データ分析、機械学習、LLMアプリ、プロンプト設計（8,922 件）
概要と使いどころ
Leverages Ahrefs REST API v3 to identify content gap opportunities through competing domain analysis. Pulls referring domains data, anchor text distributions, and DR metrics to prioritize content creation for link-worthy topics.
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。
Query NFT and token data, trade NFTs on Seaport, swap ERC20 tokens via DEX aggregator, configure wallet signing providers, and build/register/gate AI agent tools on Base. Covers the full OpenSea developer surface across CLI, MCP server, shell scripts, and SDK. Pick the right sub-skill using the routing table below, then read that sub-skill's SKILL.md for operational detail.
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。
Swap ERC20 tokens across supported chains via OpenSea's cross-chain DEX aggregator. Get quotes with optimal routing, check token balances, and execute swaps. For NFT trading use opensea-marketplace, for querying token data use opensea-api.
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。
日本の行政オープンデータにアクセスする。不動産取引価格（国交省）、官公需・入札情報、e-Stat政府統計の3つのデータソースを検索・取得できる。「不動産価格を調べて」「入札情報を検索」「政府統計を取得」「官公需で○○を探して」「地価を調べて」「統計データ」などで使用。MCP server (mcp.n-3.ai) 経由でリアルタイムにデータ取得する。
Generate A-share end-of-day market review reports with Pandadata data, covering trade-date checks, index performance and valuation, market breadth, limit-up/down sentiment, industries/concepts, 龙虎榜, block trades, margin financing, northbound holdings, risk notes, and optional scheduled after-close automation. Use when the user asks for 今日复盘, 收盘总结, 每日市场报告, A股复盘, 龙虎榜复盘, 北向资金动向, or to set up an automated daily market review.
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 names a coding, Claude Code, research, documentation, or technology-selection task; asks which prompt to use; says to use the right prompt; or when an agent enters this repository and needs to orient without reading every file.
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 names a coding or Claude Code task ("add OAuth", "set up hooks", "review this PR", "pick a model"), asks which prompt to use, says to "use the right prompt", or when you enter this repo and need to orient without reading every file.
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。
Silently restructures the user's natural-language prompt into the format the model CURRENTLY running this skill handles best, then answers. On activation it identifies which model family is executing it (Claude, GPT, Gemini, Llama, DeepSeek, Mistral, Qwen, Grok, Perplexity, Kimi, GLM, Command, Nova, or Phi) and loads that one model's official strategy — so the optimization always matches the model that actually runs it. Activate with /prompt-refine. Use when users want better answers without learning prompt engineering.
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。
Automatically optimize AI agent credit usage by routing tasks to the most cost-efficient execution path. Use when you want to reduce AI API costs by 30-75% without quality loss, classify task complexity before execution, route simple tasks to free or low-cost models, split complex tasks into optimized sub-tasks, or detect vague prompts before wasting credits.
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。
Strategic product leadership guidance for SaaS and technology companies. Covers product strategy, roadmap planning, product discovery, user research, growth product management, platform strategy, product analytics, and product launches. Use when defining product vision, prioritizing features, conducting discovery, analyzing metrics, or launching products. Use for "product strategy", "roadmap planning", "feature prioritization", "product discovery", "product metrics", "PRD writing".
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。
Expert product marketing guidance for SaaS growth - trial acquisition, user activation to first value, and freemium-to-paid conversion. Use when writing landing pages, onboarding flows, email sequences, pricing pages, upgrade prompts, or any growth/conversion copy.
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 images and video from text prompts via the mediagen CLI, across Gemini, OpenAI and Kie AI. Use when asked to create, generate, render, or edit an image or a video — a logo, an illustration, a mockup, a photo, a banner, a clip — or to mark existing media as AI-generated. Also covers choosing a provider or model for a generation task.
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。
The Mileva Method (CRISP) — Phase 3: Investigate. Process mapping for existing or greenfield processes, user journey mapping, UX discovery, project goals, system architecture (integration map + data flow). Use after outcome alignment. Triggers on "investigate", "phase 3", "map the process", "process mapping", "user journey", "as-is process", or after Phase R exit checklist is complete.
