code2prompt

Generate a prompt for the current project and copy it to the clipboard (default behaviour)

$ code2prompt [path/to/project]

$ code2prompt [path/to/project] [[-i|--include]] "[**/*.rs]" [[-e|--exclude]] "[tests/**]"

$ code2prompt [path/to/project] [[-O|--output-file]] [my_prompt.txt]

$ code2prompt [path/to/project] [[-F|--output-format]] json

$ code2prompt [path/to/project] [[-t|--template]] [my_template.hbs]

code2prompt [OPTIONS] [FILE...]
or cat FILE | code2prompt [OPTIONS]

FILE...
    Path to one or more source code files. If omitted or - is specified, reads from standard input.

-l LANGUAGE, --language=LANGUAGE
    Specifies the programming language of the input code (e.g., python, java, javascript). This helps in proper markdown formatting and language-specific context.

-c CONTEXT_FILE, --context=CONTEXT_FILE
    Reads additional descriptive context from CONTEXT_FILE and prepends it to the generated prompt.

-i INSTRUCTIONS_FILE, --instructions=INSTRUCTIONS_FILE
    Reads specific instructions for the AI from INSTRUCTIONS_FILE and appends them to the prompt.

-s SECTION_FORMAT, --section=SECTION_FORMAT
    Defines how the code block should be wrapped. Common formats include markdown (default: ```language\ncode\n```) or plain.

-o OUTPUT_FILE, --output=OUTPUT_FILE
    Writes the generated prompt to OUTPUT_FILE instead of standard output.

--no-header
    Suppresses the default introductory header (e.g., 'Here's the code:') that typically precedes the code block.

--line-numbers
    Includes line numbers alongside the code in the output prompt, useful for specific AI tasks.

-h, --help
    Displays a help message and exits.

-v, --version
    Displays version information and exits.

The code2prompt command is a conceptual utility designed to streamline the process of preparing source code for large language models (LLMs) or other AI systems. Its primary function is to ingest a code file or direct code input and format it into a structured, readable prompt. This often involves adding contextual information, specific instructions for the AI, and wrapping the code within designated markers (e.g., markdown code blocks) to ensure optimal interpretation by the model. The command aims to save developers time by automating the often tedious task of manually copying, pasting, and annotating code snippets for AI interaction, making it easier to leverage AI for tasks like code explanation, refactoring, debugging, or generating documentation.

It is important to note that code2prompt is presented here as a conceptual Linux command. As of current public knowledge, it is not a standard utility found in typical Linux distributions or common package repositories. The analysis above is based on the probable design and functionality one would expect from a tool named 'code to prompt' given the rise of large language models. Actual implementations might vary significantly in options, behavior, and availability. Users looking for similar functionality would typically use custom scripts, AI client libraries, or integrate with existing IDE extensions.

A common use case involves piping a source file into code2prompt while specifying the language and adding instructions from another file.

Example:
cat my_script.py | code2prompt -l python -i ai_instructions.txt > prompt.txt

This creates a prompt.txt file ready for an AI, containing the Python code along with the specified instructions.

While code2prompt itself is a conceptual command, its underlying need stems from the rapid evolution of large language models (LLMs) and their increasing integration into software development workflows since the early 2020s. Developers frequently need to present code snippets to AI for analysis, generation, or transformation. Early approaches involved manual copy-pasting and formatting. The idea of an automated tool like code2prompt represents a natural progression towards making AI interaction more seamless and efficient, evolving from simple scripts that add markdown fences to more sophisticated utilities that manage context, instructions, and various output formats for diverse AI models.

cat(1), echo(1), sed(1), awk(1)