The SCRIPT framework: how to structure reusable prompts that perform

If you’re here, it most likely means you have already identified a few tasks that could benefit from reusable prompts.

Maybe they are tasks you repeat often, tasks that require a consistent structure or tone, or tasks that simply take too much mental energy every time you start from scratch.

In the first article of this series, we looked at how to spot those prompt-worthy tasks through 5 signals: consistency, repetition, time horizon, mental load, and structure.

Now comes the next step: turning those tasks into prompts you can actually reuse.

This is also where many teams and professionals get stuck. They identify the task, open their AI chatbot, write a slightly better version of their usual prompt, and still end up in the same messy loop of corrections and rewrites.

If you want to build a prompt library for smarter workflows, you need prompts that work beyond the moment in which you write them, you need a prompting frawork to apply consistently.

That is where the SCRIPT prompting framework comes in.

Why reusable prompts need structure

A reusable prompt needs to work beyond the moment in which you write it.

It should still make sense next week, next month, or the next time the task comes up. It should reduce the need to reconstruct your thinking from scratch. It should make the output more predictable.

Without structure, the prompt depends too much on what you remember to include in the moment.

With structure, the process is externalized.

This is especially useful for tasks that require consistency, repetition, time horizon, mental load, or clear phases. In other words, the same signals that make a task prompt-worthy are the same reasons why the final prompt needs a system behind it.

SCRIPT is that system.

What the SCRIPT framework stands for

SCRIPT is a prompting structure built around six elements:

• Situation
• Character
• Request
• Instructions
• Proof
• Template

Each part gives the model a different piece of the task. Together, they create a prompt that can be saved, reused, improved, and stored inside a prompt library.

Situation

Situation is the context. This is where you explain what is happening, what the goal is, and where the output will be used.

Without context, the model is guessing. It does not know whether the output is for a blog post, a client report, a YouTube description, a landing page, an internal workflow, or a LinkedIn post.

Situation tells the model what environment it is operating in.

Character

Character is the role you assign to the AI. This anchors the type of expertise the model should apply.

Depending on the task, the AI might need to act as a senior SEO strategist, YouTube scriptwriter, technical editor, data analyst, B2B copywriter, or content strategist.

Request

Request is the core task. This is where you define exactly what the AI should produce.

It should be explicit and unambiguous. Do you want an outline, a full draft, a summary, a table, a script, a list of ideas, or an analysis?

Instructions

Instructions define how the task should be executed. This is where you include tone, constraints, formatting rules, length, steps, style preferences, and things to avoid.

Instructions remove ambiguity.

Proof

Proof is where examples come in. This can include sample outputs, reference structures, good and bad examples, tone references, or previous content you want the model to learn from.

Examples improve output quality because they give the model a pattern to follow.

Template

Template defines the final output structure. Instead of hoping the model formats the answer correctly, you specify the structure upfront.

This final step is what makes the prompt easier to reuse, especially if you want consistent outputs across multiple tasks, projects, or team members.

A simple SCRIPT example

Here is a short version of the framework in action:

S - Situation
You are creating a short YouTube video explaining what prompting is and why it matters when using AI chatbots.

C - Character
Act as an AI educator who explains complex concepts simply and engagingly.

R - Request
Write a short script for this video that explains what prompting is and why better prompts produce better AI outputs.

I - Instructions
• Keep it beginner-friendly  
• Use clear language  
• Include a good vs bad prompt example

P - Proof
Structure the script as:
Hook → Explanation → Example → Takeaway
Here’s an example you can look at: ...

T - Template
Give me the script here in the chat.

Remember that an actual reusable prompt needs to be 2 times more specific (at least) if you really want it to perform well, but this example is already much stronger than “write a script for a YouTube video about prompting”.

Bonus tip: add SCRIPT to your custom instructions

Once you have a prompting framework that works for you, add it to the custom instructions of your AI chatbot, just like this:

When asked to create a prompt for reusability, follow this prompting framework. 

The SCRIPT framework:
S = Situation
The Situation provides the necessary context and background information to ground the model in a specific scenario. 
C = Character
Character involves assigning a specific role, persona, or identity to the AI. 
R = Request
The Request is the functional core of the prompt, explicitly stating the task or objective the AI must perform. 
I = Instructions
Instructions break down the request into logical, sequential steps or procedures for the AI to follow. 
P = Proof
Proof utilizes "few-shot" prompting by providing examples, references, or benchmarks to guide the AI’s response style and structure. 
T = Template
The Template specifies the output architecture and desired format of the final response. 

This way, every time you ask the chatbot to help you create a reusable prompt, it already knows the structure you want it to follow.

Instead of explaining the framework from scratch every time, you can simply say:

Turn this task into a reusable prompt using my SCRIPT framework.

The chatbot will then structure the prompt around Situation, Character, Request, Instructions, Proof, and Template automatically.

This is a small setup step, but it reduces friction every time you want to capture a new prompt-worthy task and add it to your prompt library.

From SCRIPT to your prompt library

As you can see, SCRIPT is a way to turn prompt-worthy tasks into reusable assets.

Every time you find a task that is repetitive, structured, mentally heavy, or likely to return over time, you can transform it into a performative prompt using this framework. Then you can store it in your prompt library and improve it as you use it.

That is the second step in moving from random prompting to smarter workflows.

If you want to check out the full process together checkout this article that covers How to build a prompt library for smarter workflows.