AI Overview (AI Mode) scraping via Google Search. Returns AI-summarized key points for a single keyword, ideal for deep research, technical Q&A, long-tail product selection, and cross-border consumer preference analysis using the latest web information. Single-round only; follow-ups require the agent to summarize context and issue a new request. Triggered by: Google AI, AI Overview, AI Mode, Google AI search, AI search, deep research, consumer preference analysis, web summary, long-tail product research, cross-border market insights.
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☆ 912026年10月14日 更新
Route Amazon product-selection work across market, keyword, competitor, trend, review, and profitability capabilities. Use this skill when the user asks for this exact ecommerce workflow, analysis, data lookup, or deliverable.
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☆ 912026年10月14日 更新
Query 1688 product bestseller billboard data for sourcing discovery and wholesale product research. Trigger when the user mentions 1688 product rankings, 1688 bestseller lists, wholesale hot products, domestic sourcing rankings, dropshipping product selection, 1688 trending products, bulk purchase popular items, supplier product rankings, 1688 billboard, 1688 bestsellers, sourcing rankings, wholesale hot products, trending product rankings, supplier rankings, 1688 trends. Even if the user does not explicitly mention "1688 billboard", if their need involves discovering hot-selling wholesale products or sourcing opportunities on the 1688 platform, 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☆ 912026年10月14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☆ 912026年10月14日 更新
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。
nexscope-ai/nexscope-ecommerce-skills☆ 912026年10月14日 更新
Query Amazon business insight reports by keyword, covering six dimensions: market potential, product characteristics, user reviews, customer profiles, search trends, and pricing analysis with AI-powered comprehensive analysis. Trigger when users mention Amazon business insights, market opportunity reports, product selection reports, competitive landscape, consumer profiles, pricing analysis, niche market research, or similar terms. Even if the user does not explicitly say "business insights," trigger this skill whenever their request involves a comprehensive market opportunity assessment or report generation for an Amazon keyword.
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☆ 912026年10月14日 更新
Use Nexscope market statistics capability to output a market statistics dashboard by category node, including top listing average rating, average price, BSR, sales, seller count, and new product related metrics, suitable for quickly assessing the market quality and competitive landscape of a category. Trigger when the user mentions category market statistics, market selection dashboard, market fundamentals assessment, node market quality, top product statistics, Nexscope market statistics, category statistics. Even if the user does not explicitly mention "Nexscope", if the need is to view aggregated statistical results by category node, 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☆ 912026年10月14日 更新
Design a document set for a document-based lesson — selecting and sequencing sources for analytical tension around a central question. Use when assembling sources for a new lesson or when an existing set produces flat responses.
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。
nota-america/forgecat-agent-profiles☆ 912026年9月24日 更新
Map which elements of an assignment benefit from AI assistance vs. which AI use undermines. Use when redesigning tasks for AI-age classrooms or setting defensible AI use policies for specific assignments.
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。
nota-america/forgecat-agent-profiles☆ 912026年9月24日 更新
Generate AI design contract (AI-SPEC.md) for phases that involve building AI systems — framework selection, implementation guidance from official docs, and evaluation strategy
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☆ 902026年10月14日 更新
Gather phase context through adaptive questioning before planning. Use --all to skip area selection and discuss all gray areas interactively. Use --auto to skip interactive questions (Claude picks recommended defaults). Use --chain for interactive discuss followed by automatic plan+execute. Use --power for bulk question generation into a file-based UI (answer at your own pace).
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☆ 902026年10月14日 更新
Cross-chain & L2 Architect: Chain selection, rollups, bridges and cross-chain messaging with their trust assumptions.
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。
alvin-reyes/better-agentic-ide☆ 902026年10月13日 更新
Seed-stage growth playbook focused on channel selection and prioritization. Helps founders with product-market fit decide where to invest limited resources for maximum traction. By @WeiYipei.
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。
Gingiris-1031/gingiris-skills☆ 842026年10月8日 更新
The complete first-timer's guide to launching on Product Hunt. Covers timing, hunter selection, asset prep, launch-day engagement, and post-launch follow-up. Follow @WeiYipei on X for more launch strategies.
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。
Gingiris-1031/gingiris-skills☆ 842026年10月8日 更新
A reusable Go-to-Market strategy template for both B2B and B2C launches. Covers positioning, messaging, ICP definition, channel selection, and competitive analysis frameworks. By @WeiYipei.
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。
Gingiris-1031/gingiris-skills☆ 842026年10月8日 更新
Kolu's project-wide default for filesystem monitoring is `@parcel/watcher`. Reach for this skill when adding or modifying any code that watches files or directories — recursive subtree watching, single-file observation, fs.watch alternatives, chokidar replacement, inotify/FSEvents/watchman backend selection, ignore globs, watcher debouncing, or refcounted shared subscriptions. Covers backend dispatch, the watchman invocation path, ignore handling, post-install reconciliation, and the failure modes Kolu's logger surfaces.
