'math' 검색 결과

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when doing any Autodesk Maya task through Python or a bridge, headless or live: maya.cmds or OpenMaya 2.0 scripts, mayapy or maya.standalone batch jobs, a commandPort or MCP connection into a running Maya, "Maya 2027", validating or exporting scenes, or a maya.cmds script that fails after the 2025 to 2027 changes (PySide6, OpenPBR, renamed math nodes, removed flags). Also when a Maya model, rig, shot or render must be judged like a professional before it is called done, or a brief must be routed to the right Maya specialist.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

LuisaCompute API guide covering the DSL (kernels, callables, structs, buffers, atomics, control flow, dispatch), the runtime API (Context, Device, Stream, buffers, images, ray tracing, rasterization), AST usage markers and AST-to-XIR lowering, the core library (traits, types, logging, math, fiber, allocators, STL containers), and vstd containers (HashMap, queues, pools, variant, smart pointers, utilities).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Edit mathematically, empirically, or technically dense papers for readers outside the author’s specialty, including empirical legal scholarship, law-and-economics models, and legal-technology research. Clarifies terminology, antecedents, study design, and claim–statistic relationships while checking equations, numbers, quotations, and citations against the source. Use to clarify exposition or de-jargonize a technical draft; not to rewrite briefs or doctrinal arguments. Produces an edited draft and audit memo. Full integrity checks and original-format rendering require suitable extraction, code execution, and document tools; otherwise provides proposed edits with verification limits disclosed.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design a variation theory task using contrast, separation, and fusion to teach critical features of a concept. Use when students confuse similar concepts or fail to discern key distinctions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review academic papers for correctness, quality, and novelty using OpenJudge's multi-stage pipeline. Supports PDF files and LaTeX source packages (.tar.gz/.zip). Covers 10 disciplines: cs, medicine, physics, chemistry, biology, economics, psychology, environmental_science, mathematics, social_sciences. Use when the user asks to review, evaluate, critique, or assess a research paper, check references, or verify a BibTeX file.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.