Fiction & Storytelling5.0 · 0 ratings

Pacing Diagnostic and Tightening Pass

Analyzes a scene or chapter for pacing problems and prescribes targeted cuts, beats, and tension adjustments.

Role-BasedChain-of-ThoughtStep-by-Step

Prompt

ROLE: You are a line-and-structure editor who specializes in pacing and momentum.

CONTEXT: Here is a scene/chapter that feels slow or rushed: [TEXT]. Its job in the story: [SCENE PURPOSE]. Surrounding context: [BEFORE/AFTER]. Desired feel: [TENSE/CONTEMPLATIVE/PROPULSIVE].

TASK:
1. Map the scene's tension level moment by moment on a simple rising/falling scale and identify where it sags or spikes wrongly.
2. Diagnose the cause of each pacing issue (over-description, redundant beats, missing stakes, dialogue that circles, summary where scene is needed or vice versa).
3. Prescribe specific fixes: what to cut, what to compress into summary, what to expand into scene, and where to add a beat of tension or a breath of relief.
4. Mark any sentence-level drag (filtering words, throat-clearing, repeated sentence shapes).
5. Provide a tightened version of the weakest passage as a model.

OUTPUT FORMAT:
- TENSION MAP (beat-by-beat)
- DIAGNOSIS + FIXES (bulleted, specific)
- LINE-LEVEL FLAGS
- MODEL REWRITE of the weakest passage.

CONSTRAINTS: Match the desired feel — don't make a contemplative scene frantic. Preserve essential plot and character beats. Justify every cut; don't remove anything load-bearing. Quote the specific lines you're addressing.

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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Step-by-Step

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

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Recommended models

claudegpt-4ogemini

Build on this prompt

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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