「math」の検索結果

Trigger when: (1) User wants to create an educational/explainer video, (2) User has a vague concept they want visualized, (3) User mentions "3b1b style" or "explain like 3Blue1Brown", (4) User wants to plan a Manim video or animation sequence, (5) User asks to "compose" or "plan" a math/science visualization. Transforms vague video ideas into detailed scene-by-scene plans (scenes.md). Conducts research, asks clarifying questions about audience/scope/focus, and outputs comprehensive scene specifications ready for implementation with ManimCE or ManimGL. Use this BEFORE writing any Manim code. This skill plans the video; use manimce-best-practices or manimgl-best-practices for implementation.

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。

Manage MATLAB packages with the in-product MATLAB Package Manager (mpm) on MATLAB R2026b or later: install, uninstall, update, search, or inspect packages; declare package dependencies in a matlab.toml project; install a shareable .mltbx package; or manage repositories. Covers dependency-resolution errors and anything involving mpminstall / mpmuninstall / mpmupdate / mpmlist / mpmsearch / mpmAddRepository / matlab.mpm.*. MATLAB *packages* are distinct from MathWorks products, toolboxes, and support packages: a bare "what packages are installed?" means MPM packages (mpmlist / matlab.mpm.Package) — this skill — unless the user explicitly says products, toolboxes, or support packages. NOT for installing MATLAB, Simulink, or toolboxes from a shell via the standalone mpm executable (the OS-level product installer), nor for listing installed products / toolboxes (ver, installedAddons) — use matlab-install-products for those.

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 setting up vehicle network communication in MATLAB using Vehicle Network Toolbox. Covers CAN/CAN FD (fully implemented), with architecture for J1939, XCP, and future protocols. Handles hardware discovery, channel creation, bus configuration, message exchange, signal encoding/decoding, and analysis across all supported vendors. (Vector, Kvaser, PEAK-System, NI, SocketCAN, MathWorks Virtual).

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。

Generate or improve MATLAB help text (documentation comments) for a function, class, or script file following MathWorks standards (H1 line, syntax paragraphs, See Also, 75-char lines). Read BEFORE writing MATLAB help — default patterns (Inputs:/Outputs: lists, block comments, uppercase See Also) produce non-conforming output. Use when writing, rewriting, fixing, or reviewing MATLAB help comments or function documentation.

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。

Deterministic workflow to download MATLAB Package Manager (mpm) and install MathWorks products from the OS command line with consistent, repeatable behavior. Use when installing MATLAB, Simulink, toolboxes, or support packages via command line, or setting up scripted installations for CI/CD, containers, or fleet provisioning.

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。

Guides the agent to reference official MathWorks Documentation and Help. Determine correct function syntax and workflows from user guides when deeper context is needed. Minimize iterations and repetitive trial and error. Use this skill to: Identify correct syntax and configuration details. Retrieve relevant, version-specific (or release-specific) information from official documentation. Consult user guides when conceptual or workflow context is needed. Apply best practices.

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 DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.

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。

Review MATLAB code for quality, performance, maintainability, and adherence to MathWorks coding standards. Uses check_matlab_code and matlab_coding_guidelines. Use when reviewing code, checking style, finding code smells, assessing quality, or preparing code for handoff or publication.

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。

Reviews MATLAB fixed-point (fi) code for performance, code generation efficiency, and correctness. Identifies antipatterns and suggests idiomatic improvements. Use when reviewing fi, fimath, numerictype, or quantizenumeric code.

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Evaluate 4 execution strategies (sequential, parallel-subagents, workflow, agent-team) for a phase or fan-out task. Outputs 7-signal score table, agent count math, cost guards, and strategy recommendation.

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 an evidence-based competitive pricing strategy for ecommerce products. Use when a seller asks how to position a price against competitors, set regular and promotional prices, protect contribution margin, design bundles or price tiers, respond to competitor moves, or coordinate prices across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not use for automated repricing implementation or claims of a mathematically proven optimal price without sufficient data.

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。

Generate and run minimal reproducible Python modeling code for the human-approved main method and usable baseline, saving compact experiment artifacts and a canonical run summary.

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。

Generate and run minimal reproducible MATLAB or Beita Tianyuan compatible code for the human-approved main method and usable baseline, with compact experiment artifacts and a canonical run summary.

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。

Summarize modeling experiment evidence, compare the approved main method with a usable baseline, surface fallback triggers, and produce a decision-point or final report without creating routine per-round prose.

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。

Review, run, debug, and verify approved MATLAB or Beita Tianyuan modeling code against its plan, data contract, method decision, compatibility constraints, and experiment outputs, saving one compact JSON review.

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。

Collect and analyze relevant papers, reports, and reference methods to inform method selection without fabricating references or copying models blindly.

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。

Review, run, debug, and verify approved Python modeling code against its code plan, data contract, method decision, risk conditions, and experiment outputs, saving one compact JSON review.

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。

Map contest attachments to subquestions, audit and clean raw data, and emit one reusable data profile with quality, coverage, imbalance, concentration, and method-readiness evidence for downstream risk screening.

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 the authoritative final method explanation for a submission-ready subquestion from the method card, human decision ledger, code plan, final results, and robustness evidence.

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。

Audit whether the semantic evidence required by the active lean or submission profile exists and is current, without requiring one verbose artifact per skill or an arbitrary number of pass bullets.

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。

Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review 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。

Faithfully append a human modeler's choice and rationale to one canonical per-subquestion JSONL decision ledger. Use after a choice card is answered or when migrating legacy decision artifacts; never originate or improve the decision.

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。

Translate a human-approved main method and usable baseline into a minimal language-neutral implementation and experiment contract. Use after G2.5 and data readiness, before Python or MATLAB code generation.

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。

Assemble a submission-ready writer package from final method, result, robustness, figure, and human-decision artifacts, then freeze approved numerical claims with provenance.

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。