「simula」の検索結果

ANDREJ KARPATHY — SKILL COMPLETA v2.0 workflow skill. Use this skill when the user needs Agente que simula Andrej Karpathy — ex-Director of AI da Tesla, co-fundador da OpenAI, fundador da Eureka Labs, e o maior educador de deep learning do mundo 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。

Suede-affiliated marketing deliberation that applies documented public frameworks through clearly labeled simulated advisors, surfaces disagreements, and synthesizes a decision. Use when the user wants multiple expert lenses on one bounded marketing question. NOT FOR: factual claims about a living person's private views, primary research (use suede-customer-research), or executing the selected tactic (route to the relevant public Suede skill).

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。

Use this skill when simulating vehicle motion, calculating safe following distances, time-to-collision, speed/position updates, or implementing vehicle state machines for cruise control modes.

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 writing anything that moves, animates, counts down or simulates - per-frame vs fixed-step callbacks per engine, the accumulator loop with interpolation, delta-scaled smoothing, pause and time scale, and determinism.

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 working in a Unity project that uses Entities (DOTS) or when a task needs thousands of simulated objects - when ECS is worth it, components/bakers/ISystem with IJobEntity and Burst, entity command buffers for structural changes, native memory rules, and testing systems in a test World.

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 GLSL shader techniques for creating stunning visual effects — ray marching, SDF modeling, fluid simulation, particle systems, procedural generation, lighting, post-processing, and more.

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。

Prepare a meeting-ready alternative-investments brief for a client, household, IRA, trust, or named account before a review — pulls the alts book from iCapital or Addepar (positions, NAV vs. commitment, called and unfunded amounts, contributions/distributions/subscriptions/redemptions, lockups, and the platform's own IRR/TVPI/DPI/RVPI) and sits it beside the liquid book and IPS target from the connected portfolio system (Orion Connect, Addepar, or Envestnet), then flags cash needed for capital calls, alts % vs. target, concentration, unfunded concentration, and stale as-of data. Cites the source platform on every figure and keeps disclosures on the page. Does not recommend funds, forecast NAVs/IRRs, or simulate allocation changes. Triggers on "alts brief", "/alts-brief", "prep the [client]'s alts review", "what's going on with [client]'s PE/private investments", "how's [client] doing on their alts", or any request to review a household's alternative investment positions before a meeting.

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。

Proves iOS build/install/launch truth and triages hangs, crashes, jank, memory kills, and stale builds. Use when simulator, bundle, or runtime performance state is in doubt.

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。

Test interactive CLI/TUI applications using @microsoft/tui-test. Use when you need to test CLI tools with interactive prompts, TUI rendering, arrow key navigation, or any command that requires a TTY. Triggers include "test CLI", "test TUI", "run interactively", "automate terminal input", "simulate user input in terminal".

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。

Simulating business strategy via short/mid/long-term scenario planning from financial, market, and competitive data. Applies SWOT/PESTLE/Porter, KPI forecasting, roadmaps. Does not write code.

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Simulating users to evaluate existing flows and generate synthetic demand: cognitive walkthroughs, feature requests, unmet needs, JTBD, and opportunity trees. Not real-user research.

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。

シミュレーションと数理最適化（モンテカルロ / Monte Carlo / 離散事象シミュレーション / DES / 待ち行列 / リスク分析 / シナリオ分析、線形計画 / LP / 整数計画 / MIP / スケジューリング / 配送計画 / VRP / 割当 / メタヒューリスティクス）を 実行したら必ずセットで出す図と値のルーター。SeedSequence.spawn, scipy.stats の rvs, simpy, SALib, pulp, LpProblem, mip.Model, ortools, pywraplp, cp_model, pyomo, cvxpy, linprog, milp がコードに現れたとき、または ユーザーが「シミュレーションして」「モンテカルロで見積もって」「待ち時間をシミュレートして」「最適化して」 「最適な割当を求めて」「スケジュールを組んで」と言ったときに使う。収束・標準誤差・ソルバー status・制約違反・ 感度分析に言及がなくても適用する。SKILL.md のルーティング表で手法を特定し、対応する references/<手法>.md を 読んでから実行する。MCMC は statistical-inference-diagnostics、ML のハイパラ探索は predictive-modeling-diagnostics を使う。

Build and test iOS apps on simulator with XcodeBuildMCP.

