「category」の検索結果

Assign items to a CLOSED label vocabulary that is too large to put in a prompt — product taxonomies, category hierarchies, tag vocabularies, routing tables, ICD/SIC-style code lists. A cheap model writes the label it thinks the vocabulary would use, and an embedder snaps that writing onto the nearest legal value, so the schema is never transmitted and the output is always in-vocabulary. Use for "classify these into our taxonomy", "tag these against the existing tag list", "map these queries to categories", "the enum is too big to send", or a Literal/enum that hits a provider cap. NOT for a vocabulary that fits in a prompt — structured output measured 0.701 acc@1 there against this pattern's 0.564. NOT for open-ended labelling with no fixed vocabulary, and not for ranked retrieval over documents (bm25).

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。

Audits any landing or service page across 48 checks in 10 categories for LLM/AI discoverability, GEO readiness, content clarity, schema markup, internal linking, freshness signals, and technical crawlability. Produces an overall score out of 100, a per-category score out of 100, pass/warn/fail/ manual counts, and a prioritised fix list — all rendered as a standalone HTML report saved to disk. Use when a user provides a URL and asks to audit it, score it, check its GEO readiness, test its AI discoverability, run a landing page audit, or evaluate it for LLM citation potential. Also trigger on phrases like "audit this page", "score this URL", "check GEO for", "is this page AI-ready", "run a GEO audit", "LLM page check", "how would AI read this page", or "landing page score".

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。

Full-lifecycle blog engine with 30 sub-skills, 12 content templates, 5-category 100-point scoring, and 5 specialized agents. Routes user requests to the right sub-skill: writing, rewriting, analysis, outlines, audits, schema, charts, images, repurposing, AI-citation optimization, FLOW framework prompts, topic-cluster execution, and multilingual publishing. Optimized for Google rankings (December 2025 Core Update, E-E-A-T) and AI citations (GEO/AEO). Supports any platform (WordPress, Next.js MDX, Hugo, Ghost, Astro, Jekyll, 11ty, Gatsby, HTML). Use when user says "blog", "write a blog", "blog post", "blog strategy", "content brief", "editorial calendar", "blog audit", "blog optimization", "topic cluster", "multilingual blog", "FLOW framework", or any /blog subcommand. Sub-skill descriptions cover narrower triggers.

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 score blog posts on a 5-category 100-point scoring system covering content quality, SEO optimization, E-E-A-T signals, technical elements, and AI citation readiness. Includes AI content detection (burstiness, phrase flagging, vocabulary diversity). Supports export formats (markdown, JSON, table) and batch analysis with sorting. Generates prioritized recommendations (Critical/High/Medium/Low) with specific fixes. Works with any format (MDX, markdown, HTML, URL). Use when user says "analyze blog", "audit blog", "blog score", "check blog quality", "blog review", "rate this blog", "blog health check".

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。

Extract structured company lists from directories with Firecrawl. Use for scraping YC, Crunchbase, Product Hunt, G2, startup directories, category directories, or custom company databases into JSON, CSV, CRM-ready lists, or research tables.

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。

Designs Amazon-compliant main product images and the 7 secondary image types (Infographic / Multi-angle / Detail Shot / Lifestyle / Variants / What's in Box / Size Reference) — with platform-mandatory main-image rules, conversion-rate-tuned secondary types per product category, and multi-image consistency anchored to the main image URL. Use when the user says "Amazon main image", "亚马逊主图", "product main image", "white background product image", "Amazon listing image", "亚马逊副图", "secondary images", "product carousel images", or any request for the 1:1 product image suite that lives on the Amazon detail page carousel. For A+ Content / detail-page modules (21:9 Hero Banner and 3:2 module layouts), see the `product-shots-detail-page` skill.

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 Cognee memory. Session memory is automatically searched on every prompt via hooks. Use this skill explicitly for permanent knowledge graph search, filtered category search, or when you need more results than the automatic lookup provides.

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。

Store data permanently in the Cognee knowledge graph. Accepts a data category (user, project, or agent) to tag the data with the correct node_set for filtered retrieval.

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。

Analyze a warehouse for stale, unused, or redundant tables via the analyze_storage_costs MCP tool. Classifies waste patterns and table categories, computes safety tiers, and handles category drill-downs and lineage follow-ups.

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 browse Walmart product listings by keyword, category, price range, and other conditions. Trigger when users mention Walmart product search, Walmart product listing, Walmart price comparison, Walmart competitor analysis, Walmart product research, Walmart market data, find products on Walmart, Walmart search, Walmart products, Walmart product selection, Walmart pricing, Walmart competitors, Walmart market. Even if the user does not explicitly mention "Walmart search", trigger this skill whenever their need involves searching for products on Walmart, checking product availability, comparing Walmart prices, or analyzing Walmart product listings.

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。

Filter Temu stores by multiple dimensions (store name/ID, country site, backend category, fully-managed/semi-managed, total/weekly/monthly sales and revenue, rating, reviews, followers, product count, store opening time, etc.). Trigger when users mention Temu store, Temu store analysis, Temu seller, Temu store ranking, Temu semi-managed store, Temu revenue, temu stores, Temu store query. Even if the user does not mention the tool name, trigger this skill whenever they are looking for stores on Temu, filtering store data, or analyzing store performance.

