

Design and execute a social engineering penetration test including phishing, vishing, smishing, and physical pretexting campaigns to measure human security resilience and identify training gaps.

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。

Hardware Security Modules (HSMs) are tamper-resistant physical devices that safeguard cryptographic keys and perform cryptographic operations in a hardened environment. Keys stored in an HSM never lea

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。

Solve quantitative problems in biophysics — pharmacokinetics (PK volume of distribution, clearance, half-life), epidemiology (R0, attack rate), toxicology (LD50, NOAEL), population genetics (Hardy-Weinberg, Fst), enzyme kinetics (Michaelis-Menten), thermodynamics. Use for first-principles quantitative biology calculations, dose calculations, exposure assessment, and biophysical-property estimation.

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。

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

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。

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output 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。

Orchestrates an end-to-end long-read structural-variant pipeline - basecalling to minimap2 alignment (platform-matched preset) to Sniffles2/cuteSV/pbsv calling to optional assembly-based calling (dipcall/PAV) to two-step .snf cohort merging to Truvari benchmarking - chaining ONT and PacBio HiFi runs while handing the SV signal mechanism off to the component skills. Use when running a long-read SV workflow from reads to a benchmarked callset, choosing the minimap2 preset and SV caller by platform and goal, deciding when long reads are worth it for the insertions and repeat-mediated SVs short reads physically miss, building a joint-genotyped cohort with the two-step .snf design, or parameterizing a Truvari benchmark against GIAB HG002 Tier 1 plus CMRG. Not for the SV signal mechanism itself (see variant-calling/structural-variant-calling) or short-read SV.

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。

Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.

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。

Quality control for IMC/MIBI data across pixel, channel, image, slide, and batch levels, covering Poisson-count SNR (cell-level Gaussian-mixture and empty-channel comparison), spillover-matrix QC (the three physical sources), drift and the missing EQ-bead analog, acquisition artifacts, and sample-of-origin batch effects. Use when deciding whether to keep or drop a channel, ROI, or slide, distinguishing a dim antibody from a failed one, reading a spillover matrix, or diagnosing batch-driven clustering before 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。

Query protein-protein and gene interaction databases (STRING, BioGRID, IntAct, SIGNOR, Reactome, HuRI, HuMAP, OmniPath, ConsensusPathDB, DIP). Use when building PPI networks, choosing between physical vs functional vs genetic interactions, signed/directed vs undirected, high-throughput vs curated, picking confidence thresholds, aggregating across resources, or navigating license constraints. Encodes the database decision matrix, STRING v12 channel semantics, OmniPath as meta-database, SIGNOR for signed signaling, and per-resource rate limits.

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。

Real-world mimicry with textured surfaces, 3D effects, and familiar physical metaphors for intuitive digital interfaces.

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。

A playful, arcade-inspired interface for games — built on the VT323 pixel typeface, hard-edged 0px corners, chunky pill buttons that physically press into solid offset blocks

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。

Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.

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。