「moat」の検索結果

Warren Buffett said he looks for "economic castles protected by unbreachable moats." A moat is a sustainable competitive advantage that protects your business from competitors. Without a moat, success attracts competition that erodes your margins to zero. This skill covers identifying, building, and deepening moats. Network effects, switching costs, brand, scale economies, and the rarer moats like regulatory capture and counter-positioning. Use when "moat, defensibility, competitive advantage, network effects, switching costs, barrier to entry, unfair advantage, protect from competition, sustainable advantage, winner take all, flywheel, lock-in, moat, defensibility, strategy, network-effects, switching-costs, competitive-advantage, seven-powers" mentioned.

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Assess competitor defensibility and define our own moat strategy during PRD v0.3 Commercial Model. Triggers on requests to analyze competitor moats, define our defensibility, assess switching costs, identify vulnerabilities, find wedge opportunities, or when user asks "what's our moat?", "how defensible are they?", "where can we compete?", "switching costs?", "defensibility", "who to target". Consumes Competitive Landscape (v0.2) CFD- entries. Outputs CFD- entries for competitor moats and BR- entries for targeting rules and our defensibility strategy.

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Estimate pages needed for topical authority. Go/no-go decision before investing months in content. Triggers on: "how much content do I need", "topical authority estimate", "content moat", "how many articles", "content gap analysis", "can I compete in this niche", "content investment calculator", "is this niche worth the effort", "SEO feasibility", "how many pages to rank", "content volume needed", "competitive content analysis", "moat calculation", "authority gap", "should I invest in this niche".

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Maps a startup's competitive landscape, scores moat strength across 6+ dimensions, and writes an investor-ready competition narrative with a positioning map. Use when the user asks for a competitive landscape, moat analysis, or positioning review and gives enough to work from: a deck, a list of competitors, or a clearly named startup. A full run takes about 30–40 minutes and searches the web, so for a passing question such as 'who are my competitors?' or 'is that a real moat?', answer briefly in chat and offer the analysis instead of starting it. When it runs, the competitor set and moat scores are checked adversarially, never recalled from memory.

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Builds a product strategy and stress-tests your moat, then writes a one-page strategy doc (diagnosis, where to play, how to win, guiding policy, coherent actions, moat verdicts). Use for 'we need a product strategy', 'is our moat real', 'do we have a competitive advantage', 'where should we play', 'defensibility', 'should we expand into X', 'our roadmap has no strategy', or 'write a product vision'. Draws on 909 insights from 236 Lenny's Podcast guests incl. Roger Martin, Richard Rumelt and Hamilton Helmer.

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Use when mapping competitors, naming defensibility, and finding white-space — moat reasoning, where-to-play, where-not-to-play. Triggers on 'who are we competing with', 'what's our moat'.

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当用户在纠结职业/副业/自由职业方向、问「我该做什么才能赚钱/不被替代」、或想找到自己的独特优势时调用。 核心理念: 特殊知识(不可培训、对你像玩对别人像工作) × 产品化(杠杆规模化) = 无可替代的致富定位。 不适用于: 纯求职投递、写简历、已有明确方向的执行细节。 Triggers: 找方向/独特优势/副业/不可替代/productize/special knowledge/moat

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Create original surveys, benchmarks, and aggregated data nobody else has. Automate data collection for content moats. Triggers on: "create original data", "proprietary data", "survey design", "benchmark study", "original research", "data-driven content", "create a survey", "industry benchmark", "aggregated data", "unique data", "first-party data", "data moat", "generate research data", "create a study", "original statistics", "data nobody else has", "competitive data advantage".

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Define and prioritize features with strategic traceability during PRD v0.3 Commercial Model. Triggers on requests to define features, prioritize capabilities, scope MVP, map features to pricing tiers, identify parity vs. delta features, or when user asks "what features do we build?", "what's in MVP?", "which features matter?", "feature priority", "parity features", "what's our delta?". Consumes KPI- (Outcome Definition), BR- (Pricing Model, Moat), and CFD- (Market Moat Analysis) from v0.3. Outputs FEA- entries with strategic traceability and BR-FEA- governance rules. Feeds v0.4 User Journeys.

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用户需要评估一个企业的长期竞争力和定价权——它能否在极少额外资本下持续增长利润。 语言信号："这个公司有护城河吗"/"竞争优势在哪里"/"为什么这个公司能长期赚钱"/"品牌值多少钱" 不适用于：纯粹的行业分析（不涉及单个企业竞争力评估时）。

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Value investing analysis using Graham (NCAV/net-net/defensive-investor) and Buffett (economic moat/ROE/FCF) methodologies. Covers single-stock diagnostics and batch screening for both Graham cigar-butt and Buffett quality-compounder criteria. Runs cross-statement reconciliation before scoring. Data from Longbridge CLI first, MCP fallback, WebSearch only for genuine gaps. Triggers: "格雷厄姆", "巴菲特", "捡烟蒂", "烟蒂股", "NCAV", "净流动资产", "护城河", "价值投资", "安全边际", "深度价值", "撿煙蒂", "煙蒂股", "淨流動資產", "護城河", "安全邊際", "Graham", "Buffett", "cigar butt", "net-net", "NCAV screen", "moat", "value investing", "margin of safety", "deep value", "quality compounder", "價值投資", "深度價值", "防御型投资者", "防禦型投資者"

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Evaluates competitive advantage sustainability with switching costs, network effects, data assets, and brand strength analysis. Use when assessing competitive moats, analyzing defensibility, or evaluating long-term positioning.

