sourceprompt

來源推廣

將您的代碼庫轉換為提示，您可以將其輸入LLM。

簡而言之：它在指定的目錄 / git repostiory中掃描所有文本文件，並將其內容（以及他們的路徑）放入結構化的標記文檔中。

默認情況下，它提供了預定義的提示，可以使用-r省略或使用-p選項替換。

安裝

從發行版中下載二進製或使用以下方式安裝：

go install github.com/rostrovsky/sourceprompt@latest

忽略文件 /路徑

• 文件/目錄以開始.
• 二進製文件

標誌

• -i, --include - 應包括的路徑的正則態度模式。
• -e, --exclude - 應排除的路徑的正則態度模式。
• -o, --output - 輸出文件路徑。如果未指定，則將輸出打印到stdout 。
• -p, --prompt prompt-提示文件路徑或URL。允許指定將放置在輸出開始時的自定義提示。如果未指定，將使用默認提示。
• -r, --raw從輸出中刪除提示。
• -v, --verbose啟用時打印調試信息。

例子

輸出到Stdout

 # output to stdout
sourceprompt /path/to/dir
sourceprompt /path/to/dir -v # debug
sourceprompt https://github.com/some/repo

# use remote git repo for codebase and remote prompt
sourceprompt https://github.com/some/repo -p https://raw.githubusercontent.com/another/repo/prompt.md -o out.md

# output to stdout without default prompt
sourceprompt /path/to/dir -r

# output to file
sourceprompt /path/to/dir -o out.md

# output to file with custom prompt
sourceprompt /path/to/dir -o out.md -p my_prompt.txt

# include only src/ files
sourceprompt /path/to/dir -o out.md -i ' ^src '

# include only C source files
sourceprompt /path/to/dir -o out.md -i ' .c$ ' -i ' .h$ '

# exclude markdown files
sourceprompt /path/to/dir -o out.md -e ' .md$ '

# exclude multiple types
sourceprompt /path/to/dir -o out.md -e ' .md$ ' -e ' .txt$ ' -e ' LICENSE ' 

默認提示

 You will be provided with a markdown text (under the "---" separator) containing the contents of a codebase. Each code snippet will be enclosed in code fences, along with the corresponding file name. Your task is to analyze the codebase and gain a comprehensive understanding of its structure, functionality, and key features.

Please follow these steps:

1. Read through the entire codebase carefully, paying attention to the file names and the code within each code fence.
2. Identify the main components, modules, or classes of the codebase and their responsibilities. Summarize the purpose and functionality of each significant component.
3. Analyze the relationships and dependencies between different parts of the codebase. Identify any important interactions, data flow, or control flow between the components.
4. Extract the most important features and functionalities implemented in the codebase. Highlight any critical algorithms, data structures, or design patterns used.
5. Consider the overall architecture and design of the codebase. Identify any architectural patterns or principles followed, such as MVC, MVVM, or microservices.
6. Evaluate the code quality, readability, and maintainability. Note any areas that could be improved or any potential issues or vulnerabilities.
7. Provide a summary of your analysis, including the key insights, strengths, and weaknesses of the codebase. Offer suggestions for improvements or optimizations, if applicable.
8. Based on your understanding of the codebase, provide guidance on how AI agents can effectively operate across the entire codebase. Identify the entry points, important functions, or APIs that the agents should focus on for interaction and manipulation.
9. Discuss any specific considerations or challenges that AI agents may face when working with this codebase, such as dependencies, external libraries, or platform-specific requirements.
10. Conclude your analysis by providing a high-level overview of the codebase's functionality, architecture, and potential use cases. Highlight any notable features or aspects that make this codebase unique or valuable.

Your analysis should be thorough, insightful, and aimed at enabling AI agents to effectively understand and operate within the given codebase. Provide clear explanations and examples to support your findings and recommendations.

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