askrepo

v1.3.3 suspicious
6.0
Medium Risk

Codebase Intelligence AI — chat with any codebase using semantic search and LLMs

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential shell command execution and code obfuscation, though there is no evidence of credential harvesting or severe network communication issues.

  • Shell risk due to potential uncontrolled execution of shell commands.
  • High obfuscation risk from the use of exec with compiled code.
Per-check LLM notes
  • Network: The network calls suggest the package may be communicating with an external service, which could be benign if it's part of its intended functionality.
  • Shell: Executing shell commands can pose risks if not properly sanitized or controlled, especially if they involve modifying system state or interacting with sensitive files.
  • Obfuscation: The use of exec with compiled code from a file suggests potential for code injection and obfuscation.
  • Credentials: No clear signs of credential harvesting observed.
  • Metadata: The maintainer has only one package, indicating a potentially new or less active account.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (17116 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 66 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 11 commits in aswin-2005/AskRepo
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • .encode("utf-8") req = urllib.request.Request( f"{config.OLLAMA_BASE_URL}/api/generate",
  • ) try: with urllib.request.urlopen(req, timeout=120) as resp: body = json.
  • }).encode("utf-8") req = urllib.request.Request( f"{config.OLLAMA_BASE_URL}/api/generate",
  • thod="POST", ) with urllib.request.urlopen(req, timeout=120) as resp: body = json.load
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ding="utf-8") as _f: exec(compile(_f.read(), str(user_cfg), "exec"), _ns) # noqa: S102
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • hanges...") result = subprocess.run( ["git", "-C", clone_dir, "pull", "--depth=1"],
  • lone_dir] result = subprocess.run(cmd, capture_output=True, text=True) if result.retu
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aswin-2005/AskRepo appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Aswin P Nair" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with askrepo 
Develop a mini-application called 'CodeQuery' that leverages the 'askrepo' package to enable users to interact with any codebase via natural language queries. This application will use semantic search and large language models (LLMs) to understand and respond to user questions about the codebase. Here’s a detailed breakdown of the steps and features:

1. **Setup**: Install the 'askrepo' package and set up a local or remote codebase that the application will query. Ensure the codebase is indexed properly so that 'askrepo' can perform efficient searches.
2. **User Interface**: Design a simple and intuitive user interface where users can input their queries. This could be a command-line interface (CLI) or a web-based interface depending on the complexity and target audience of your application.
3. **Query Processing**: Utilize the 'askrepo' package to process user queries. The application should be able to interpret natural language questions and convert them into search queries that 'askrepo' can handle.
4. **Response Generation**: Once 'askrepo' has processed the search queries, it will return relevant snippets from the codebase along with contextual information. Your application should then format these results into a readable and understandable response for the user.
5. **Feature Suggestions**:
   - **History Feature**: Keep a history of previous queries and responses to allow users to review past interactions.
   - **Favorites**: Allow users to mark certain code snippets or responses as favorites for quick access later.
   - **Integration with Version Control Systems**: Enable users to directly link to specific versions of the codebase if they are querying a version-controlled repository.
6. **Testing and Validation**: Test the application with various types of queries to ensure it handles different scenarios effectively. Validate the accuracy of responses by comparing them against manual searches within the codebase.
7. **Documentation**: Provide comprehensive documentation on how to install, configure, and use the application. Include examples of common queries and expected responses.
8. **Deployment**: If the application has a web interface, deploy it to a server or cloud platform like Heroku or AWS. For CLI applications, provide installation scripts and setup instructions.

This project aims to demonstrate the power of integrating AI with codebases to make software development more accessible and efficient.

💬 Discussion Feed

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