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。

应用从 AI for Everyone 蒸馏出的「AI战略护城河 — 用AI构建可持续竞争优势」方法论。Use when the user needs practical guidance, diagnosis, workflow design, decision support, or an action plan related to ai-strategy-moat.

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OpenDesign's investor pitch book: market map, moat, unit economics, and the ask — analyst-grade and diligence-ready. Built as a decision-grade fundraising pitch deck for growth-equity investors.

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OpenDesign's demo-day pitch: hook, traction, moat, and the raise — built to make a partner sit up by page three. Built as a decision-grade fundraising pitch deck for accelerator partners, angels.

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Write a publication-grade 8-part deep-dive series on a single company (~120k words total): cognitive reset / moat / profit engine / hidden assets / era variable (e.g. AI) / financials Buffett-style / management / valuation+redlines. The core IP is NOT writing but REVISING — a strict fact-check checklist catches pseudo-precision (probability-weighted expectations, third-party MAU discrepancies, linear extrapolation), absolute language, and cross-article number inconsistencies that most finance long-forms violate. Each piece stands alone but shares one valuation/management/price framework. Use when the user wants textbook-level depth on one company for public publishing (a single research report or earnings note is NOT this — use investment-research / earnings-review instead).

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Scrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.

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Start and scale networked products using Andrew Chen's "The Cold Start Problem" framework for network effects. Use when the user mentions "network effects", "chicken and egg", "cold start", "two-sided marketplace", "atomic network", "hard side", "liquidity", "critical mass", "invite-only launch", "how do I get my first users", or "the marketplace has no buyers or sellers". Also trigger when launching a marketplace, social, or collaboration product that is worthless without other users, deciding launch sequencing and seeding tactics, or diagnosing stalled network growth at scale. Covers the five stages: cold start, tipping point, escape velocity, hitting the ceiling, and the moat. For word-of-mouth virality, see contagious. For habit-driven retention, see hooked-ux.

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Use when evaluating a business through Buffett-style ownership, owner earnings, durable moat, management quality, capital allocation, and margin-of-safety judgment.

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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.

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Review a business plan or product strategy through the eyes of a smart-money seed/Series-A investor. Asks the brutal questions: Is this fundable? What's the moat? What's the exit story? Outputs a verdict with one of four modes — SEED VIABLE / LATER ROUND ONLY / BOOTSTRAP-ONLY / UNFUNDABLE. Use when the user has a business plan and wants to know if it would survive an investor pitch. Triggered by: 'investor review', 'is this fundable', 'VC perspective', '投资人视角'.

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When you want to pressure-test a potential new business, product, or side project against the serial-founder filter. Not "marketing ideas for a product" (that's marketing-skills:marketing-ideas) — this is "should this business exist + can you win it." Runs the idea through a structured framework (problem, audience, wedge, monetization, moat, portfolio fit, distribution, energy fit, opportunity cost), checks domain availability via /domain, optionally triggers /deep-research for market validation, and outputs a viability brief: build / sleep on it / pass. Archives every idea to ~/.config/makerskills/business-brainstorm/archive/ so past work is searchable. Triggers on "/business-brainstorm," "/brainstorm," "new business idea," "should I build X," "pressure test this idea," "validate this idea," "is X a good business," "what about a [type] for [audience]."

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Entry point + orchestrator for the recomby-geo GEO (Generative Engine Optimization) workflow on OpenAI Codex CLI. Use when the user wants to run any stage of the GEO pipeline on a client folder — intake, visibility audit, content-gap analysis, content brief, draft production, distribution, or monthly re-audit — or asks to "run GEO", "audit AI search visibility", or "GEO this client". Codex has no bare slash commands, so this skill is how the 7 stages (that Claude Code runs as /01-intake … /07-reaudit) are driven on Codex. It routes to the per-stage specs in this plugin's commands/ and enforces the orchestration rules. Does not auto-fill expert content — the human-in-loop brief checkpoint is the moat.

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Decide which AI features to build, delay, shelve or kill, and whether each one gets better as models improve. Produces an AI Bet Memo (feature-by-feature verdicts, retest triggers, scaffolding ledger, moat and safety check) from a roadmap or idea list. Use for 'AI strategy', 'AI roadmap', 'what should we build with AI', 'will the next model make this obsolete', 'build vs wait for the model', 'will OpenAI/Anthropic build this', 'agent or chatbot', 'is this feature worth shipping'. Draws on 118 Lenny's Podcast guests incl. Nick Turley, Kevin Weil and Dianne Penn.

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。