「eth」の検索結果

Text trademark detection and infringement risk analysis for e-commerce product listings. Trigger when users mention trademark detection, trademark risk check, brand infringement screening, product title trademark scan, text trademark search, listing compliance check, intellectual property risk assessment, text trademark detection, trademark infringement, brand infringement screening, listing compliance, intellectual property risk, Ruiguan. Even if the user does not explicitly say "trademark", trigger this skill whenever their need involves checking whether product text (titles, descriptions, bullet points) contains potentially infringing trademarks.

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。

Detect and search for similar utility/invention patents based on product information. Trigger when users mention utility patent detection, patent infringement risk, patent similarity search, patent screening, invention patent search, patent risk assessment, TRO (Temporary Restraining Order) risk analysis, utility patent, invention patent detection, patent infringement risk, patent search, TRO risk, Ruiguan. Even if the user does not explicitly say "utility patent", trigger this skill whenever their need involves checking whether a product may infringe existing utility/invention patents before selling in a target market.

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。

Detect image copyright infringement risks by comparing against a database of registered copyrighted works with similarity scoring, TRO litigation history, and radar-based infringement assessment. Triggered when users mention copyright detection, image infringement check, copyright risk analysis, TRO risk, copyright similarity search, rights owner lookup, or copyright compliance verification. Even if the user does not explicitly say "copyright," this skill should be triggered whenever the need involves checking whether an image may infringe registered copyright works.

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。

Detect design patent infringement risks by comparing a product image against a global design patent database across 25+ jurisdictions. Triggered when users mention design patent detection, patent infringement check, patent risk analysis, TRO case lookup, design patent search, product design patent screen, or visual patent similarity. Even if the user does not explicitly say "design patent," this skill should be triggered whenever the need involves checking whether a product image may infringe existing design patents.

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 patent legal status information from the Nexscope (Nexscope) database. Triggered when users mention patent legal status, patent validity check, patent status query, patent event history, simple legal status, legal events such as transfer, license, pledge, opposition, litigation, re-examination, patent legal status, patent validity, patent events, transfer/license/pledge, Nexscope, or patent status lookup. Also triggered when users ask whether a patent is active, inactive, pending, expired, granted, withdrawn, or revoked, or want to look up legal status by patent ID or publication number.

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。

Nexscope Ozon shop product search: fetches the product list of an Ozon shop (seller) by shop ID, returning each product's 30-day sales, price, rating, weight, fulfillment method (FBO/FBS), seller type (local/cross-border), return/cancellation rate, and the shop's total 30-day sales. Use for competitor shop product analysis, shop bestseller mining, seller product structure analysis. Trigger when the user mentions Ozon shop products, Ozon seller product list, competitor shop analysis, Ozon shop bestsellers, Ozon seller analysis, Nexscope Ozon shop search, Ozon shop search, Ozon seller products, competitor shop analysis, Ozon store products. Also trigger when the intent is to view an Ozon shop/seller's products and sales data, even without explicitly mentioning 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。

Nexscope Ozon category product search: fetches the product list for a given Ozon category ID, returning category-level aggregates (total sales, total revenue, average price, average rating, seasonality) and per-product sales, price, rating, review count, brand, seller, and fulfillment method. Use for category selection analysis, category bestseller mining, category capacity and price band analysis, seasonality assessment. Trigger when the user mentions Ozon category products, Ozon category analysis, Ozon category selection, Ozon category bestsellers, Ozon category total sales, Ozon category average price, Ozon category search, Ozon category products, category best-sellers, category analysis. Also trigger when the intent is to view products and category-level summary data within an Ozon category, even without explicitly mentioning 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。

Nexscope Ozon product detail query: fetches the complete detail of a single Ozon product by SKU, returning title, price (RUB), rating, review count, QA count, total and daily average sales within the stats window, revenue, stock, category ranking, daily sales trend, brand, seller, fulfillment method (FBO/FBS/OZON), weight, and listing time/days/months. Use for single product deep analysis, competitor product teardown, Ozon product selection assessment, listing diagnosis, sales trend and category ranking tracking. Trigger when the user mentions Ozon product detail, Ozon single product analysis, Ozon SKU query, competitor product data, Ozon sales trend, Ozon category ranking, Ozon stock, Ozon listing time, Nexscope Ozon product search, Ozon product detail, Ozon SKU lookup, single product analysis, competitor product teardown, Ozon sales trend, category rank. Also trigger when the intent is to view detailed data of an Ozon product, even without explicitly mentioning 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 search and filter Amazon products, supporting multi-dimensional criteria including price, monthly sales, BSR ranking, gross margin, ratings, fulfillment method, badges, seller origin, and more across multiple Amazon marketplaces. Trigger when the user mentions Amazon product selection research, product filtering, sales filtering, product discovery, BSR analysis, niche product discovery, competitor analysis, market opportunity assessment, product-level market size estimation, gross margin screening, Nexscope product selection, Amazon product selection, sales filtering, BSR analysis, profit screening, market analysis, product selection tool. Even if the user does not explicitly mention "Nexscope", if their need involves filtering and analyzing Amazon product-level data for product selection, 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。

