「train」の検索結果

Recover missing spreadsheet values from row and column totals, percentage shares, year-over-year changes, CAGR relationships, and cross-sheet constraints.

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 OR-Tools RoutingModel vehicle-routing models with transit matrices, dimensions, capacity constraints, time windows, optional visits, invalid-arc handling, search parameters, and route extraction.

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。

Data conditioning techniques for gravitational wave detector data. Use when preprocessing raw detector strain data before matched filtering, including high-pass filtering, resampling, removing filter wraparound artifacts, and estimating power spectral density (PSD). Works with PyCBC TimeSeries 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。

SCIP optimization with PySCIPOpt. Use when facing an optimization problem with an objective, hard constraints, soft penalties, integer decisions, routing, assignment, scheduling, allocation, packing, capacity, inventory, or service-level rules. Prefer modeling and solving the problem with PySCIPOpt when it is available.

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。

Nonlinear optimization with CasADi and IPOPT solver. Use when building and solving NLP problems: defining symbolic variables, adding nonlinear constraints, setting solver options, handling multiple initializations, and extracting solutions. Covers power systems optimization patterns including per-unit scaling and complex number formulations.

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 logistics and operations rules into optimization variables and constraints. Use when an operations problem describes vehicles, routes, depots, pickups, dropoffs, inventory, capacity, assignments, time windows, service targets, penalties, resource limits, or other business rules that need to become an optimization 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。

Use for formulating, solving, debugging, and validating mixed-integer linear optimization models with open-source solvers, including variable indexing, sparse constraints, linearized costs, solver limits, MIP gaps, incumbent extraction, numerical tolerances, and deterministic output reporting.

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 checking simplified ADA-derived plan-view bathroom accessibility constraints such as turning space, door clear width, toilet centerline, grab bars, and lavatory knee/toe clearance.

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 Python code on cloud GPUs using Modal serverless platform. Use when you need A100/T4/A10G GPU access for training ML models. Covers Modal app setup, GPU selection, data downloading inside functions, and result handling.

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 producing a minimally changed repaired 2D layout in JSON from extracted architectural geometry and rule-based clearance constraints.

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 parse LeetCode problems with metadata, constraints, examples, hints, difficulty ratings, and related problems. Integrates with LeetCode API for comprehensive problem data retrieval.

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 argument validation logic with type coercion, constraints, custom validators, and helpful error messages for CLI applications.

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 comprehensive test cases including edge cases, stress tests, and counter-examples for algorithm correctness verification. Supports random generation, constraint-based generation, and brute force oracle comparison.

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。

Select optimal graph algorithm based on problem constraints

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。

spaCy NER model training and entity extraction for conversational AI

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。

Rasa NLU pipeline configuration and training for intent and entity extraction

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。

Establish project governing principles including dev guidelines, code quality standards, testing policies, UX requirements, performance benchmarks, and security constraints.

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 technical architecture, select technology stack, and define implementation strategy from specifications and constitution constraints.

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。

Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)

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。

Toolkit for creating animated GIFs optimized for Slack, with validators for size constraints and composable animation primitives. This skill applies when users request animated GIFs or emoji animations for Slack from descriptions like "make me a GIF for Slack of X doing Y".

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 ONLY when the user asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a deterministic domain-correctness gate that catches code which runs but is scientifically wrong (unit/dimension mismatch, Euclidean distance on lat/lon without a CRS, 0-based/1-based coordinate and strand errors, impossible SMILES valence, uncorrected multiple comparisons, averaging a categorical code, unconstrained kinetic-parameter fits, kLa computed from raw DO instead of the driving force, arithmetic mean of raw CFU counts, ANOVA/Tukey with no assumption check, a curvature DOE design fit with a first-order model, an ML model fit across process scales with no correction). Surfaces structured findings; never claims the code is correct.

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。

Understand the components, mechanics, and constraints of context in agent systems. Use when writing, editing, or optimizing commands, skills, or sub-agents prompts.

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 a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features

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 comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.

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。