254 件 ・ 関連度順
概要と使いどころ
Scan the current market landscape and produce a ground-truth ecosystem snapshot. Chains /last30days for live research, validates with user, and writes brand/landscape.md with a Claims Blacklist that hard-gates all content generation. Use when: "landscape", "ecosystem", "market snapshot", "ground truth", "what's happening", "refresh landscape", "market trends", or before any content campaign to verify claims.
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 a MISP (Malware Information Sharing Platform) instance via PyMISP to compute event statistics, IOC type breakdowns, threat actor galaxy clusters, and tag trends, and generate threat landscape reports with temporal trends. Use when asked to analyze threat intelligence data, summarize top threat actors or malware families, or produce a CTI landscape report from MISP events.
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。
Patent research and IP landscape: prior-art search, claim mapping, landscape analysis, and freedom-to-operate. Use when conducting a prior-art search, mapping an IP landscape, evaluating patentability, or running an FTO analysis.
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。
Think and work like an expert Landscape Ecologist. Use when a task calls for Landscape Ecologist judgment. Reasons from scale-explicit pattern-process feedbacks, configuration over composition, and functional rather than graphical connectivity through FRAGSTATS, landscapemetrics, Circuitscape resistance surfaces, NLMR neutral models, and block cross-validation, while treating MAUP grain artifacts, classification error propagation, isolation-by-distance confounding isolation-by-resistance, and resistance surfaces unvalidated by movement as first-class failure modes.
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。
Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends.
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。
Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends.
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。
Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends.
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。
Deep competitive intelligence for any market. Analyzes competitors' products, pricing, customer sentiment, GTM strategy, and growth signals using real web data. Produces battle cards, pricing landscape, and feature matrix. Use when the user wants to understand their competitive landscape, analyze competitors, compare products in a market, or research who they're competing against. Triggers for "who are my competitors", "competitive analysis", "competitor research", "battle cards", "pricing comparison", "competitor pricing", "market players", "competitive intelligence", "competitive landscape", "who else is in this space", "competitive moat", or any request to profile, compare, or map competitors in a category. Works standalone — no prior startup-design session 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。
Map the competitive landscape before positioning your product for PRD v0.2 Market Definition. Triggers on completing v0.1 Spark, analyzing competitors, researching market, or requests like "competitive analysis", "who else solves this", "market landscape", "what alternatives exist", "competitor research", "feature comparison". Outputs CFD- entries for competitive intelligence and BR- entries for positioning rules.
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 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。
Deep analysis of Amazon niche markets by product ASIN, covering monopoly level, brand concentration, new product success rate, and market opportunity score. Trigger when the user mentions niche market analysis by ASIN, ASIN market research, ASIN niche lookup, monopoly assessment, brand concentration analysis, new product success rate, market demand score, competitive landscape, Amazon sub-market exploration, ASIN niche analysis, niche by ASIN, monopoly level, brand concentration, new product success rate, market opportunity score, competitive landscape, Nexscope data. Even if the user does not explicitly mention "niche market" or "ASIN", if their need involves evaluating the competitive landscape, brand density, or opportunity potential of the niche segment a specific product ASIN belongs to, 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。
Deep analysis of Amazon niche markets by keyword, covering monopoly level, brand concentration, new product success rate, and market opportunity score. Trigger when the user mentions niche market analysis, keyword market research, monopoly assessment, brand concentration analysis, new product success rate, market demand score, competitive landscape, Amazon sub-market exploration, niche market analysis, keyword market, monopoly level, brand concentration, new product success rate, market opportunity score, competitive landscape, Nexscope data. Even if the user does not explicitly mention "niche market", if their need involves evaluating the competitive landscape, brand density, or opportunity potential of the Amazon market at the keyword dimension level, 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。
Map the intellectual property landscape for a technology domain or product area. Covers patent cluster analysis, white space identification, competitor IP portfolio assessment, freedom-to-operate preliminary screening, and strategic IP positioning recommendations. Use before starting R&D in a new technology area, when evaluating market entry against incumbents with strong patent portfolios, preparing for investment due diligence, informing a patent filing strategy, or assessing freedom-to-operate risk for a new 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。
Expert landscape designer transforming yards through photo mapping, 3D visualization, seasonal planning, and deep plant knowledge. Specializes in fast-growing privacy screens (knows arborvitae pitfalls), architecture-appropriate design, outdoor living spaces, and realistic maintenance expectations. Activate on "landscape design", "yard design", "garden planning", "plant selection", "privacy screen", "outdoor living", "backyard makeover", "arborvitae", "hedge", "fast growing tree", "landscaping ideas". NOT for interior design (use interior-design-expert), hardscape construction (consult contractors), or lawn care chemicals (consult local experts).
