2,022 件 ・ 関連度順
概要と使いどころ
UI/UX design intelligence. 67 styles, 96 palettes, 57 font pairings, 25 charts, 13 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, check UI/UX 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。
noteプラットフォームに最適化された記事を生成・最適化。タイトル最適化、モバイル対応、ハッシュタグ生成。「note用に最適化」「note記事作成」などで使用。
原稿からXのアルゴリズムに最適化されたポスト内容を生成。「X投稿を最適化」「ツイート作成」などで使用。
3D print models generated with Meshy AI. Handles printability analysis, slicer integration, multi-color printing guidance, and print-optimized download workflows. Use when the user mentions 3D printing, slicing, Bambu, OrcaSlicer, Prusa, Cura, Creality Print, or wants to print a figurine, miniature, or physical 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。
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。
Database optimization, query tuning, indexing strategies, migration management, and performance monitoring for PostgreSQL with Drizzle ORM. Use when optimizing slow queries, designing schemas, creating migrations, or setting up database monitoring.
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 Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.
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。
Django performance code review. Use when asked to "review Django performance", "find N+1 queries", "optimize Django", "check queryset performance", "database performance", "Django ORM issues", or audit Django code for performance problems.
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 optimize application performance, including bundle size, rendering, database queries, and Core Web Vitals.
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。
Publication-quality physics plots — vector fields, streamlines, contour maps, 3D surfaces, phase space, spectrograms, and animations. Optimized for journal submission with LaTeX labels, proper colormaps, and multi-panel layouts.
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。
High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
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。
Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.
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。
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
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。
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
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 complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
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。
Orchestrate the full ToolUniverse self-improvement cycle: discover APIs, create tools, test with researcher personas, fix issues, optimize skills, and push via git. References and dispatches to all other devtu skills. Use when asked to: run the self-improvement loop, do a debug/test round, expand tool coverage, improve tool quality, or evolve ToolUniverse.
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。
Benchmark stellar-build skills to measure quality and the lift from optimization. Use when the user says "bench my skills", "bench-skills", "benchmark skills", "did optimization help", "measure my skills", "stellar-loop bench", or wants to quantify whether /optimize-skills actually improved things. Held-out eval prompts scored by an LLM judge, reproducible with fixed seeds. Fully local.
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。
Explain and control the stellar-build learning loop — the system that improves your skills from how you use them. Use when the user says "learning loop", "how does the learning loop work", "what is the learning loop", "self-improving skills", "stellar-loop", "show my skill usage", "is tracing on", or wants an overview / status of the local trace-capture + optimize + bench cycle.
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。
When the user wants to create, optimize, or analyze a referral program, affiliate program, or word-of-mouth strategy. Also use when the user mentions 'referral,' 'affiliate,' 'ambassador,' 'word of mouth,' 'viral loop,' 'refer a friend,' or 'partner program.' This skill covers program design, incentive structure, and growth optimization.
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。
When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," or "in-app pricing." Distinct from public pricing pages (see page-cro) — this skill focuses on in-product upgrade moments where the user has already experienced value.
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。
When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," or "new user experience." For signup/registration optimization, see signup-flow-cro. For ongoing email sequences, see email-sequence.
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。
When the user wants to add, fix, or optimize schema markup and structured data on their site. Also use when the user mentions "schema markup," "structured data," "JSON-LD," "rich snippets," "schema.org," "FAQ schema," "product schema," "review schema," or "breadcrumb schema." For broader SEO issues, see seo-audit.
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。
Optimize accounts receivable processes. TRIGGERS - Use when user needs help with accounts-receivable-optimization related tasks.
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。