Promptr

Promptr is a CLI tool that lets you use plain English to instruct OpenAI LLM models to make changes to your codebase.

Usage

promptr [options] -p "your instructions" <file1> <file2> <file3> ...

Examples

Cleanup the code in a file

$ promptr -p "Cleanup the code in src/index.js"

Promptr recognizes that the file src/index.js is referenced in the prompt, so the content of src/index.js is sent to the model along with the user's prompt.
The model's response is automatically applied to the relevant files.

Alphabetize the methods in all of the javascript files

$ promptr -p "Alphabetize the method names in all of these files" $(git ls-tree -r --name-only HEAD | grep ".js" | tr '\n' ' ')

The command above uses git-tree, grep, and tr to pass a list of javascript file paths to promptr.

The PR's below are good examples of what can be accomplished using Promptr. You can find links to the individual commits and the prompts that created them in the PR descriptions.

• #38
• #41
  
  

I've found this to be a good workflow:

• Commit any changes, so you have a clean working area.
• Author your prompt in a file. The prompt should be specific clear instructions.
• Make sure your prompt contains the relative paths of any files that are relevant to your instructions.
• Use Promptr to execute your prompt. Provide the path to your prompt file using the -p option: promptr -p my_prompt.txt

Promptr applies the model's response to your files. Use your favorite git UI to inspect the results.

Templating

Promptr supports templating using liquidjs, which allows users to incorporate templating commands within their prompt files. This feature enhances the flexibility and reusability of prompts, especially when working on larger projects with repetitive patterns or standards.

Using Includes

Projects can have one or more "includes"—reusable snippets of code or instructions—that can be included from a prompt file. These includes may contain project-specific standards, instructions, or code patterns, enabling users to maintain consistency across their codebase.

For example, you might have an include file named _poject.liquid with the following content:

This project uses Node version 18.
Use yarn for dependency management.
Use import not require in Javascript.
Don't include `module.exports` at the bottom of Javascript classes.
Alphabetize method names and variable declarations.

In your prompt file, you can use the render function from liquidjs to include this include file in a prompt file that you're working with:

{% render '_project.liquid' %}
// your prompt here

This approach allows for the development of reusable include files that can be shared across multiple projects or within different parts of the same project.

Example Use Cases

• Project-Wide Coding Standards: Create an include file with comments outlining coding standards, and include it in every new code file for the project.

• Boilerplate Code: Develop a set of boilerplate code snippets for different parts of the application (e.g., model definitions, API endpoints) and include them as needed.

• Shared Instructions: Maintain a set of instructions or guidelines for specific tasks (e.g., how to document functions) and include them in relevant prompt files.

By leveraging the templating feature, prompt engineers can significantly reduce redundancy and ensure consistency in prompt creation, leading to more efficient and standardized modifications to the codebase.

Options

Option	Description
-p, --prompt <prompt>	Specifies the prompt to use in non-interactive mode. A path or a url can also be specified - in this case the content at the specified path or url is used as the prompt. The prompt can leverage the liquidjs templating system.
-m, --model <model>	Optional flag to set the model, defaults to gpt-4-turbo-preview. Using the value "gpt3" will use the gpt-3.5-turbo model.
-d, --dry-run	Optional boolean flag that can be used to run the tool in dry-run mode where only the prompt that will be sent to the model is displayed. No changes are made to your filesystem when this option is used.
-i, --interactive	Optional boolean flag that enables interactive mode where the user can provide input interactively. If this flag is not set, the tool runs in non-interactive mode.
`-t, --template <templateName	templatePath
-x	Optional boolean flag. Promptr parses the model's response and applies the resulting operations to your file system when using the default template. You only need to pass the -x flag if you've created your own template, and you want Promptr to parse and apply the output in the same way that the built in "refactor" template output is parsed and applied to your file system.
-o, --output-path <outputPath>	Optional string flag that specifies the path to the output file. If this flag is not set, the output will be printed to stdout.
-v, --verbose	Optional boolean flag that enables verbose output, providing more detailed information during execution.
-dac, --disable-auto-context	Prevents files referenced in the prompt from being automatically included in the context sent to the model.
--version	Display the version and exit

Additional parameters can specify the paths to files that will be included as context in the prompt. The parameters should be separated by a space.

## Requirements - Node 18 - [API key from OpenAI](https://beta.openai.com/account/api-keys) - [Billing setup in OpenAI](https://platform.openai.com/account/billing/overview)

Installation

With yarn

yarn global add @ifnotnowwhen/promptr

With npm

npm install -g @ifnotnowwhen/promptr

With the release binaries

You can install Promptr by copying the binary for the current release to your path. Only MacOS is supported right now.

Set OpenAI API Key

An environment variable called OPENAI_API_KEY is expected to contain your OpenAI API secret key.

Build Binaries using PKG

npm run bundle

npm run build:<platform win|macos|linux>

npm run test-binary

License

Promptr is released under the MIT License.