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 analyze TikTok video data, filter videos by region, creator, product, category, views, duration, publish time, selling/ad/AI video flags, and return views, likes, comments, shares, favorites, video sales and GMV metrics across 16 TikTok Shop sites. Trigger when users mention TikTok video search, TikTok video list, TikTok promotional videos, TikTok video data, TikTok video views, TikTok video sales, TikTok video analytics, Nexscope video, TikTok video search, TikTok video list, TikTok video analytics, TikTok promotional videos, TikTok video views, TikTok video engagement. Even if the user does not explicitly mention "Nexscope" or "TikTok", trigger this skill whenever their need involves searching or analyzing TikTok video performance metrics by criteria.

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。

Retrieve the current public details of one Shopee listing from a supported product URL, including price, discount, sales, stock, variants, media, brand, category, shop, and rating data. Use when a user provides a Shopee product URL and asks for listing details, SKU variants, inventory, pricing, or competitor-page analysis. Do not use for keyword search, historical trends, reviews, or seller-account operations.

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 filter TikTok global e-commerce products based on FastMoss data, supporting keyword search, multi-dimensional filtering (category, shop type, commission rate, sales, creator count, etc.) and sorting. Trigger when users mention TikTok product selection, TikTok product search, TikTok product data, TikTok creator sales, TikTok commission rate, TikTok bestseller tracking, TikTok GMV analysis, TikTok product search, TikTok product research, TikTok creator sales, TikTok commission rate, TikTok GMV analysis, FastMoss. Even if the user does not explicitly mention "FastMoss", trigger this skill whenever their need involves searching product data or analyzing product performance on TikTok.

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。

YouYing Shopee product selection tool supporting product query and filtering across all Shopee marketplaces, covering Malaysia, Taiwan (China), Indonesia, Thailand, Philippines, Singapore, Vietnam, Brazil, Mexico, Chile, and Colombia. Triggered when users mention Shopee product selection, Shopee product search, Shopee bestsellers, Shopee market analysis, Shopee category selection, Shopee keyword selection, Shopee sales filtering, Shopee price filtering, Southeast Asia e-commerce product selection, Shopee product search, Shopee product selection, Shopee bestsellers, or Shopee market analysis. Even if the user does not explicitly mention "YouYing" or "Shopee," this skill should be triggered whenever their need involves searching for products or filtering Shopee product data on the Shopee platform.

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 Etsy stores with multi-dimensional filters (sales, favorites, reviews, store opening date, country, primary category, Raving/Star ratings). Trigger when user mentions Etsy store, Etsy shop search, Etsy seller, Etsy shop ranking, Etsy weekly sales stores, Etsy stores, or Etsy store query — even if the tool name is not mentioned, as long as the need is to find stores, filter store data, or analyze store performance on Etsy.

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 an ecommerce URL for SEO + GEO readiness and AI citation visibility. Triggers on "geo score", "SEO + GEO score", "check ecommerce GEO", "Amazon/Shopify GEO", "GEO readiness audit". Covers product pages, store pages, marketplace listings, category pages, and homepages, scored from crawlable evidence (not live AI polling).

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 Etsy products with multi-dimensional filters (keyword/URL, price, sales, favorites, reviews, listing date, category, handmade/vintage types, Pick/Bestseller/Raving tags). Trigger when user mentions Etsy products, Etsy listings, Etsy product sourcing, Etsy bestsellers, Etsy handmade, Etsy vintage, Etsy items, or Etsy product query — even if the tool name is not mentioned, as long as the need is to search products, view sales/price/tags, or filter items on Etsy.

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。

Mercado Libre product selection data query and analysis via the Nexscope gateway, covering 24 tools for products, catalog, keywords, categories, trends, sellers, reviews, exchange rates, and plan usage across Mexico, Brazil, Argentina, Chile, and Colombia sites. Triggered by: Mercado Libre, MercadoLibre, product selection, product search, category trends, hot keywords, reverse traffic keywords, seller search, review search, exchange rate, plan usage, Lanjing, Mercado product 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。

Analyze public Temu category opportunities and keyword demand through Nexscope.

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 Nexscope data to find and analyze competitors on Amazon, covering 12 marketplaces, with product metrics including sales, BSR, pricing, ratings, and growth trends. Trigger when the user mentions competitor lookup, competitor analysis, ASIN reverse lookup, competitive product research, finding similar products, market competitor discovery, product benchmarking, competitor sales estimation, analyzing competitor listings, competitor analysis, ASIN reverse lookup, competitor sales, competitor research, Nexscope, market competitor discovery, competitor trends. Even if the user does not explicitly mention "Nexscope" or "competitor lookup", if their need involves discovering and analyzing Amazon competitors by ASIN, keyword, seller name, brand, or category, this skill should also be triggered.

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。

Detects when a stated outcome names a specific instance — a mechanism-as-means or a single named product — where a category was meant, and returns a neutral fork for the lead to show the user, or nothing. Invoked by launch commands at outcome capture, in place of the verbatim echo.

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。

List all available skills grouped by category. Use when the user asks what skills are available or runs /skills.

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。

Analyze LLM eval results — score breakdowns by category, regression detection vs baseline, failure clustering. Use when asked to "analyze our eval results", "did the model regress", or "cluster the eval failures".

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。