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概要と使いどころ
Structure a student's self-determined project with mentoring checkpoints, competency alignment, and reflection. Use when guiding student-led projects within a competency-based framework.
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Generate scaffolds supporting student self-regulation across planning, monitoring, and evaluation phases. Use when students struggle to manage their own learning during extended or independent tasks.
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。
Create self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying principles.
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 structured goal-setting protocol using SMART or implementation-intention frameworks for students. Use when launching units, projects, or developing student self-direction habits.
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 Shopify standalone store products by multiple dimensions (keyword/URL, price, weekly sales, listing date, Facebook ads, competitiveness, supplier availability, shipping country, etc.). Triggered when users mention Shopify products, Shopify product selection, standalone store product selection, Shopify bestsellers, Shopify dropshipping, standalone store sourcing, Facebook ad products, Shopify product query, or shopify items. Even if the user does not mention the tool name, this skill should be triggered whenever searching for products, viewing weekly sales/revenue/competitiveness, or filtering products on Shopify standalone stores.
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 TikTok e-commerce product leaderboards via Kalodata and query detailed data for specific products. Supports viewing high-ranking/hot-selling products by region, currency, language, and date range, and using productId to retrieve price range, sales, revenue, commission rate, listing/delisting time, and owning shop. Trigger when users mention TikTok product ranking, TikTok bestseller chart, TikTok top products, kalodata product rank, TikTok product detail, TikTok product profile, product price, product sales, TikTok product ranking, TikTok bestseller chart, TikTok top products, kalodata product rank, TikTok product detail, kalodata product detail, product analytics. Even if the user does not explicitly mention "kalodata", trigger this skill whenever their need involves viewing TikTok product leaderboards or detailed data for a specific TikTok product.
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 Russia product search and reverse lookup. Searches Ozon products in the Nexscope database by Russian keyword or SKU, returning product ID, title, brand, and seller information. The entry point for Ozon product discovery and competitor analysis chains. Trigger when the user mentions Ozon product selection, Ozon product search, Russian e-commerce product selection, Ozon keyword search, Ozon SKU query, Nexscope Ozon, Ozon product search, Nexscope Ozon, Russian marketplace, Ozon SKU lookup, Ozon keyword search. Also trigger when the intent is to discover or reverse-lookup products on Ozon Russia by keyword or SKU, 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 Russia category drill-down product list by Russian category path. Returns all products in a category with complete metrics: sales, revenue, price, rating, stock, turnover, lost revenue, supporting multi-dimensional numeric filters, sorting, and currency conversion. Use for category bestseller mining, blue-ocean insight discovery, category ranking analysis, brand landscape observation. Trigger when the user mentions Ozon category drill-down, Ozon category products, Ozon blue-ocean mining, Ozon category bestsellers, Ozon category ranking, Ozon subcategory structure, Ozon niche SKUs, Nexscope category, Ozon category drill-down, Russian marketplace niche, Ozon niche mining, Ozon subcategory bestseller. Also trigger when the intent is to view all products and their sales/price/ranking performance under an Ozon category path, 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。
Query Amazon product historical time-series data, including price trends, BSR (Best Sellers Rank) trends, rating changes, seller counts, and monthly sales, supporting any ASIN across multiple Amazon marketplaces. Triggered when users mention price history, price tracking, BSR history, BSR trends, historical pricing, price fluctuations, Nexscope data, rank history, price drop alerts, lightning deal historical prices, Buy Box price trends, coupon prices, FBA/FBM price comparison, seller count changes, rating trends, sales history. Even if users do not explicitly mention "Nexscope" or "time-series data", this skill should be triggered whenever the task involves analyzing Amazon product-level historical data (such as price, rank, or sales trends over time).
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 market list capability to filter Amazon niche markets by category dimensions, supporting market size, competition, top concentration, seller structure, new product share, price/rating/margin ranges, and many other criteria for discovering viable markets and evaluating product selection directions. Trigger when the user mentions Amazon market research, niche category research, market opportunity screening, market concentration analysis, new product opportunities, market selection, Nexscope market research, category market research. Even if the user does not explicitly mention "Nexscope", if the need is to screen and evaluate Amazon markets by category dimensions, 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 and analyze products on the Chinese 1688 wholesale platform (Alibaba domestic B2B market) for sourcing, supplier discovery, and product selection. Trigger when the user mentions 1688 product search, finding suppliers on 1688, wholesale product queries, factory sourcing, dropshipping supplier search, 1688 keyword product selection, wholesale price comparison, filtering by sales volume, any product research on the 1688 platform, 1688 search, 1688 product selection, find suppliers, factory lookup, wholesale pricing, supplier search, domestic sourcing, 1688 products. Even if the user does not explicitly say "1688", if their need involves searching for wholesale products, finding domestic suppliers, or sourcing from the domestic Chinese market, 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。
Advanced Amazon product search and filtering powered by Nexscope data, supporting multi-dimensional criteria including category, price, monthly sales, keywords, BSR rank, review count, rating, package dimensions, weight, fulfillment type, and more. Trigger when users mention Nexscope product selection, Amazon product search, advanced product selection, BSR filtering, sales rank filtering, keyword-based product discovery, category search, competitor screening, niche product discovery, historical rank filtering, or similar terms. Even if the user does not explicitly mention "Nexscope," trigger this skill whenever the request involves multi-criteria Amazon product search, sales-metric-based filtering, or advanced product selection beyond simple keyword search.