Browser automation CLI for AI agents. Use this when asked to test something in a real 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。

Improve existing code without changing behavior: scan smells, simplify, dedupe, reuse utilities, and verify after edits. Use for refactor, cleanup, simplify, reduce duplication, extract method, dead code, or code smells. No PR.

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 something breaks in production: triage the severity, gather evidence, identify root cause, deploy a fix or mitigation, and write a post-mortem. Provides a calm, structured process for high-stress moments.

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 whenever you learn something important during a conversation: a key architectural decision, a project-specific convention, a bug root-cause, a 3rd-party API quirk, or any fact that would help a future agent avoid re-doing the same research. Writes structured entries to the project's knowledge base so that context persists across conversations and agents.

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。

Scan a repo for mixed ADRs, PRDs, SPECs, and DOCs and bootstrap or merge the full .planning/ setup from them. Classifies each doc in parallel, synthesizes a consolidated context with a conflicts report, and routes to new-project or merge-milestone depending on whether .planning/ already exists.

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 evidence-first engineering for WeChat and other mini programs from a vague idea or existing repository through project discovery, product specification, architecture, implementation, debugging, device adaptation, verification, and release readiness. Use when users ask to build a mini program from zero to one, take over an existing mini-program project, deliver a cross-stage feature, diagnose project status, coordinate development and testing, or judge whether a mini program is ready to upload or release. Enforces fact discovery, change boundaries, stage gates, evidence-calibrated status, sensitive-information redaction, and continuity after side questions.

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 evidence-first engineering discipline to any agent-built software project: fact discovery before action, evidence-calibrated status reporting, explicit change boundaries and stage gates, controlled confirmation for risky or external actions, sensitive-information redaction for anything shared publicly, and resumable continuity after interruptions. Use when users ask an agent to take over an existing codebase, deliver a feature across stages, verify whether work is actually complete, judge release readiness, or keep conclusions honest about what is proven versus assumed. This foundation skill is domain-neutral; vertical suites (for example mini-program engineering) build on it by adding domain facts and platform rules.

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。

Verify mini-program implementations and fixes with risk-calibrated evidence across static checks, unit tests, integration tests, state matrices, simulators, real devices, cloud environments, and release artifacts. Use when users ask to test, validate, accept, regression-check, quality-check, confirm readiness, or determine whether a mini-program feature is actually complete. Binds results to a source and build fingerprint, records commands and observable evidence, separates passed, failed, blocked, and not-run layers, prioritizes the next highest-information check, and never converts local success into device, cloud, release, or formal acceptance claims.

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 when reviewing statistical analysis, experiments, model evaluation, or ML work done by someone else (a notebook, PR, or report) — checking that the required context is stated and that every method applied comes with the figures and values required by the matching *-diagnostics skill. When running an analysis yourself, apply the matching *-diagnostics skill directly.

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。

After the learner demonstrates understanding of a concept, present near-transfer and far-transfer challenges. Use to test whether learning is portable or task-specific — this is what separates understanding from familiarity.

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 a learner gets something wrong or feels stuck, require them to diagnose the problem before receiving help. Ensures help targets the actual cognitive breakdown, not just the surface error.

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 a learning task where mixed-age students work together with mutual benefit for all age groups. Use when planning cross-age tutoring, vertical grouping, or multi-year group activities.

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 formative assessments that make students' historical thinking visible — revealing whether they source, close-read, contextualise, and corroborate. Use when assessing historical thinking skills or planning a diagnostic.

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 a diagnostic hinge question that reveals whether students understand enough to move on. Use when planning key checkpoints mid-lesson during explicit or direct instruction.

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 follow-up teaching moves that extend student thinking after a specific classroom response. Use when a student says something worth exploring and the teacher wants to deepen the dialogue.

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。