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概要と使いどころ
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。
Retroactively audit an executed AI phase's evaluation coverage — scores each eval dimension as COVERED/PARTIAL/MISSING and produces an actionable EVAL-REVIEW.md with remediation plan
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。
Auto-fix issues found by code review in REVIEW.md. Spawns fixer agent, commits each fix atomically, produces REVIEW-FIX.md summary.
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 keyword mining: mines Ozon (and Wildberries) related keywords around a seed keyword with multi-dimensional filtering by search volume, growth, product count, seller count, competitor count, price, relevancy, title density, cart-add conversion, etc. Each mined keyword carries a full market profile (monthly search volume, growth, market space, competitor/seller counts, average price, cart-add conversion, top products). Use for Ozon keyword expansion, long-tail keyword mining, and seed keyword opportunity analysis. Trigger when the user mentions Ozon keyword mining, Ozon keyword expansion, Ozon long-tail keyword mining, find related keywords around a term, Ozon blue-ocean keyword mining, Nexscope Ozon, Ozon keyword mining, Ozon keyword expansion, Ozon related keywords, mine Ozon keywords. Also trigger when the intent is to mine Ozon related keywords around a seed term and view market profiles, 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 keyword reverse lookup: reverse-looks up Ozon (and Wildberries) search keywords by a list of product SKUs (up to 20), returning which search terms those products appear under (organic/ad search terms), with multi-dimensional filtering by search volume, growth, product count, seller count, competitor count, natural rank, ad rank, exposure, conversion, cart-add conversion, etc. Each keyword carries monthly search volume, growth, market space, competitor/seller counts, average price, cart-add conversion, top products, and organic/ad channel, rank, exposure, and conversion (dimension) market profiles. Use for Ozon keyword reverse lookup, listing keyword optimization, competitor traffic word mining, and ad keyword analysis. Trigger when the user mentions Ozon keyword reverse lookup, Ozon reverse keyword search, Ozon SKU keyword reverse, Ozon product traffic keywords, Ozon competitor ranking keywords, Ozon organic/ad keyword reverse lookup, Nexscope Ozon, Ozon keyword back search, Ozon reverse keyword lookup, Ozon SKU keyword reverse. Also trigger when the intent is to reverse-lookup Ozon search keywords by product SKU and view market profiles, 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 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 product report search: filters Ozon products by multi-dimensional metrics including sales, revenue, sales/revenue growth rate, cart conversion rate, order conversion rate, price, rating, review count, QA count, variant count, page views, gross margin, return/cancellation rate, ad spend share, weight/volume, listing time, brand, seller, fulfillment method, and labels. Returns each product's SKU, title, price (RUB), sales, revenue, lost revenue, conversion rate, rating, reviews, brand, seller, fulfillment method, listing days/months, and complete product report fields. Use for Ozon product selection, competitor product analysis, best-seller mining, price/conversion band filtering. Trigger when the user mentions Ozon product report, Ozon product selection, Ozon product filtering, Ozon product analysis, Ozon best-selling products, Ozon competitor product analysis, Ozon product report, Ozon product screener, filter Ozon products by sales, Ozon best-seller mining, Nexscope Ozon product report. Also trigger when the intent is to filter Ozon products by multiple metrics and view product-level reports, 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 market hot keyword search: filters Ozon (and Wildberries) market keywords by multi-dimensional metrics including search volume, growth, product count, seller count, competitor count, price, sales, conversion concentration, etc. Each keyword carries monthly search volume, growth, market space, competitor/seller counts, average price, cart-add conversion, top products, and market profile. Use for Ozon keyword selection, blue-ocean keyword mining, and market opportunity analysis. Trigger when the user mentions Ozon hot keywords, Ozon keyword market analysis, Ozon keyword selection, Ozon blue-ocean keywords, Ozon search volume, Wildberries keywords, Nexscope Ozon, Ozon market keyword search, Ozon keyword research, blue ocean keywords Ozon. Also trigger when the intent is to filter Ozon market keywords by metrics and view market profiles, 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。
Initiate natural language Q&A through Amazon's storefront Alexa shopping assistant to get shopping guidance answers, recommended product groups, ASIN lists, and follow-up questions. Each call supports only 1 prompt; for follow-ups, the agent must summarize context and concatenate a new question for a new request. A url can be used to supplement Amazon page context. Trigger when the user mentions Amazon Alexa, Alexa shopping assistant, Amazon smart assistant, AI shopping guide, conversational product selection, natural language shopping, Amazon chat Q&A, Amazon Alexa shopping, conversational shopping, AI shopping assistant, follow-up questions, product recommendation conversation, context follow-ups. Even if the user does not explicitly mention "Alexa", if their need is to "ask for product recommendations on Amazon using natural language", 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。
Search with feature attributions — return WHY each match scored where it did. Use when debugging recall quality, auditing for bias, or explaining results to a user.
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。
K-hop traversal from a starting node in AgentDB's graph. Use to explore neighborhoods, find reachable nodes, or visualize a memory's "context".
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 worked example fading sequence from fully worked examples through to independent practice. Use when teaching procedures, algorithms, or multi-step processes to novice learners.
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 Montessori three-period lesson for introducing concepts through concrete materials and naming. Use when teaching vocabulary, classifications, or concepts through hands-on materials.
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 variation theory task using contrast, separation, and fusion to teach critical features of a concept. Use when students confuse similar concepts or fail to discern key distinctions.
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。
Develop TPACK for integrating a specific technology or AI tool into subject teaching with pedagogical alignment. Use when adopting new ed-tech, reviewing AI tools, or planning technology integration.
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。
Script a teacher think-aloud demonstrating expert reasoning processes for a specific task. Use when modelling problem-solving, writing, reading comprehension, or analytical processes.
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 complete lesson study cycle from research question through collaborative planning to research lesson. Use when planning jugyou kenkyuu or collaborative teacher inquiry into practice.
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 media deconstruction protocol analysing persuasion, bias, or representation in media texts. Use when teaching students to critically read advertisements, news, or social media content.
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 single-point rubric with one criterion and open columns for evidence. Use for student self-assessment, peer feedback, teacher formative feedback, or pre-task planning. Works with any learning target, with or without a band system.
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。
Select and sequence reading comprehension strategies matched to a specific text's challenges and demands. Use when students struggle with a text or need targeted reading support.
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 sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.
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。
Redesign a direct instruction sequence to include productive struggle before the explanation phase. Use when teaching concepts that benefit from failure-first approaches.
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 professional development session using adult learning principles with active teacher engagement. Use when planning INSET days, CPD workshops, or staff training sessions.
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 structured reflection prompts for a specific teaching experience or professional learning challenge. Use when debriefing lessons, journaling, or preparing for coaching conversations.
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。