auto-coder

v3.0.56 safe
3.0
Low Risk

AutoCoder: AutoCoder

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories, with no clear indications of malicious activities. The combination of base64 decoding and use of gh CLI are not unusual and do not suggest a supply-chain attack.

  • No network calls detected
  • No suspicious credential handling
  • Single package from maintainer
Per-check LLM notes
  • Network: No network calls were detected, reducing risk.
  • Shell: Detection of shell execution and gh CLI usage suggests interaction with GitHub but does not necessarily indicate malicious intent.
  • Obfuscation: Base64 decoding is commonly used for data serialization and not necessarily indicative of malicious activity.
  • Credentials: No suspicious patterns indicating credential harvesting were found.
  • Metadata: The maintainer has only one package, indicating potential new or less active status which warrants caution but does not strongly suggest malicious intent.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_base_agent.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4460 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

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

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in allwefantasy/auto-coder
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ) image_data = base64.b64decode(response[0].output) with open("output.jpg", "wb") as
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • process process = subprocess.Popen( command, shell=True,
  • 安装了 gh CLI gh_check = subprocess.run(["gh", "--version"], capture_output=True, text=True)
  • 录 GitHub auth_check = subprocess.run( ["gh", "auth", "status"], capture_output=True,
  • ] pr_result = subprocess.run(pr_cmd, capture_output=True, text=True) if pr_resul
  • ng_dir self.process = subprocess.Popen([self.shell], stdin=subprocess.PIPE, cwd=working_dir,
  • command, shell=True, cwd=self.env.cwd, stdout=s
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository allwefantasy/auto-coder appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "allwefantasy" 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 auto-coder 
Create a fully functional code generation assistant named 'CodeGenie' using the Python package 'auto-coder'. This tool aims to simplify the process of generating boilerplate code for common programming tasks, thereby increasing developer productivity. The application should have a user-friendly command-line interface (CLI) and support multiple programming languages including Python, JavaScript, and Java.

### Features:
1. **Language Support:** Users should be able to specify the target programming language for the generated code snippets.
2. **Common Code Snippets:** Provide a list of common code snippets such as HTTP requests, file operations, database connections, etc., from which users can choose.
3. **Customization Options:** Allow users to customize the generated code by providing specific parameters (e.g., file paths, URLs, database names).
4. **Interactive Mode:** Implement an interactive mode where users can input their requirements and receive code suggestions dynamically.
5. **Documentation Generation:** Automatically generate comments and documentation strings for the generated code to improve readability and maintainability.
6. **Integration with Version Control Systems:** Enable the option to automatically commit the generated code to a version control system like Git.

### Utilizing 'auto-coder':
- Use 'auto-coder' to handle the core logic of generating code snippets based on user inputs. This includes parsing user commands, fetching relevant templates from a predefined set, and customizing these templates according to user specifications.
- Leverage 'auto-coder's advanced features to optimize the generated code for performance and readability.
- Integrate 'auto-coder' with the CLI to ensure seamless interaction between the user and the code generation process.

### Steps to Build the Application:
1. Set up a virtual environment and install necessary dependencies including 'auto-coder'.
2. Design and implement the CLI interface using Python's argparse module or similar tools.
3. Develop the backend logic using 'auto-coder', focusing on handling different types of user inputs and generating corresponding code snippets.
4. Implement customization options allowing users to modify the generated code to fit their specific needs.
5. Add an interactive mode that allows users to input requirements dynamically and receive real-time code suggestions.
6. Integrate documentation generation capabilities into the code generation process.
7. Implement integration with version control systems to allow automatic commits of generated code.
8. Test the application thoroughly to ensure all features work as expected and provide a smooth user experience.
9. Document the setup and usage instructions for end-users.
10. Deploy the application and make it available for download/installation via platforms like PyPI.

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