「icon」の検索結果

Make game-ready 2D characters, creatures, props, icons and FX as master stills, sprite sheets or animation clips. Plan size, motion and finish; generate the art (image generation is the default, through generate2dmedia route_media.py - the API when a key is configured, else the local Codex or Grok CLI); approve one master still per character; then key, slice, register, scale, palette, QA and export frames for a game engine (Aseprite JSON, Godot SpriteFrames or Sprite3D). HD finish by default, pixel finish on request. Character animation is one image-to-video clip per action from the master (video2dsprite); sheets are for FX, icons and props. Use for sprites, masters, sheets, frame-by-frame animation and packaging frames from any source. Not for maps, tiles or backgrounds (generate2dmap), video clips (video2dsprite), code-drawn art (codeart2d, only on request) or calling an image or video API by itself (generate2dmedia).

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 ready-to-paste AI image-gen prompts for 2D game art (character/sprite, icon/UI/item, background/tileset) that stay consistent with the project's art style across separate chat sessions in Claude/Gemini/ChatGPT chat UIs. Use when the user wants to render, generate, or create game art/sprites/icons/backgrounds with AI, or mentions an "art style bible" for a game project. Maintains a persistent `art-style-bible.md` so style doesn't drift between requests, and gives a post-generation checklist plus post-processing guidance for common technical misses (transparency, size, aspect ratio).

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 and build cohesive web pages with custom visual identity — landing pages and portfolios get rich anatomy + section archetypes; any other page type (blog, about, pricing, contact, coming-soon, dashboard, admin, e-commerce, custom) is supported through generic anatomy + skeleton. Use this skill whenever the user mentions: landing page, portfolio, personal site, hero section, marketing page, product page, sales page, splash page, blog page, about page, pricing page, contact page, coming-soon, waitlist, dashboard, admin panel, e-commerce store, redesign my site, design my portfolio, build a portfolio, work showcase, hire-me page, perfect-ui, perfect landing, or asks to create any web page surface. Two tiers: special (landing | portfolio — rich treatment) and generic (everything else — universal craft toolkit). Self-contained 8-phase pipeline: vibe discovery → visual direction (color/typography/mood) → custom icon set (NO emoji, NO icon library) → AI-generated visuals → optional shader effects layer → inline plan + implement on Next.js + Tailwind + shadcn. Always proposes effect layer as optional. No refusals; outputs distinctive sites, not AI slop.

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, audit, or improve an iOS App Store listing — app name, subtitle, keyword field, screenshots, app icon, preview video, creative assets, tags, ratings strategy, localization, and featuring nominations. Use when creating or reviewing App Store metadata, doing keyword research, planning a metadata update, or when the user mentions ASO, App Store Optimization, app store listing, keywords, subtitle, screenshot captions, preview video, app icon, Liquid Glass, creative assets, product page header, search results creative, Asset Library, featuring, ratings prompt, or Apple Ads.

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。

Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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。

Search and preview Irasutoya illustrations with the installed `irasutoya` command-line tool. Use this skill whenever the user wants to find, recommend, preview, randomly pull up, or get the page/image URL of an Irasutoya (いらすとや) image — from a topic, scene, Korean/Japanese/English phrase, visual description, emotion, character, object, or action — even if they don't say "Irasutoya" by name but clearly want a free, cute illustration for a slide, 발표자료, blog, doc, or chat. Also use it to optimize vague requests into fast Japanese keyword candidates and to return page/image URLs. Do NOT use it to generate brand-new AI art (e.g. DALL·E, Midjourney), draw charts or diagrams, design logos or icons, cut out/edit existing images, or recommend other stock-image sites — it only searches Irasutoya's existing catalog.

Expert-level condensed matter physics knowledge. Use when working with crystal structure, band theory, semiconductors, superconductivity, magnetism, phase transitions, Fermi liquids, topological materials, or strongly correlated systems. Also use when the user mentions 'band gap', 'Fermi energy', 'semiconductor', 'superconductor', 'phonon', 'crystal lattice', 'Brillouin zone', 'Bloch theorem', 'Hall effect', 'magnetism', 'phase transition', or 'topological insulator'.

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。

Formato GGUF e quantização llama.cpp para inferência eficiente em CPU/GPU. Use ao implantar modelos em hardware de consumidor, Apple Silicon, ou quando precisar de quantização flexível de 2-8 bits sem requisitos de GPU.

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。

Executa inferência de LLM em CPU, Apple Silicon e GPUs consumer sem hardware NVIDIA. Use para edge deployment, Macs M1/M2/M3, GPUs AMD/Intel ou quando CUDA não está disponível. Suporta quantização GGUF (1,5-8 bits) para redução de memória e aceleração de 4-10× vs PyTorch em CPU.

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 original, scalable logos and production-ready asset sets for a specific usage context. Use when Codex needs to create or refine a logo, symbol, wordmark, app icon, repository identity, CLI mark, website brand, social preview, or Codex skill/plugin icon; adapt concepts, formats, dimensions, variants, and delivery checks to the target platform instead of producing a context-free image.