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。
Map the competitive landscape for a Stellar project idea. Use when a user says "who are my competitors on Stellar", "competitive analysis Stellar", "what already exists in this space on Stellar", "show me similar Stellar projects", "is anyone else building this on Stellar", or "map the Stellar landscape for X". Queries the 728-project LumenLoop ecosystem database and Electric Capital developer activity to rank competitors by SCF funding history and repo activity.
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。
Deep competitive intelligence for any market. Analyzes competitors' products, pricing, customer sentiment, GTM strategy, and growth signals using real web data. Produces battle cards, pricing landscape, and feature matrix. Use when the user wants to understand their competitive landscape, analyze competitors, compare products in a market, or research who they're competing against. Triggers for "who are my competitors", "competitive analysis", "competitor research", "battle cards", "pricing comparison", "competitor pricing", "market players", "competitive intelligence", "competitive landscape", "who else is in this space", "competitive moat", or any request to profile, compare, or map competitors in a category. Works standalone — no prior startup-design session 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。
Expert landscape designer transforming yards through photo mapping, 3D visualization, seasonal planning, and deep plant knowledge. Specializes in fast-growing privacy screens (knows arborvitae pitfalls), architecture-appropriate design, outdoor living spaces, and realistic maintenance expectations. Activate on "landscape design", "yard design", "garden planning", "plant selection", "privacy screen", "outdoor living", "backyard makeover", "arborvitae", "hedge", "fast growing tree", "landscaping ideas". NOT for interior design (use interior-design-expert), hardscape construction (consult contractors), or lawn care chemicals (consult local experts).
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。
自社の強みや顧客像を基に、比較すべき競合と調査先を選びます。直接競合・周辺競合・目標となる企業に分類し、根拠付きの候補一覧を作って詳しい比較の準備を整えます。
採点済みの競合情報を、市場マップや比較表、差別化の余地、優先施策を含むレポートにまとめます。経営層への報告や、チームで競争方針を決める場面に役立ちます。
Create comprehensive industry and sector landscape reports covering market dynamics, competitive positioning, key players, and thematic trends. Use for client requests, sector initiations, thematic research pieces, or internal knowledge building. Triggers on "sector overview", "industry report", "market landscape", "sector analysis", "industry deep dive", or "thematic research".
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。
Framework for building competitive landscape decks — market positioning, competitor deep-dives, comparative analysis, strategic synthesis. Use when the user asks for a competitive landscape, competitor analysis, peer comparison, market positioning assessment, strategic review, or investment memo deck. Also triggers on "who are the competitors to X", "benchmark X against peers", "build a market map", or any request to systematically evaluate competitive dynamics across an industry.
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。
Build a live Three.js landscape that stays quiet behind a subject — a noise heightfield on a polar grid so resolution follows the lens, ground coloured by slope and moisture rather than by texture, instanced GPU grass whose wind costs nothing on the CPU, scattered stones, a gradient sky dome, a star field you can actually see, and a time-of-day system that cross-fades instead of cutting. Use for hero backdrops, product stages, scroll worlds, or any scene where a building, object, or figure has to sit in a place rather than float on a gradient.
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。
Scans the biomarker landscape of a disease area by biomarker type, clinical/research use case, evidence layer, validation status, and maturity level. Use this skill when a user wants a field-level biomarker evidence map rather than a generic literature summary. Always separate exploratory biomarkers from externally validated or clinically embedded biomarkers, and never imply clinical maturity without explicit evidence support.
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。
Organizes the evidence and competitive landscape around a drug, target, or pathway by separating disease relevance, tractability, preclinical evidence, clinical evidence, modality fit, and crowding. Always map what is biologically supported, what is druggable, what has actually advanced, and what remains strategically open. Never confuse target relevance with druggability, preclinical activity with clinical promise, or narrative excitement with validated development maturity. Never fabricate references, trial status, approval status, company activity, or asset metadata.
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。