「minimal」の検索結果

Modern web design trends, principles, and implementation patterns for 2024-2025. Use this skill when designing websites, creating interactive experiences, implementing design systems, ensuring accessibility, or building performance-first interfaces. Triggers on tasks involving modern design trends, micro-interactions, scrollytelling, bold minimalism, cursor UX, glassmorphism, accessibility compliance, performance optimization, or design system architecture. References animation skills (GSAP, Framer Motion, React Spring), 3D skills (Three.js, R3F, Babylon.js), and component libraries for implementation guidance.

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Design, enforce, audit, and validate Power BI report themes through the pbir CLI. Invoke when a report uses a default or minimal theme, visuals have inconsistent formatting or many local overrides, or the user asks to create, copy, apply, standardize, rebrand, audit, or validate a theme; change theme colors or typography; define style presets; promote visual formatting into the theme; clear overrides; or improve theme compliance.

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Production-ready scripts for Android app testing, building, and automation. Provides semantic UI navigation, build automation, log monitoring, and emulator lifecycle management. Optimized for AI agents with minimal token output.

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A minimal test skill that greets the user and demonstrates the ASM publish workflow.

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Help the user turn their Retentioneering ideas, friction reports, bug findings, or feature needs into high-quality upstream contributions: from capturing and validating the idea, through minimal reproductions and issue drafts, to preparing, testing, and submitting a pull request that follows this repository's conventions. Use when the user says they found a bug, wants a feature, wrote a workaround worth upstreaming, or asks how to contribute, open an issue, or make a PR to retentioneering-tools.

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Builds .NET 8 applications with minimal APIs, clean architecture, cloud-native microservices, Entity Framework Core, CQRS with MediatR, and AOT compilation.

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Determines whether a technology qualifies as an AI system under Art. 3(1) of the EU AI Act and classifies its risk tier (prohibited, high-risk, GPAI with systemic risk, limited risk, minimal risk). This skill should be used when the user asks to "classify an AI system under the AI Act", "determine the AI Act risk tier", "check if something is an AI system", "assess prohibited practices", "check high-risk classification", "determine Art. 6 exception applicability", or mentions "KI-Verordnung", "Risikoklassifizierung", Art. 5, Annex III, or GPAI systemic risk.

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Classify AI systems under the EU AI Act (Regulation (EU) 2024/1689) and determine compliance obligations. Use when asked to assess AI risk level, check if an AI system is prohibited, determine high-risk status, identify GPAI model obligations, map provider/deployer responsibilities, or build an AI Act compliance roadmap. Covers the full classification decision tree (Prohibited → High-Risk → Limited → Minimal → GPAI), obligations by role (provider, deployer, importer, distributor), compliance timelines, and DACH-specific considerations (German works council, BaFin, BSI, BNetzA). Triggers on phrases like "classify this AI system", "is this high-risk under the AI Act", "AI Act risk assessment", "EU AI Act compliance", "prohibited AI practice", "GPAI obligations", "AI Act timeline".

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Build a risk-matched whitebox or the approved production game for its target runtime. Turn GAME_DESIGN, and ART_DIRECTION when production begins, into a minimal BUILD_BRIEF and a runnable candidate that can be iterated with replayable evidence. Use for prototype the riskiest design question, implement the approved game design, or turn this design into a running game. 游戏构建执行。先按最大风险做白盒，或在正式生产时把批准后的 GAME_DESIGN 与 ART_DIRECTION 压缩成最小 BUILD_BRIEF，在目标运行环境中实现可运行候选并用可回放证据迭代。用于验证高风险设计问题、构建游戏原型或实现批准方案。

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An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss.

