result to prompt rebuilder
by nowrich
Reverse-engineer reusable prompt templates from example outputs and their source inputs.
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Works with the AI tools you already use
See it in action
You say
Here is an AI-generated product description for 'EverGrip Boots' using inputs {product: EverGrip Boots, material: Vulcanized Rubber}. Rebuild the prompt used to make this.
Your agent does
Reconstructed Prompt
You are a product copywriter. Write a concise description.
INPUTS product: {{product}} material: {{material}}
[Observed] Use a bold header for the product name. [Inferred] Focus on durability benefits of the material.
Test Plan
- Swap material to 'Breathable Mesh'.
What you get
About this skill
The problem
Reverse-engineering a prompt from a successful output is tedious and prone to error. Manually identifying which parts of a result are static instructions versus dynamic variables often leads to "leaky" templates that fail on new inputs.
What it does
- Analyzes a single example output and its source input values to map data flow.
- Separates variable-driven content from fixed structural instructions and formatting constraints.
- Reconstructs a clean, imperative prompt template using double-curly-brace placeholders.
- Generates a PromptBase-style INPUTS block for standardized execution.
- Produces an inference ledger that flags every rule as observed or speculative for targeted testing.
Why this beats prompting it yourself
This skill moves beyond guessing. It uses a structured evidence map to distinguish between coincidence and intent, preventing you from hard-coding incidental details that break your prompt's versatility. It provides a formal test plan to validate the reconstruction across edge cases immediately.
Use cases
- Turn a one-off successful AI response into a reusable tool or a sellable PromptBase product.
- Recover lost prompt logic when you only have the historical logs of inputs and outputs.
- Standardize internal prompt libraries by converting varied examples into a consistent template format.
- Debug why a prompt works by isolating the specific rules that drive the successful output.
Known limitations
Requires both the complete output and the exact input values that generated it. It reconstructs behavior, not the original author's exact wording.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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