「strain」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.

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。

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.

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。

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.

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 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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唯一性约束校验工具。检查指定字段的值是否在全表中唯一，标记重复出现的值所在行 （保留首次出现），支持空值跳过。当用户提到唯一性检查、重复值检测、主键约束等需求时使用此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。

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.

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, 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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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.

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。

Commit mesajlarina yapilandirilmis karar trailer'lari ekle. Constraint, Rejected, Confidence, Scope-risk, Not-tested trailer'lari ile karar baglamini koru.

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

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 and rewrite evaluation/numeric claims to ensure they carry minimal protocol context (task + metric + constraint) and avoid underspecified model naming.

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。

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.

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。

Write, run, or repair .NET tests that use TUnit. Use when a repo uses `TUnit`, `TUnit.Playwright`, `[Test]`, `[Arguments]`, `ClassDataSource`, `SharedType.PerTestSession`, or Microsoft.Testing.Platform-based execution. Preserve TUnit's parallel-by-default model and constrain only destructive shared-state collisions. DO NOT USE FOR: xUnit projects; MSTest projects.

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Convert .NET tests from NUnit 3/4 to MSTest v4 while preserving VSTest or MTP. Use for replacing NUnit/NUnit3TestAdapter packages, Test/TestCase/ TestCaseSource/Values attributes, constraint assertions, SetUp/TearDown, OneTimeSetUp, SetUpFixture, FixtureLifeCycle, TestContext, categories, retries, timeouts, and NUnit parallelization. Also use when a "convert NUnit to MSTest" request may already be migrated: inspect and report the no-op. Do not use for NUnit version upgrades, xUnit/TUnit conversion, MSTest upgrades, or runner-only VSTest-to-MTP migration.

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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.

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 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.

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。

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.

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 a framed problem needs root-cause investigation rather than a symptom-level fix, applying Five Whys, Fishbone (Ishikawa), Current Reality Tree, and constraint identification. This should trigger when an issue's Root Cause Analysis point of view needs evaluation, or when a maintainer directly asks to find the root cause of a problem before proposing a fix. Part of Plinth Toolkit

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Cross-engine decision rubric for self-hosting or recommending an LLM serving stack. Picks among vLLM, SGLang, TensorRT-LLM, TGI, llama.cpp, Ollama, and MLX as a function of (hardware × workload × constraint), not "which is fastest". Activates whenever a coder-agent must choose, defend, or migrate a serving runtime.

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Pick an LM output format per (task x consumer x model) rather than by reflex: different formats carry different cognitive load (e.g. code-in-JSON makes the same model write worse code than plain-text+diff, while asking for prose when you need a typed object fails the other way). Use when designing or debugging an LM's output schema, choosing between plain text / diff / JSON / tool-call / grammar-constrained output, or when a model's quality drops after wrapping its output in a structured format.

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。

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。