2,033 件 ・ 関連度順
概要と使いどころ
When the user wants to plan or optimize paid user acquisition campaigns. Also use when the user mentions "Apple Search Ads", "user acquisition", "paid ads", "UA", "ad campaign", "install campaign", "Facebook ads for apps", "TikTok ads", or "cost per install". For organic growth, see aso-audit. For launch-specific UA, see app-launch.
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 implement, optimize, or use App Clips for app discovery and conversion. Use when the user mentions "App Clip", "app clip code", "mini app", "instant app", "App Clip card", "App Clip link", "no download required", "instant experience", or wants to understand how App Clips appear in App Store search. For general App Store discoverability, see aso-audit. For marketing campaigns, see ua-campaign.
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 design, optimize, or evaluate App Store screenshots and preview videos. Also use when the user mentions "screenshots", "app preview", "product page design", "screenshot design", "creative assets", or "what should my screenshots show". For A/B testing screenshots, see ab-test-store-listing. For full ASO audit, see aso-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。
Score a LinkedIn profile out of 100 against a 12-part rubric and rewrite the parts that lose points - headline, about, experience, featured, banner. Use when the user says "optimize my profile", "score my LinkedIn", "rewrite my headline", "fix my about section", or pastes their profile and asks how it reads.
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。
Turns rough intent or a weak prompt into a reliable one — diagnosing why output is inconsistent, restructuring the instruction, and adapting it across models. Use this when a prompt is not producing what was wanted, when output varies run to run, when writing a prompt for a repeated task, when moving a prompt between models, or when someone describes what they want an AI to do and needs it written properly.
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 for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.
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。
Plans, runs, and optimizes paid acquisition across search, social, and display — account structure, targeting, creative, bidding, budget, and the analysis that says whether to scale or stop. Use this to set up or restructure campaigns, write and iterate ad creative, diagnose rising costs or falling performance, decide budget allocation, or judge whether a channel is working.
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。
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
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。
Baidu Intelligent Cloud marketing skill for generating marketing notes, videos, and optimized copy. Supports immediate and delayed call triggers. Use when user needs marketing content generation or campaign 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。
Baidu FaMou algorithm skills for efficient algorithm self-evolution. Provides experiment management and visualization capabilities to help optimize complex algorithms. Use when user needs algorithm optimization or experiment management.
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。
Professional beautification tool for gene expression heatmaps, automatically adds clustering trees, color annotation tracks, and intelligently optimizes label layout.
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 optimizing LinkedIn profiles for doctors, physicians, nurses, healthcare professionals, or medical researchers. Crafts compelling headlines, writes professional summaries, integrates healthcare keywords, and builds personal branding for medical careers.
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。
Recommend suitable prompts from 10,000+ Nano Banana Pro image generation prompts based on user needs. Optimized for Nano Banana Pro (Gemini), but prompts also work with Nano Banana 2, Seedream 5.0, GPT Image 1.5, Midjourney, DALL-E, Flux, Stable Diffusion, and any text-to-image AI model. Use this skill when users want to: - Generate images with AI (any model — Nano Banana Pro, Gemini, GPT Image, Seedream, etc.) - Find proven AI image generation prompts and prompt templates - Get prompt recommendations for specific use cases (portraits, products, social media, posters, etc.) - Create illustrations for articles, videos, podcasts, or marketing content - Browse a curated prompt library with sample images - Translate and understand prompt techniques Also available: "ai-image-prompts" skill — a model-agnostic version of this library for universal image generation.
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 UI performance against Core Web Vitals — LCP, INP, CLS — with loading/code-split strategy, layout-shift prevention, and animation performance rules. Use when the user wants to improve speed, fix jank or layout shift, hit Web Vitals budgets, or make a UI feel fast on low-end devices.
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 this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.
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 this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
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。
SEO & GEO (Generative Engine Optimization) for websites. Analyze keywords, generate schema markup, optimize for AI search engines (ChatGPT, Perplexity, Gemini, Copilot, Claude) and traditional search (Google, Bing). Use when user wants to improve search visibility.
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。
SEO & GEO (Generative Engine Optimization) for websites. Analyze keywords, generate schema markup, optimize for AI search engines (ChatGPT, Perplexity, Gemini, Copilot, Claude) and traditional search (Google, Bing). Use when user wants to improve search visibility, search optimization, search ranking, AI visibility, ChatGPT ranking, Google AI Overview, indexing, JSON-LD, meta tags, or keyword research.
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 scipy.optimize.curve_fit for nonlinear least squares parameter estimation from experimental data.
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。
Train GPT-2 scale models (~124M parameters) efficiently on a single GPU. Covers GPT-124M architecture, tokenized dataset loading (e.g., HuggingFace Hub shards), modern optimizers (Muon, AdamW), mixed precision training, and training loop implementation.
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。
Transcribe audio segments to text using Whisper models. Use larger models (small, base, medium, large-v3) for better accuracy, or faster-whisper for optimized performance. Always align transcription timestamps with diarization segments for accurate speaker-labeled subtitles.
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 optimized prime sieves and factorization routines
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。
Apply language-specific micro-optimizations
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。