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Draft, audit, or minimally revise English- or Chinese-language academic prose to reduce formulaic, vacuous, mechanically repetitive, or process-leaking language while preserving claims, evidence strength, logical relations, manuscript-wide terminology identity, document-level pattern variation, and scholarly register. Use for papers, abstracts, grants, cover letters, and reviewer responses when the user asks to de-AI, humanize, audit AI-like phrasing, or rewrite text without changing meaning. English is primary; Chinese is supported. Not for detector evasion, policy circumvention, pure translation, non-academic copy, or adding facts, citations, examples, or author experiences that the source does not contain.

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Write the test first, watch it fail, write minimal code to pass

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Turn a feature request into a minimal, file-level implementation plan before any code.

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Transform ordinary snapshots—including daily moments, portraits, objects, pets, food, interiors, streets, architecture, landscapes, travel, and humanistic scenes—into the restrained Shijing Paper Zine visual style. Use when the user wants any real photo turned into a spacious paper-editorial composition with an irregular photographic fragment, simplified source-derived drawing, one structural print color, tactile torn-paper texture, minimal micro-copy, a consistent multi-image series, or a source-to-poster comparison.

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Maximum-efficiency dev mode: terse zero-fluff prose + YAGNI/ladder-first code. Use when user says "chisle mode", "activate chisle", "chislify", "be efficient", "be minimal", "no fluff", "yagni", or invokes /chisle. Deactivate: "stop chisle" / "normal mode".

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Minimal MDA source demonstrating relationship-graph footnotes and metadata.mda.* MDA-extended fields. Use as a reference fixture when learning the MDA source format.

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Phase 2 of the prd-taskmaster pipeline: spec generation and task parsing. Loads a template (comprehensive|minimal), fills it with DISCOVER-phase constraints and answers, validates the spec (placeholders_found, grade thresholds), parses the PRD into tasks via task-master, runs TaskMaster's native complexity analysis, and expands every task into verifiable subtasks. Autonomous-safe. Declares GENERATE complete so HANDOFF can follow.

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Transform a user-uploaded photograph into one horizontal collectible memory card with a large quiet editorial illustration, six integrated journaling-sticker motifs, three small English keywords beneath the left illustration, tactile paper grain, and deliberately clumsy flat color fields. Use when Codex needs to P图, edit, redraw, stylize, or turn a travel, street, landscape, lifestyle, portrait, or pet photo into a minimalist postcard-like card or sticker-card in this visual language. Preserve recognition through one same-medium identification anchor and optionally one exact place-defining landmark text shown once; never retain an unintended photorealistic patch, signature, or watermark.

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Quick execution for small tasks — minimal run lifecycle (start + done) with evidence recording. Full LLM capability, scoped to mechanically clear tasks.

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Property-based testing (PBT) patterns with fast-check (JS/TS), Hypothesis (Python), and gopter (Go). Generate random inputs, define invariants, shrink failures to minimal cases. Adapted from Trail of Bits. Use when testing pure functions, parsers, serializers, state machines, or any code where example-based tests miss edge cases.

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Surgical code fixer for Bug Hunter. Implements minimal, precise fixes for verified bugs. Uses doc-lookup (Context Hub + Context7) to verify correct API usage in patches. Respects fix strategy classifications (safe-autofix vs manual-review vs larger-refactor).

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

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Internal template for creating or refactoring a skill into the repository's reference-first shape. **Trigger**: reference-first template, blueprint skill, create a reusable skill, refactor a script-heavy skill. **Use when**: you need a lean `SKILL.md`, explicit `references/`, machine-readable `assets/`, and a minimal deterministic `run.py`.

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Elite frontend image-direction skill for generating premium, conversion-aware website design references. CRITICAL OUTPUT RULE — generate ONE separate horizontal image FOR EVERY section. A landing page with 8 sections produces 8 images. Never compress multiple sections into one image. Enforces composition variety (not always left-text / right-image), background-image freedom, varied CTAs, varied hero scales (giant / mid / mini minimalist), narrative concept spine, second-read moments, and a single consistent palette across all images. Optimized for landing pages, marketing sites, and product comps that developers or coding models can accurately recreate.

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。