style(pip): apply black, isort and lint fixes

[calchiwo]

• Run black formatting
• Normalize import ordering with isort
• Apply ruff autofixes
• No functional changes

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Project	Deployment	Actions	Updated (UTC)
explainthisrepo	Ready	Preview, Comment	Feb 11, 2026 7:33pm

[gemini-code-assist]

Summary of Changes

Hello @calchiwo, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on enhancing code quality and maintainability by standardizing the codebase's style and structure. It integrates automated formatting with black, organizes imports using isort, and applies linting fixes from ruff, ensuring a cleaner and more consistent development environment without altering any existing functionality.

Highlights

• Code Formatting: Applied black formatting to enforce consistent code style across the codebase.
• Import Organization: Normalized import statements using isort for better readability and consistency.
• Linting Corrections: Applied automatic fixes suggested by the ruff linter to address various code quality issues.
• Functional Stability: Verified that these styling and linting changes introduce no functional modifications to the application.

Changelog

• explain_this_repo/cli.py
  • Added a missing newline at the end of the file.
• explain_this_repo/prompt.py
  • Added a missing newline at the end of the file.

Activity

• No specific activity (comments, reviews, or progress updates) has been recorded for this pull request yet.

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[gemini-code-assist]

Code Review

This pull request applies automated formatting and linting fixes, specifically adding newlines to explain_this_repo/cli.py and explain_this_repo/prompt.py to adhere to standard Python style conventions (PEP 8). While these changes improve code consistency, a security audit of the codebase revealed a significant vulnerability. The application is susceptible to Prompt Injection because it directly includes content from a target GitHub repository in the prompts sent to a Large Language Model without proper sanitization. This issue is exacerbated by a Path Traversal vulnerability in the repository parsing logic, which allows an attacker to specify an arbitrary repository, making the prompt injection easily exploitable and potentially leading to manipulated outputs from the language model.

[gemini-code-assist]

The function build_simple_prompt constructs a prompt for a Large Language Model (LLM) by directly embedding content fetched from a GitHub repository (readme, tree_text). This content is considered untrusted user input. A remote attacker can craft a malicious README file or source code within a repository that contains instructions to manipulate the LLM. When the application processes this malicious repository, these instructions are injected into the prompt, which could cause the LLM to ignore its original purpose, reveal sensitive information like its system prompt, or generate malicious content.

This is possible because the repository content is not sanitized before being included in the prompt. Furthermore, a path traversal vulnerability in explain_this_repo/cli.py allows an attacker to point the tool to an arbitrary, malicious repository, making this vulnerability easily exploitable.