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。
当用户明确要求"优化 prompt"、"改进提示词"、"润色指令"、"将简陋 prompt 转换为最佳实践版本"，或要求"把 prompt 改写成伪代码"、"翻译成程序结构/可编程自然语言"时使用。支持两种输出模式：standard（基于 OpenAI/Anthropic 官方最佳实践做增强优化）与 prompt_program（严格等价翻译为 Prompt Program 方言）。
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。
根据用户描述生成高质量绘图 prompt，并按通用、roadmap、schematic 模式通过 BenszAPI 直接完成 gpt-image-2.5-flare、gpt-image-2.5-sunburst、gpt-image-2 或 Nano Banana/Gemini 出图、编辑和多轮迭代；这是自包含的图片生成工作流，选中后不得调用或依赖 imagegen，除非用户明确要求同时使用 imagegen。
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。
调用 Yosys 0.35 对 RTL 进行逻辑综合并技术映射到 ASAP7 标准单元，产出门级网表 + QoR 报告。支持并行综合（基于空闲CPU数量）。Workflow: (1)生成综合脚本;(2)并行执行综合;(3)LLM检查结果并迭代优化。触发场景：(1) bba-guru-synthesis 已完成 SDC 与 CDC 检查、需要生成 netlist；(2) PD 反馈 area/path 超标需要重综合；(3) 显式 /bb-invoke-yosys。
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 says 'migrate elementor to bricks', 'convert elementor to bricks', or 'move my site from elementor to bricks'. Reads the Elementor JSON widget data, maps each widget to its Bricks equivalent, and creates draft duplicates with clean Bricks JSON.
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 says 'export my site', 'backup my pages before editing', 'download my content locally', or 'migrate content to my other site'. Exports pages, posts, and custom posts to portable local packages with builder data, media, and markdown, then imports elsewhere with ID remapping.
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 says 'migrate visual composer to gutenberg', 'convert vc to blocks', or 'move visual composer pages to the block editor'. Parses the Visual Composer data, maps elements to core blocks, and creates draft duplicates for review.
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 says 'migrate brizy to gutenberg', 'convert brizy to blocks', or 'replace brizy with native wordpress'. Parses the Brizy JSON data, maps elements to core blocks, and creates draft duplicates for review.
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 scraping JavaScript-heavy pages or SPAs, crawling multiple URLs concurrently, extracting structured data with reusable CSS/JSON schemas, or building automated web data pipelines. Wraps the Crawl4AI library (`crwl` CLI and Python SDK) with schema-generation patterns for LLM-free extraction. Triggers on crawl4ai, crwl, scrape JS-heavy site, scrape SPA, headless browser scrape, schema-based extraction, batch crawl, sitemap crawl, web data pipeline. SKIP when a static HTML page can be read with `defuddle` / `fetch-web` — those are faster cold-start and don't need a browser.
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。
Read your Threads (Meta) audience and niche from real data. Scan a niche query or hashtag for the top posts right now with likes, replies, reposts, and quotes, and pull a profile (yours or a competitor) for follower count, bio, verified status, and recent posts with engagement. Powered by Apify, no login. Triggers on "analyze Threads", "what is working on Threads", "top posts for", "competitor Threads profile", "niche scan", "follower count". Not for writing posts (use threads-post-writer).
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Scores and strengthens startup pitch decks (pre-seed through Series A) against 35 investor-grade criteria grounded in Sequoia, DocSend, YC, a16z, and Carta data. Use when the user has shared a pitch deck (PDF, PPTX, markdown, pasted slide text, or a link to one) and asks for a review, score, feedback, or critique. A full review takes about 15–25 minutes, 40 or more for a long investor deck, so for a general pitch question with no deck, answer briefly in chat and offer the review instead of starting it. When it runs, score with the rubric, never with deck advice from memory.
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Use for analytics and data pipelines. Triggers: ETL, ELT, data quality, SQL optimization, schema drift, backfill, 데이터 파이프라인, 데이터 품질, 백필.
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。