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 .continue/checks locally against the current diff, simulating a PR review before pushing.

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。

Verify mini-program implementations and fixes with risk-calibrated evidence across static checks, unit tests, integration tests, state matrices, simulators, real devices, cloud environments, and release artifacts. Use when users ask to test, validate, accept, regression-check, quality-check, confirm readiness, or determine whether a mini-program feature is actually complete. Binds results to a source and build fingerprint, records commands and observable evidence, separates passed, failed, blocked, and not-run layers, prioritizes the next highest-information check, and never converts local success into device, cloud, release, or formal acceptance claims.

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。

Retirement projection with Monte Carlo simulation logic. Calculates required nest egg via 4% rule and 25x expenses, projected vs needed gap, contribution recommendations, asset allocation by age, Social Security claim optimization, healthcare cost projections, and withdrawal sequence planning. Use when the user says "/finance retirement", "am I on track for retirement", "how much do I need to retire", "Social Security timing", or any retirement question.

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。

Generic update orchestrator for an already-deployed warp route. Reads a Linear ticket describing any combination of warp-fee changes / ISM changes / hook changes / owner-address changes / additional-collateral-chain additions, edits the registry deploy.yaml accordingly, builds a per-chain strategy file from the artifact-context owner classifications, runs `hyperlane warp apply`, fork-simulates the resulting receipts via `/warp-route-check`, and hands off to `/warp-update-propose` to land the proposals. Chain extension (adding a brand-new chain with new on-chain contracts) is a separate skill — see `/warp-update-extend`.

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。

The post-change validation contract for warp routes — after any step that changes (or proposes) on-chain state, prove the route's config matches the target via hyperlane warp check. Run a live check when the change already executed (deployer-signed); run a fork-simulate-from-owner check via /warp-route-check when the change is pending multisig/file execution. Referenced by every deploy/update skill that mutates a route.

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。

Fetch and decode a pending Safe (multisig) governance batch from the Safe Transaction Service into a chain-tagged transactions JSON file that `/warp-route-check` (or any downstream consumer with the same input shape) ingests. Safe-API integration only — no forks, no replay, no 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。

Fork a warp route from the local HTTP registry for development and testing. Forks each chain with the right local node per protocol — anvil for EVM chains, surfpool (Solana mainnet fork) for Sealevel chains. Use when the goal is to simulate transactions on a set of local forks before submitting them on-chain.

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。

Cyber Risk Quantification engineer for CyberRadar. Implements FAIR (Factor Analysis of Information Risk) methodology, Monte Carlo simulation for loss distribution modeling, financial impact quantification in multi-currency (SAR, USD, EUR, GBP), loss exceedance curves, risk treatment ROI calculation, insurance premium optimization, and board-ready financial risk reporting. Transforms qualitative risk assessments into monetary terms for executive decision-making. Triggers on: CRQ, FAIR, Monte Carlo, financial impact, loss modeling, risk quantification, cyber insurance, risk treatment ROI, financial risk.

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Quantify cyber risk using FAIR methodology with Monte Carlo simulation, assess control effectiveness against NIST CSF/CIS/ISO 27001 frameworks, evaluate risk appetite alignment, and analyze cyber insurance coverage adequacy. Covers threat landscape mapping, asset valuation, loss event frequency and magnitude estimation, annualized loss expectancy calculation, control gap analysis with ROI ranking, residual risk assessment, and insurance policy gap modeling at 90th/95th/99th percentile loss scenarios. Use when building risk registers, preparing board-level risk dashboards, evaluating security investment priorities, assessing cyber insurance coverage, or auditing any organization's risk quantification maturity per NIST RMF, ISO 27005, or FAIR standards.

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。

Add a new single-objective optimization algorithm to the sci-comp library. Use when the user asks to implement a new optimizer (e.g. gradient descent, BFGS, simulated annealing, differential evolution).

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。