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。
juspay/kolu☆ 812026年10月14日 更新
Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.
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。
001TMF/harness-forge☆ 802026年6月15日 更新
Transforms Claude into a CFA-level financial analyst for specialty finance, regulatory compliance, and industry-specific analysis. Use when performing private credit pricing, insurance reserving, FP&A variance analysis, wealth planning, restructuring recovery analysis, real estate valuation, venture capital analysis, ESG assessment, regulatory capital analysis (Basel III), compliance reporting (MiFID II, GIPS), credit derivatives (CDS, CVA), convertible bond analysis, lease accounting (ASC 842/IFRS 16), pension funding/LDI, sovereign risk assessment, real option valuation, equity research (SOTP, target price), commodity spread trading, treasury operations, PPP infrastructure modelling, onshore fund structuring, offshore fund structuring, transfer pricing analysis (BEPS, Pillar Two), tax treaty optimisation, FATCA/CRS compliance, economic substance analysis, regulatory reporting (AIFMD, Form PF), AML/KYC compliance, crypto/DeFi analysis, municipal bond analysis, structured products, trade finance, fund of funds (J-curve, commitment pacing, manager selection, secondaries), bank analytics (NIM, CAMELS, CECL, deposit beta, loan book), carbon markets (credit pricing, ETS, CBAM, offsets, shadow carbon price), or private wealth (concentrated stock, philanthropic vehicles, wealth transfer, direct indexing, family governance) analysis is required. Pairs with corp-finance-mcp tools for computation.
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。
FDU-INS/Insurance-Skills☆ 762026年7月12日 更新
Guide farm insurance selection and crop coverage. TRIGGERS - Use when user needs help with farm-insurance-guide 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。
FDU-INS/Insurance-Skills☆ 762026年7月12日 更新
Guide cyber insurance selection with coverage analysis. TRIGGERS - Use when user needs help with cyber-insurance-guide 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。
FDU-INS/Insurance-Skills☆ 762026年7月12日 更新
Guide D&O insurance selection and coverage analysis. TRIGGERS - Use when user needs help with directors-officers-guide 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。
FDU-INS/Insurance-Skills☆ 762026年7月12日 更新
Use the corp-finance-mcp server tools for specialty finance, regulatory, and compliance calculations. Invoke when performing private credit (unitranche, direct lending, syndication), insurance (loss reserving, premium pricing, Solvency II SCR), FP&A (variance analysis, break-even, working capital, rolling forecast), wealth management (retirement planning, tax-loss harvesting, estate planning), restructuring (recovery analysis, distressed debt), real assets (property valuation, project finance), venture capital (dilution, convertible instruments, fund returns), ESG (scoring, climate/carbon, green bonds, SLL), regulatory capital (Basel III, LCR/NSFR, ALM), compliance (MiFID II best execution, GIPS reporting), credit derivatives (CDS pricing, CVA/DVA), convertible bonds (binomial tree pricing, scenario analysis), lease accounting (ASC 842/IFRS 16, sale-leaseback), pension & LDI (funding analysis, liability-driven investing), sovereign risk (bond analysis, country risk), real options (binomial valuation, decision trees), equity research (SOTP, target price), commodity trading (spread analysis, storage economics), treasury management (cash management, hedge effectiveness), infrastructure finance (PPP models, concession valuation), crypto (token valuation, DeFi analysis), municipal bonds (pricing, credit analysis), structured products (notes, exotic), trade finance (LC, supply chain), fund structuring (US onshore, UK/EU, Cayman/BVI offshore, Luxembourg/Ireland), transfer pricing (BEPS/Pillar Two, intercompany pricing), tax treaty (treaty network optimization, holding structures), FATCA/CRS (reporting, entity classification), economic substance (multi-jurisdiction testing), regulatory reporting (AIFMD Annex IV, SEC Form PF, CFTC CPO-PQR), AML compliance (KYC risk scoring, sanctions screening), fund of funds (J-curve, commitment pacing, manager selection, secondaries pricing), bank analytics (NIM analysis, CAMELS rating, CECL provisioning, deposit beta, loan book), carbon markets (credit pricing, ETS compliance, CBAM, offset valuation, shadow carbon price), private wealth (concentrated stock, philanthropic vehicles, wealth transfer, direct indexing, family governance). All computation uses 128-bit decimal precision.
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。
FDU-INS/Insurance-Skills☆ 762026年7月12日 更新
Coordinates credit monitoring and identity theft protection services for individuals affected by a data breach. Covers vendor selection criteria, enrollment logistics, coverage duration (12-24 months), identity theft insurance options, communication to affected individuals, and enrollment rate tracking. Keywords: credit monitoring, identity protection, breach response, Experian, enrollment, identity theft insurance.
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。
FDU-INS/Insurance-Skills☆ 762026年7月12日 更新
Deterministic-first mobile policy for tool selection, verification, and escalation.
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。
FDU-INS/Insurance-Skills☆ 762026年7月12日 更新