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。

METAINFORMANT rules for directory src/metainformant/metagenomics/amplicon. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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 the user asks to "measure how our narrative is landing", "track echo rate against our canon lexicon", or "check how AI answer engines describe our brand"; produces a resonance report — echo rate (overlap of market language with the narrative-registry canon lexicon, method declared), AI-answer perception via tavily.py --answer (proxy-labeled), share-of-voice on a locked competitor panel (reusing share-of-voice-tracker), and resonance signals from bluesky.py / gdelt.py / pageviews.py — every number labeled Measured / proxy / User-provided, feeding the TALE E dimension and the upstream of the E1 evidence-integrity veto. Not for rebuilding share-of-voice machinery — use share-of-voice-tracker; not for own-site GA4/GSC analytics — use performance-monitor; not for scoring TALE profile result — use narrative-quality-auditor; not for adjudicating claims — use offer-claims-registry. 回声率/AI回答感知/份额之声/共鸣信号

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。

素材库 ↔ 创作词库 ↔ 画像：harvest 用去 AI 味筛子抽专属词， guide 出用词包，acquire 按画像缺口出搜索计划。只读，不 apply。 触发：抽创作词、用词包、金句抽词、写之前看词、素材库词库打通、画像补词。

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。

合并多份词表，自动改明显英文笔误和「词对、拼音错」，剔除 v2u / 拼音当词。 吃不准的进待确认，不写回输入法。

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。

This skill should be used when the user asks how to "use VibeTags", "add VibeTags annotations", "set up AI guardrails", "protect code from AI", "configure AI platforms", asks about @AILocked, @AIContext, @AIDraft, @AIAudit, @AIIgnore, @AIPrivacy, @AICore, @AIPerformance, @AIContract, @AITestDriven, @AIThreadSafe, @AIImmutable, @AIDeprecated, @AIObservability, @AIRegulation, @AIArchitecture, @AILegacyBridge, @AIStrictClasspath, @AIInternationalized, @AIPublicAPI, @AISchemaSafe, @AIStrictExceptions, @AIStrictTypes, @AIParallelTests, @AIIdempotent, @AIFeatureFlag, @AISecure, @AICallersOnly, @AISandboxOnly, @AIMemoryBudget, @AIPure, @AIDomainModel, @AIExtensible, @AIInputSanitized, @AISecureLogging, @AIExplain, @AIPrototype, @AISunset, @AITemporary annotations, or wants to control how AI tools interact with Java code.

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。

OCI（Oracle Cloud Infrastructure）のアーキテクチャ構成図を draw.io 形式（.drawio）で生成・編集する。AWS/Azure の構成図を OCI へ変換することもできる。Generate, edit, and convert OCI architecture diagrams in draw.io (.drawio) format using official Oracle icons.

Generates Nano Banana Pro prompts for profile icons and SNS images. Use when user mentions "プロフィール画像", "アイコン作成", "SNS用画像", or "ヘッダー画像".

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

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。

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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。

End-to-end 16S/ITS amplicon workflow from demultiplexed FASTQ to a consensus differential-abundance result, orchestrating cutadapt primer removal, per-run DADA2 ASV inference (learnErrors/mergeSequenceTables/removeBimeraDenovo), region-matched taxonomy assignment, a SEPP/Greengenes2 tree, alpha/beta diversity at a declared sampling depth (phyloseq/vegan, adonis2 paired with betadisper), compositional DA as a consensus of >=2 tools (ALDEx2/ANCOM-BC2) on unrarefied counts, and optional PICRUSt2 functional prediction gated on NSTI. Covers the stage-ordering decisions (primers before truncation, per-run error model, rarefy for diversity not DA, predicted potential not activity) and defers each per-step choice to the six microbiome skills. Use when staging an amplicon study end to end or chaining ASV inference, taxonomy, diversity, and differential abundance. For shotgun reads see workflows/metagenomics-pipeline.

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。

Rust 言語リファレンス。 別名: rustlang, rs (拡張子)、Rust-lang。 TRPL (The Rust Programming Language)、Rust by Example、Reference、 Cargo Book、rustc Book、rustdoc Book、Rustonomicon (Unsafe Rust)、 Edition Guide、Standard Library、Error Index。 所有権、ライフタイム、トレイト、async / await、エラーハンドリング、マクロ。

Apple グラフィックスフレームワークリファレンス。 Metal, Core Animation, Core Graphics, Core Image, SpriteKit, SceneKit。 MTLDevice, MTKView, MTLBuffer, MTLTexture, Metal 4 (MTL4CommandQueue, MTL4CommandBuffer, MTL4ArgumentTable), CALayer, CABasicAnimation, CADisplayLink, CAMetalDisplayLink, CAMetalDrawable, CGContext, CGPath, CGImage, CIFilter, CIContext, CIImage, CIRAWFilter, CIWarpKernel, CIBlendKernel, SKScene, SKSpriteNode, SKAction, SCNNode, SCNGeometry, SCNMaterial。

YomiTokuでPDF書籍をOCR処理しMarkdownに変換、目次解析・章分割・テーブル抽出まで 一貫実行するパイプラインスキル。4モード（通常/figure/デュアル/辞書）対応。 Apple Silicon MPS最適化。並列バッチ処理で大規模PDFも高速処理。 「書籍OCR」「PDF→Markdown」「章分割」「YomiToku」「本のデジタル化」 「OCRして」「この本をテキスト化」「PDFをMarkdownに」などのキーワードで使用。 PDFや書籍のOCR処理を依頼されたら、たとえユーザーが明示的にスキル名を言わなくても 積極的にこのスキルを使うこと。

Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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。