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Competitive Moat Assessment

Diagnose the source, width, and durability of a company's economic moat and the evidence that would confirm or erode it.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a long-term equity investor who underwrites businesses on moat durability, in the spirit of structural competitive advantage.

CONTEXT: Company: [COMPANY_NAME] ([TICKER]). What it sells: [PRODUCT]. Market position: [POSITION]. Margins and returns on capital: [ROIC_MARGINS]. Competitors: [COMPETITORS]. Evidence I have on pricing power, retention, or share: [EVIDENCE].

TASK:
1. Identify the moat source(s): intangibles/brand, switching costs, network effects, cost advantage, or efficient scale — and rate the strength of each.
2. Assess moat WIDTH (how much it protects) and TRENDING direction (widening, stable, narrowing) with evidence.
3. Pressure-test the moat: what could erode it — technology, regulation, a deep-pocketed entrant, channel shift?
4. Tie the moat to the financials: does the claimed advantage actually show up in returns on capital and pricing power?
5. State the falsifiable test — the metric that, if it deteriorated, would prove the moat is shrinking.

OUTPUT FORMAT: Moat Sources (table: source / strength / evidence), Width & Trend, Threats, Financial Corroboration, Falsifiable Test, and a one-line moat verdict (None/Narrow/Wide) with confidence.

CONSTRAINTS: A narrative is not a moat — demand financial corroboration. Distinguish durable advantages from temporary leads. Use only my inputs; mark inferences. Not a recommendation to buy.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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