「rag」の検索結果

Claude adapter for diagnosing PlainTab bugs, broken flows, startup issues, storage or wallpaper regressions, and performance regressions.

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 an agent or user is unfamiliar with a PlainTab module, needs a higher-level map of related files and callers, or asks how a section fits into startup, wallpaper, settings, search, command palette, storage, or i18n flows.

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 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。

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 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。

Nexscope ASIN sales estimates query, returning daily estimated sales and latest known price for a specified ASIN over a given time period, covering 10 marketplaces including US, UK, Germany, and Japan. Triggered when users mention ASIN sales estimates, ASIN daily sales, sales estimation, competitor sales monitoring, average daily sales, sales trends, product sales tracking, Nexscope sales data, sales estimates, daily sales, estimated units sold, ASIN sales tracking, competitor sales monitoring, product sales trend, daily unit sales. Even if users do not explicitly mention "Nexscope", this skill should be triggered whenever the task involves viewing daily estimated sales data for an Amazon ASIN over a time period.

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。

Amazon reverse product selection: filter Amazon niches and keywords by 30+ business dimensions (market size & growth, price tiers & share, competition density & top concentration, demographics such as age/gender/income, review highlights & pain points) from a metrics pool aggregated from historical business insight reports. Trigger when users mention reverse product selection, metrics filtering, niche reverse lookup, blue ocean niche discovery, low-competition niche, newcomer-friendly niche, brand-fragmented market, pain-point entry, feature reverse lookup, pricing tier opportunity, demographic-based selection, or similar terms. Even if the user does not explicitly say "reverse product selection," trigger this skill whenever the request involves filtering Amazon niches that match specific business 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。

Initialize an AgentDB Cognitive Container (.rvf file) in the current project. Sets up storage, embedder config, and the agentdb MCP server. Use when the user is starting a new project that needs vector memory, or asks to "set up agentdb" / "init agentdb".

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。

Maximal Marginal Relevance rerank — get diverse top-k instead of redundant top-k. Use when standard search returns 5 near-duplicates, or when you want broader coverage of a topic.

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。

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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 search engines and vector DBs for full-text, vector, and hybrid retrieval, including permission-aware retrieval for multi-tenant or per-role corpora. Use for search design, index optimization, the RAG retrieval layer, or deciding where ACL filtering belongs in the query path.

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。

Proposing new features leveraging existing data/logic as Markdown specifications. Use when brainstorming new features, product planning, or feature proposals are needed. 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。

Adding edge-case tests, repairing flaky tests, and improving coverage. Use when test gaps need filling or regressions need guarding. Supports JS/TS, Python, Go, Rust, and Java.

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。

Engineering detection rules (Sigma/YARA), detection coverage mapping, threat hunting hypotheses, Purple Team Blue side, Detection-as-Code CI/CD. Use when defensive verification is needed.

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。

Converting a supplied prompt into an executable specification: detects vague quality/quantity/explanation/style/design/technical/judgment wording, role and persona theater, and self-contradiction, then replaces each with a numeric bound, an observable behavior, or a scorable criterion — with a per-term ledger of what changed and what stayed open. Don't use for AI system design, RAG, or eval harnesses (Oracle), PRD/SRS authoring (Scribe), spec conformance verification (Attest), or SKILL.md normalization (Gauge).

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。

Visualizing code, specs, or context as Mermaid, ASCII, or draw.io diagrams: flowcharts, sequence/state/class/ER, Journey Maps, personas, coverage heatmaps. Use to reverse-document systems visually.

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 evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.

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。

Assessing standards, regulatory controls, and legal-document coverage with cited evidence and proposed wording. Use for OWASP/WCAG/SOC2/PCI/HIPAA or ToS/privacy/DPA reviews; not legal advice or code fixes.

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。

Controlling combinatorial explosion across multi-dimensional axes: minimum coverage sets, execution plans, test/deploy/UX/risk prioritization. Use when scoping multi-axis combinations.

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 code review skill covering both requesting a review (pre-commit checklist) and receiving and responding to review feedback. Checks code quality, security, test coverage, architectural alignment, and documentation before any code is committed.

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。

Route data-related requests to the right Monte Carlo skill or workflow. USE WHEN alerts, incidents, data broken, stale, coverage gaps, data quality, or any ambiguous data observability request.

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。