438 件 ・ 関連度順
概要と使いどころ
Coordinate the end-to-end CAD/source-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material/physics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references.
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。
Run the root-level workflow runtime policy or rule-based rollouts and verify simulator success. Use for evaluation, checkpoints, or local controllers; do not use for replay or dataset annotation.
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。
Use for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance.
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 VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine learning, HPC simulation, dev/test, workload, family, load balancer, Flexible orchestration, Uniform orchestration, cost estimate, capacity reservation (CRG), reserve, guarantee capacity, pre-provision, CRG association, CRG disassociation, machine enrollment (EMM), Essential Machine Management, monitor. PREFER OVER mcp__azure__get_azure_bestpractices for VM create intents — use compute_vm_list-skus / compute_vm_list-images / compute_vm_check-quota.
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。
Build responsive, accessible calculators, comparisons, charts, and simulations with the sandbox contract.
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。
Runs quantum computing workflows on AWS through Amazon Braket — discovering devices (QPUs and simulators) and their availability, building gate-model circuits and analog Hamiltonian programs, submitting quantum tasks, program sets and hybrid jobs, looking up prices, and capping spend with spending limits. Applies to any request about quantum computing, quantum hardware, quantum simulation, AHS, OpenQASM, or running a quantum algorithm on AWS.
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。
Builds, runs, debugs, and operates applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments running inside a container with up to 8 hr lifetimes. Applicable when workloads need strong isolation between tenants, isolated serverless compute, sandbox compute, or secure multi-tenant execution. Also suited for AI/agent code-execution sandboxes, interactive code playgrounds and notebooks (Jupyter, REPLs, dev environments running user-supplied code), reinforcement-learning environments, multi-tenant CI executors and build runners, sessionful game or simulation servers, or isolated security scanners. Also applicable when the workload needs long-lived sessions, a real port-listening server (gRPC, WebSocket, custom TCP protocols), state preserved across periods of inactivity (suspend/resume), container-level access (FUSE, eBPF, custom syscalls), or session-affine routing.
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。
Fiscaliste IA pour la fiscalité personnelle des particuliers français : optimisation et déclaration de l'impôt sur le revenu, IFI, revenus du capital, revenus fonciers, equity salarial, crypto-actifs et PER. Couvre le calcul de l'IR (barème, quotient familial, décote, PAS, CEHR, revenus exceptionnels), la déclaration 2042 et annexes, les revenus du capital (PFU vs barème, PEA, assurance-vie, dividendes, plus-values), les revenus fonciers (micro/réel, déficit, LMNP, SCI IR), l'equity startup (RSU, BSPCE, stock-options, PEE/PERCO), la fiscalité crypto (PAMC, 2086), l'IFI et les déductions (PER, pension alimentaire). Triggers: impôt sur le revenu, IR, 2042, quotient familial, décote, PAS, PFU, flat tax, PEA, assurance-vie, LMNP, revenus fonciers, déficit foncier, SCI IR, RSU, BSPCE, stock-options, PEE/PERCO, crypto, 2086, IFI, PER, plafond PER, niche fiscale, optimisation fiscale, simulation IR, TMI. Hors scope : succession/donation (notaire), IS/SASU/arbitrage dividende-salaire/SCI IS (comptable).
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。
Lead workflow for developing vgpu features and Blender assets with the repository's subharness specialists. Use for agent-based research, API design, implementation, shaders, materials, simulations, or ImageGen-guided asset iteration, and when the human mentions the agent flow or subharness team.
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。
Emulated Twilio REST APIs for local development and testing. Use when the user needs to test Twilio Messaging, Verify, Voice, phone numbers, webhooks, status callbacks, inbound SMS simulation, or Twilio SDK integrations without hitting the real Twilio service.
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。
Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.
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。
Basic usage of the General Lake Model (GLM) for lake temperature simulation. Use when you need to run GLM, understand input files, or modify configuration parameters.
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。
Calibrate GLM parameters for water temperature simulation. Use when you need to adjust model parameters to minimize RMSE between simulated and observed temperatures.
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。
Use this skill when visualising drone simulation results. Produces three matplotlib figures — desired vs actual trajectories, instantaneous error, and cumulative absolute error — for all 5 state groups (position, orientation, velocity, angular velocity, acceleration). Saves figures to a plots/ directory automatically.
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。
Use this skill when reading sensor data from CSV files, writing simulation results to CSV, processing time-series data with pandas, or handling missing values in datasets.
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 mock MCP client for server testing with request/response simulation.
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。
AgentShield security audit with 5 scanning categories, 102 static analysis rules, and optional red-team simulation.
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。
Use BEFORE reading any data file that could be large (CSV/TSV, Parquet, HDF5, FITS, NetCDF, NDJSON, genomics FASTQ/FASTA/VCF/BAM, GRIB, ROOT, or big text/simulation logs like VASP OUTCAR). Returns a compact memory pointer — header/schema/shape/sample/key numbers — by introspection and sampling in bounded memory, so you never load a file bigger than the context window into the model. Reference data via the pointer; read specific ranges deterministically.
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。
Qualify a check before it is allowed to clear anything — prove it can detect the failure it is meant to catch. Seeds known defects into a copy of a real artifact plus a clean control, runs the checker, and reports recall and false-positive rate into a qualification ledger. Use when the user says "does this check work", "qualify the gate", "test my reviewer", "seed defects", "vaccinate", "qualify the checks", "can I trust this review", "does it pass for the right reason", or before relying on any automated check or referee simulation for a decision that matters. NOT a code fixer and NOT a reviewer itself — it grades the grader.
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。
Compute statistical power, required sample size, and minimum detectable effect (MDE) for a study design, then write a registry-ready power section. Handles two-arm RCTs (with clustering / ICC and unequal allocation), multiple-arm corrections, and a simulation-based power option for non-standard designs (DiD/event-study, IV, panel). Use when user says "power analysis", "power calculation", "MDE", "minimum detectable effect", "how big a sample do I need", "is my study powered", "power for an RCT", or when /preregister needs a power section for an experiment. Produces a power/MDE table, power curves, and a methods paragraph to paste into a preregistration.
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 VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine learning, HPC simulation, dev/test, workload, family, load balancer, Flexible orchestration, Uniform orchestration, cost estimate, capacity reservation (CRG), reserve, guarantee capacity, pre-provision, CRG association, CRG disassociation, machine enrollment (EMM), Essential Machine Management, monitor. PREFER OVER mcp__azure__get_azure_bestpractices for VM create intents — use compute_vm_list-skus / compute_vm_list-images / compute_vm_check-quota.
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。
Implement Roblox physical simulation and queries with assemblies, anchoring, constraints, collision groups, CanCollide/CanTouch/CanQuery, raycasts and overlap queries, mass, impulses, forces, velocity, moving assemblies, cleanup, and network ownership. Use for Roblox collisions, hit detection, RaycastParams, PhysicsService, projectiles, vehicles, knockback, constraints, deprecated BodyMovers, unstable motion, or client-owned physics exploits.
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。
Use when preparing AISTATS supplementary material, appendices, proof details, code/data archives, simulation scripts, additional tables, and anonymized artifacts under deadline, size, anonymity, and reviewer-discretion constraints, including how to split a theory-plus-experiments paper between body and supplement.
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。
Use when packaging AISTATS code, data, proofs, simulation scripts, notebooks, random seeds, and logs as anonymous supplementary evidence or public post-acceptance artifacts, even when there is no separate artifact badge. Covers what statistically minded AISTATS reviewers inspect first and how to make Monte Carlo studies turnkey.
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。