Use Nexscope (Search Intelligence Framework) data to analyze ASIN traffic source composition and exposure distribution, covering current period/previous period/newly entered/exited period comparisons. Trigger when the user mentions ASIN traffic sources, traffic structure analysis, organic traffic vs paid traffic share, exposure score breakdown, period comparison, new/exited traffic keywords, competitor traffic analysis, SP ad keyword count, brand ad exposure, Amazon's Choice exposure, editorial recommendation exposure, Top Rated exposure, video ad exposure, organic search exposure ratio, PPC traffic sources, promotional deal traffic sources, recommendation slot structure breakdown, ASIN traffic analysis, traffic sources, organic traffic share, ad traffic share, exposure analysis, traffic structure, period-over-period comparison, keyword churn, Nexscope. Even if the user does not explicitly mention "Nexscope", if their need involves analyzing an ASIN's traffic sources, exposure channel distribution, cross-period comparison, or competitor traffic structure comparison, 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。
nexscope-ai/nexscope-ecommerce-skills☆ 942026年10月15日 更新
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。
nexscope-ai/nexscope-ecommerce-skills☆ 942026年10月15日 更新
Nexscope keyword Share of Voice analysis, returning brand visibility share across the first 3 pages of Amazon search results (organic/ad/combined), 30-day exact search volume, median PPC bid, and TOP3 ASIN click and conversion data, covering 10 marketplaces. Trigger when the user mentions brand market share, brand voice share, search result brand distribution, Share of Voice, SOV analysis, brand competitive landscape, ad slot share, organic ranking brand share, PPC bid analysis, brand monopoly analysis, keyword share of voice, brand visibility, organic vs sponsored share, brand dominance, PPC bid analysis, search result brand distribution, competitive landscape, weighted SOV, top ASIN clicks conversions. Even if the user does not explicitly mention "Share of Voice" or "SOV", if their need involves analyzing brand market share or competitive landscape in Amazon keyword search results, 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。
nexscope-ai/nexscope-ecommerce-skills☆ 942026年10月15日 更新
Nexscope Product Database multi-condition filtering. Filter Amazon products by category, price, sales volume, revenue, reviews, rating, weight, BSR rank, LQS, seller type, and more across 10 marketplaces. Trigger when users mention Amazon product selection, product database filtering, BSR rank filtering, category-based product discovery, high-rating low-competition products, FBA product search, Amazon product discovery, or similar terms. Even if the user does not explicitly mention "Nexscope" or "product database," trigger this skill whenever the request involves filtering Amazon products by multiple criteria or discovering potential products.
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-ai/nexscope-ecommerce-skills☆ 942026年10月15日 更新
Query Amazon product detail and historical trends by ASIN using Nexscope data, covering 14 marketplaces. Trigger when the user mentions Nexscope product detail, ASIN detail query, sales trends, price curves, price history, BSR ranking history, BSR trends, profit analysis, FBA fee analysis, gross margin, product trend analysis, daily/monthly sales, revenue trends, Deal promotion history, product detail, sales trend, price history, BSR ranking, profit analysis, FBA fees. Even if the user does not explicitly mention "Nexscope", if their need involves querying Amazon product detail or historical trend data by ASIN, 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。
nexscope-ai/nexscope-ecommerce-skills☆ 942026年10月15日 更新
Promote a validated pattern into a reusable Skill in AgentDB's skill library. Use when the same approach has worked 3+ times across episodes, or when the user explicitly says "make this a skill" / "save this as reusable".
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。
ruvnet/agentdb☆ 932026年10月12日 更新
Authoring standalone and cross-team specifications: PRD/SRS/HLD/LLD, staged L0-L4 unified packages, BDD acceptance criteria, and traceability. Use for technical or multi-audience documentation; not implementation or architecture decisions.
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。
simota/agent-skills☆ 932026年10月16日 更新
Orchestrating migrations, upgrades, and modernization across frameworks, libraries, APIs, databases, and dependencies. Generates codemods, applies Strangler Fig, verifies equivalence, plans rollback.
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。
simota/agent-skills☆ 932026年10月16日 更新
Analyzing pre-change impact across vertical (dependency chains, files) and horizontal (pattern consistency, naming) dimensions. Use to estimate blast radius before a refactor. No 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。
simota/agent-skills☆ 932026年10月16日 更新
Converting technical knowledge into durable learning documents and publishable articles. Use for diff-based teaching, decision records, onboarding, note/Zenn/Qiita/dev.to posts, article series, retrospectives, and cross-platform repurposing.
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。
simota/agent-skills☆ 932026年10月16日 更新
Orchestrating ecosystem self-evolution: lifecycle-phase detection, agent relevance, cross-agent knowledge synthesis, evolution proposals. Use when auditing skill-ecosystem health or fitness.
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。
simota/agent-skills☆ 932026年10月16日 更新
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。
simota/agent-skills☆ 932026年10月16日 更新
Defining and managing design tokens, applying token systems to existing codebases, building design system foundations. Use for spacing, color, typography, dark mode, cross-platform output.
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。
simota/agent-skills☆ 932026年10月16日 更新
Implementing production iOS/Android/macOS native features (SwiftUI, Compose) and iterating a screen against a reference design. Not for cross-platform RN/Flutter (Forge prototypes only; production is out of scope) or web (Artisan).
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。
simota/agent-skills☆ 932026年10月16日 更新
Refracting thinking by challenging assumptions, combining cross-domain knowledge, and shifting perspectives to reframe problems. Use for stuck situations or paradigm shifts. 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。
simota/agent-skills☆ 932026年10月16日 更新
Curating cross-agent knowledge and institutional memory: extracts patterns from agent journals into METAPATTERNS.md, detects knowledge decay, propagates best practices. Use for memory curation.
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。
simota/agent-skills☆ 932026年10月16日 更新
Orchestrating design-to-implementation pipelines (code to visual to code closed loop), persisting a project design system across agents. Not for a single prototype (Forge) or direction only (Vision).
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。
simota/agent-skills☆ 932026年10月16日 更新
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。
omergocmen/vibe-coder-kit☆ 932026年6月4日 更新
Diagnoses pipeline performance issues -- slow jobs, expensive queries, latency trends -- using Monte Carlo's cross-platform observability. Uses a tiered investigation approach: discover problems, bridge to affected tables, then drill into root causes. Activates when a user asks about slow pipelines, expensive queries, or 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。
monte-carlo-data/mc-agent-toolkit☆ 932026年10月17日 更新
Guide users from coverage analysis to monitor creation. USE WHEN user asks what should I monitor, where are my gaps, improve coverage, or wants a systematic approach to monitoring across their data estate.
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。
monte-carlo-data/mc-agent-toolkit☆ 932026年10月17日 更新
Request cross-AI peer review of phase plans from external AI CLIs
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。
buildomator/buildomator☆ 922026年10月17日 更新
Manage persistent context threads for cross-session work
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。
buildomator/buildomator☆ 922026年10月17日 更新
Systematic debugging with persistent state across context resets
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。
buildomator/buildomator☆ 922026年10月17日 更新
Cross-phase audit of all outstanding UAT and verification items
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。
buildomator/buildomator☆ 922026年10月17日 更新