arcboost

v0.1.0 suspicious
4.0
Medium Risk

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🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network calls, shell executions, obfuscation, and credential handling. However, the metadata risk score suggests potential anonymity or low development effort, warranting further investigation.

  • Low metadata quality
  • Potential developer anonymity
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands, which is typical and safe.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting secure handling of sensitive information.
  • Metadata: The package shows signs of low effort and potential anonymity, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (1.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 8.0

4 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with arcboost 
Create a text analysis tool named 'TextAnalyzer' using the Python package 'arcboost'. This tool will allow users to input any text and perform various analyses on it, such as sentiment analysis, keyword extraction, and topic modeling. Additionally, the tool will provide visualizations of the results for better understanding.

Step 1: Setup the Project
- Initialize a new Python project and install necessary packages including 'arcboost', 'pandas', and 'matplotlib'.

Step 2: Develop the Core Functionality
- Implement a function to load user-provided text data into the program.
- Use 'arcboost' to perform sentiment analysis on the loaded text, identifying positive, negative, and neutral sentiments.
- Utilize 'arcboost' to extract keywords from the text, highlighting the most significant words or phrases.
- Apply 'arcboost' to conduct topic modeling on the text, summarizing the main topics discussed.

Step 3: Visualize the Results
- Create graphs and charts using 'matplotlib' to display the sentiment distribution, top keywords, and topic models.

Step 4: User Interface
- Design a simple command-line interface (CLI) for interacting with the TextAnalyzer. Users should be able to enter text directly or specify a file path.
- Provide options to choose which types of analysis to run (e.g., sentiment only, keyword extraction only, or all).

Step 5: Documentation
- Write clear documentation explaining how to install the package, how to use the CLI, and what each feature does.

Suggested Features:
- Allow users to save the analysis results to a file.
- Integrate with web scraping tools to automatically analyze content from URLs.
- Offer a web-based interface in addition to the CLI.

💬 Discussion Feed

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