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 cross-selling strategies for insurance products. TRIGGERS - Use when user needs help with cross-selling-insurance related tasks.
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 expansion revenue playbooks — upsell triggers, seat expansion sequences, tier upgrade paths, and cross-sell motions. Use when asked to "grow existing customers", "increase NRR", "design upsell", or "how do we get customers to expand".
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。
Systematic materials selection for engineering design using performance indices, Ashby charts, multi-constraint optimization, and case-based reasoning. Covers the five-step Ashby method — function, objective, constraint, free variable, index — with worked examples for stiffness-limited beams, strength-limited ties, thermally shocked vessels, and minimum-cost components. Use when choosing among metals, polymers, ceramics, and composites under competing objectives.
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。
Meta-cognitive and practical strategies for learning any language -- learner autonomy, goal setting, strategy taxonomy (Oxford's SILL framework), memory strategies, cognitive strategies (note-taking, summarizing, analyzing, reasoning), compensation strategies, metacognitive strategies (planning, monitoring, evaluating), affective strategies (managing anxiety, self-encouragement), social strategies (collaboration, questioning, empathy), immersion design, study habit optimization, plateau diagnosis, and motivation maintenance. Use when designing a language learning plan, diagnosing a learner's stuck points, selecting study methods, or building self-directed learning 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。
Regression analysis, ANOVA, generalized linear models, Bayesian methods, and model selection. Covers the full modeling workflow from problem formulation through diagnostics -- linear regression, logistic regression, Poisson regression, mixed-effects models, prior specification, posterior inference, AIC/BIC comparison, cross-validation for model selection, and assumption checking. Use when fitting models, testing hypotheses, or selecting among competing statistical explanations.
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。
Pick among candidate outputs (code, configs, plans) by running them on diverse inputs and clustering by behavioural fingerprint, rather than by textual aggregation or log-probability. Activates when an executor returns multiple plausible candidates that need disambiguation, when output-majority voting would be the default choice, or when reviewing generated code that has not yet been validated. The 2026 evidence (Semantic Voting, arxiv 2605.08680v1) is that any execution-based selector dominates output-majority voting by 19-52pp; sketch-generated inputs beat random fuzz by 11.3pp. Triggers: "pick the best candidate", "majority vote on code", "select from N samples", "validate the generated output", "behavioural verification".
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。
Run x86 Linux binaries in the browser via WebAssembly. Self-host development environments, interpreters, and even this skill itself.
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。
Dasel v3 selector patterns for Maven POM XML files — use when querying dependency versions, filtering by groupId or scope, extracting module hierarchy from parent POMs, or detecting version conflicts across enterprise multi-module Java projects. Load this skill when working with pom.xml files using dasel.
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。
Badge design and selection knowledge base for shields.io badges in README files. Use when writing or updating READMEs, choosing badge layouts, selecting badge styles, adding project health indicators, or picking Simple Icons logo slugs. Covers shields.io URL encoding rules, static vs dynamic badge selection, style variants (flat/flat-square/for-the-badge/social/plastic), layout patterns (two-tier/inline/centered), project-type badge sets for Python/JS/Rust/Claude plugins, color reference, non-obvious logo slugs, and common anti-patterns to avoid.
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 querying, modifying, or converting JSON, YAML, TOML, XML, CSV, HCL, or INI with dasel v3. Complete reference for selectors, functions, conditionals, variables, spread operator, type casting, and format-specific patterns.
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。
Guidance for selecting appropriate AI model (sonnet vs haiku) based on task complexity, reasoning requirements, and performance needs. Use when implementing agents or justifying model selection.
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。
Create .agent/baseline.md and later compare against it. Use when capturing baseline build/lint/test results or investigating newly introduced findings.
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。