「constraints」の検索結果

Design or revise concise skills with clear triggers, proportional constraints, progressive disclosure, and realistic validation.

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Defines measurable architecture drivers and constraints before system design; edits architecture docs only.

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Implement UIKit navigation, Auto Layout, and Apple Human Interface Guidelines in iOS. Use when implementing UIKit navigation, Auto Layout constraints, or HIG compliance.

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Compose modern Flutter layouts and widgets idiomatically. Use for widget trees, layout constraints, mounted safety, and UI composition—not BLoC state management, routing, dependency injection, or tests.

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Standardize SRS and FRS specifications for technical behavior, interfaces, data contracts, quality constraints, and verification mapping. Use when writing SRS, functional specification, system behavior requirements, API/data contracts, or non-functional thresholds.

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Intent-driven spec precipitation — state a constraint in natural language (加一条规范：禁止用 any / 记录架构约束：服务间走 gRPC / 质量规则：覆盖率≥80%) and the workflow infers the category and records a <spec-entry>. Spec = 项目约束规则（编码规范、架构约束、质量标准）；可复用知识文档走 /maestro-knowhow capture。Triggers on "maestro-spec add", "记录规范", "添加约束", "添加规则", "加一条规范", "spec add". Terminology：spec = project constraints/rules (<spec-entry>). Reusable knowledge documents use /maestro-knowhow capture. Learning discoveries from /maestro-learn use <learning-entry> tags in learnings.md (separate from spec entries).

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Use when a task touches Ecto, even a how-to question: schemas, changesets, validations, queries, preloads, Multi, migrations, constraints, money fields. Load it before writing Ecto code. Skip for Ash.

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Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like "make me a GIF of X doing Y for Slack."

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Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. Use for model selection questions; use huggingface-local-models for GGUF setup and hf-cli for Hub operations.

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Build, deploy, debug, or maintain a Hugging Face Space using Gradio, Docker, or Static SDKs. Use for general Space hosting and configuration; use huggingface-zerogpu for ZeroGPU runtime constraints and lora-space-builder for LoRA demos.

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Implement or debug Hugging Face Spaces ZeroGPU code using `@spaces.GPU`, including isolation, state, duration, sizing, and CUDA-wheel constraints. Use for ZeroGPU-specific behavior; use huggingface-spaces for general hosting.

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Build a 2+ level taxonomy (`outline/taxonomy.yml`) from a core paper set and scope constraints, with short descriptions per node.

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Helps find, inspect, and compare (at a high level) .NET project templates. Resolves natural-language project descriptions to ranked template matches with pre-filled parameters. USE FOR: finding the right dotnet new template for a task, inspecting a template's parameters and constraints, understanding what a template produces before creating a project, resolving intent like "web API with auth" to concrete template + parameters. DO NOT USE FOR: actually creating projects (use template-instantiation), authoring custom templates (use template-authoring), producing a detailed side-by-side comparison (use template-comparison), choosing cross-parameter defaults during creation (use template-smart-defaults), MSBuild or build issues (use dotnet-msbuild plugin), NuGet package management unrelated to template packages.

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Comprehensive guide for developing in the Astro monorepo. Covers architecture, debugging, testing, and critical constraints. Use when working on features, fixes, tests, or understanding the codebase structure.

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Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating data pipeline outputs before production use, auditing a dataset against defined business rules, or producing a quality scorecard for a data asset.

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Resizes, scales, and adjusts video resolution and dimensions using GPU-accelerated NVENC encoding via Volcengine LAS. Video resizing, video scaling, video upscaling, and video downscaling — change video resolution, enlarge or shrink video dimensions, and adjust video size for different platforms and screens. Video compression by reducing resolution and video transcoding and re-encoding with specific dimension constraints. Supports flexible min/max width and height ranges with aspect ratio preservation strategies (increase, decrease, or disable), including landscape to portrait conversion. Async submit-poll workflow with batch support. Use this skill when the user wants to resize or scale video resolution (upscale/downscale), change video dimensions for different platforms, compress videos by reducing resolution, transcode/re-encode videos with GPU NVENC, adjust aspect ratio including landscape-to-portrait conversion, adapt videos for mobile/web/social media, or batch process multiple videos.

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Decide where to enforce structured LM output (constrain at decode time with Outlines vs validate-and-retry with Instructor vs grammar with Guidance) and which failure stance to take (Assert/hard-fail vs Suggest/soft-retry). Use when an LM's output is parsed or typed by downstream code and you must pick one enforcement library plus its failure handling, when malformed output is burning tokens on retries, or when choosing between decode-time vs validation-time constraints for local vs API models.

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Auto-loaded context for Portfolio Buddy 2 development. Use for ANY task involving: React 19 development, TypeScript, portfolio analysis features, metrics calculations, trading strategy comparison, or working with the Portfolio Buddy 2 codebase. Contains tech stack, known issues, and architectural 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。

Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like "make me a GIF of X doing Y for Slack."

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 a coherent, whole-paper mathematical modeling solution framework from a complete contest problem. Use after the user supplies a CUMCM or other modeling problem and asks for modeling ideas, an overall solution plan, question-by-question analysis, candidate-model comparison, model-selection reasons, innovations, validation, or cross-question linkage. For every question, explain the task and mathematical essence, compare multiple feasible models in tables, recommend a route with explicit reasons, and keep all questions connected through shared data, variables, parameters, constraints, and validation.

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Translate a complete mathematical modeling contest problem sentence by sentence into precise modeling language, preserve every substantive condition and definition, expose hidden constraints, draw a mandatory Mermaid cross-question flowchart, audit omissions, and deliver one easy-to-open Markdown report. For Huawei Cup (CPGMCM) graduate-contest problems, additionally prepend a plain-language layer that rewrites the background and questions as a primary-school word problem with a mandatory term-mapping table. Optionally render the finished report as one standalone HTML page on request. Use whenever a user supplies a CUMCM, CPGMCM, or other modeling problem and asks to interpret, translate, unpack, read closely, identify requirements, or avoid missing details before modeling.

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。

Shared commit message rules and technical constraints for skills that write commits. Not typically invoked directly.

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

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This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.

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。