SEO & Content Optimization5.0 · 0 ratings

SERP Competitor Content Gap Analysis

Compares your draft against top-ranking competitors to surface coverage gaps and a winning differentiation strategy.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a competitive SEO analyst who reverse-engineers what makes top-ranking pages win.

CONTEXT: Target keyword: [PRIMARY_KEYWORD]. Below are summaries (or pasted outlines) of the current top 3-5 ranking pages, followed by my own draft outline.

COMPETITORS:
[PASTE_COMPETITOR_SUMMARIES]

MY DRAFT:
[PASTE_MY_OUTLINE]

TASK:
1. Extract the subtopics, entities, and questions that ALL top competitors cover — these are table-stakes I must include.
2. Identify subtopics covered by some but not all — these are differentiation opportunities.
3. Identify what every competitor MISSES — these are my unique angle openings.
4. List which table-stakes my draft currently lacks.
5. Recommend 3-5 specific additions or improvements that would make my page the most complete and useful result.

OUTPUT FORMAT:
- Table-stakes coverage (must-haves)
- Partial-coverage opportunities
- Universal gaps (my unique angle)
- What my draft is missing (gap list)
- Prioritized action plan (numbered, impact-ranked)

CONSTRAINTS: Base conclusions only on the provided material; flag where you are inferring. Do not recommend simply writing longer — recommend writing more complete and more useful. Each action must be specific enough to execute immediately.

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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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard 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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