「structure」の検索結果

Query traffic keyword lists for an Amazon ASIN via Nexscope, including traffic source type, conversion type, organic rank, and ad rank with historical month and multi-dimensional sorting. Trigger when user mentions ASIN reverse traffic keywords, traffic keyword list, keyword traffic structure, organic/ad keyword analysis, keyword conversion type, Nexscope traffic keyword, Amazon traffic keywords, reverse ASIN keywords — even if "Nexscope" is not explicitly mentioned, as long as the need involves viewing keyword traffic sources and keyword lists for a specific ASIN.

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。

Break down all competitor ASIN traffic sources under a given keyword -- organic search, SP ads, SB brand ads, SBV video ads, SP recommendations, AC/ER/TR recommendation slots, with support for ASIN filtering, custom date ranges, and new traffic keyword filters. Trigger when the user mentions keyword traffic sources, which competitors are grabbing traffic under this keyword, organic traffic vs paid traffic share, SP ad exposure, brand ad share, SP recommendation slots, recommendation ad/non-ad split, search display analysis, Amazon's Choice or editorial recommendation exposure, keyword competitive landscape, ASIN traffic composition, keyword traffic, traffic structure analysis, search share, ad share, traffic source distribution, Nexscope, traffic analysis, SP recommendation, recommend position breakdown. Even if the user does not explicitly mention "Nexscope", if their need involves analyzing competitor ASIN traffic source distribution under a keyword, 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。

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。

Retrieve Amazon product details by ASIN, including price, title, main image, listing date, material, weight, variant monthly sales, and up to 12 months of monthly sales history. Triggered when users query Amazon product details, ASIN lookup, product pricing, sales rank history, monthly sales trends, product dimensions, FBA fees, product specifications, batch ASIN query, Nexscope product details, ASIN detail lookup, monthly sales data, pricing info, product specifications, FBA fees, batch ASIN query. Even if users do not explicitly mention "Nexscope", this skill should be triggered whenever the task involves obtaining structured product data for one or more Amazon ASINs.

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 detailed Amazon product information by ASIN, including title, Item Highlights when extractable from raw HTML, images, bullet points, specifications, A+ content, pricing, ratings & reviews, variants, and more. Trigger when users mention Amazon product details, ASIN lookup, Item Highlights, product page data, listing analysis, bullet point extraction, product image retrieval, variant inspection, competitor listing research, price lookup, review breakdown, product specification query, or similar terms. Even if the user does not explicitly say "product details," trigger this skill whenever the request involves retrieving structured data from Amazon product pages via ASIN.

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。

Query and manage Grafana dashboards and Prometheus metrics for Happy infrastructure. Covers grafanactl CLI usage, direct Prometheus queries through Grafana proxy, and dashboard-as-code workflows. Use when user asks about metrics, dashboards, monitoring, Grafana, Prometheus, or wants to add/modify panels.

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 or update structured docs under docs/ with frontmatter, numbering, lifecycle status, and index regeneration — guides, references, troubleshooting, design docs and ADRs. Use for "write a doc", "document this", "create a guide", "write an ADR", "update the docs". For the system-design thinking itself (architecture, trade-offs, failure modes) use /ship:arch-design first — it hands back here to record the decision.

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 new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill templates, or understand the Claude Skills specification.

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。

Detecting unnecessary files, unused code, and orphaned files, and proposing safe deletion. Not for removal execution (Builder), repo structure (Grove), or scope cutting (Void).

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。

Provisioning infrastructure via cloud IaC (Terraform/OpenTofu/CloudFormation/Pulumi) and local dev environments (Docker Compose, env vars). Use for IaC design or multi-cloud provisioning.

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。

Designing and auditing repository structure for humans and LLM agents: layouts, monorepos, docs/tests/scripts, progressive disclosure, prompt-cache topology, and safe migrations.

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。

Constructing landing pages from a focused section to a premium multi-stage studio pipeline: structure, copy, conversion, responsive build, craft gates, and launch handoffs. Use when building or optimizing an LP, CTA, conversion flow, or premium launch surface.

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。

Comprehending and investigating codebases: structure mapping, feature discovery, data flow tracing for 'does X exist?' or 'how does Y work?'. Includes a conversational ask mode. Does not write code.

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。

Translate a confirmed mini-program product specification or bounded feature into an implementable architecture covering modules, pages, components, services, canonical state sources, data models, interfaces, permissions, failures, caching, concurrency, idempotency, migration, rollback, external dependencies, and architecture decision records. Use when users ask how to structure a mini program, design data or APIs, divide frontend and cloud responsibilities, evolve an existing system safely, or prepare implementation after product semantics are stable. Preserves product meaning, current repository constraints, user-owned changes, and verifiable acceptance behavior rather than redesigning the product for technical convenience.

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 and optimizes websites for search engine visibility (SEO) and AI search citation (GEO), covering technical health, E-E-A-T content scoring, domain authority, structured data, rich results, and entity signals. Use when running SEO audits, diagnosing traffic drops or ranking losses, generating Schema.org JSON-LD, checking Core Web Vitals, crawlability, robots.txt, sitemaps, hreflang, backlinks, planning content strategy or site migrations, fixing indexing issues, or optimizing for AI Overviews, ChatGPT, and Perplexity. NOT for paid ads (PPC/SEM), social media strategy, email marketing, or general web development unrelated to 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。

Use this when creating, editing, or reviewing Python code — including type hints, docstrings, naming conventions, imports, error handling, and code structure.

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 creating, editing, executing, or reviewing Jupyter notebooks — including cell structure, kernel management, extracting reusable logic to src/, and ensuring notebooks are restartable.

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 shotgun: generate multiple AI design variants, open a comparison board, collect structured feedback, and iterate. (gstack)

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 an uninterrupted work cycle with choice-based activities structured within a realistic time block. Use when planning Montessori-style independent work periods or extended choice 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。

Structure a decision or design challenge through multiple perspectives before committing to action. Use as a synthesis step after scoping, mapping, and dilemma navigation when a group needs a wiser next step.

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 outdoor learning sequence embedding curriculum objectives in an available outdoor space. Use when planning lessons in school grounds, parks, or local natural environments.

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 structured perspective-taking activities with anti-projection guardrails. Develops genuine understanding of complexity across history, social sciences, and literature — not performed empathy.

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。