aiograpi-rest

v6.0.0 suspicious
5.0
Medium Risk

RESTful API service for aiograpi

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to shell operations and incomplete metadata, raising concerns about its integrity and transparency.

  • Shell risk is moderately high at 6/10
  • Lack of maintainer information and basic metadata
Per-check LLM notes
  • Network: Network calls appear to be related to fetching content and uploading data, which may be normal depending on the package's intended functionality.
  • Shell: Shell executions include git operations and script execution, which could indicate benign maintenance activities but also raise concerns about potential unauthorized actions or data leakage.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low signs of malicious intent but lacks basic metadata and maintainer information.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 17 test file(s) found

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

Some documentation present

  • Detailed PyPI description (20250 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

  • 329 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in subzeroid/aiograpi-rest
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • get(sessionid) content = requests.get(url).content usernames_tags = parse_upload_usertags(user
  • get(sessionid) content = requests.get(url).content return await photo_upload_post( cl,
  • .get(sessionid) content = requests.get(url).content return await _upload_story_content(
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • sha try: result = subprocess.run( ["git", "rev-parse", "--short", "HEAD"],
  • HONPATH", None) result = subprocess.run( [sys.executable, str(ROOT / "scripts" / "export_ope
✓ 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 subzeroid/aiograpi-rest appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • 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 aiograpi-rest 
Your task is to create a mini-application called 'AI Chat Monitor' which will serve as a real-time monitoring tool for multiple AI chat services. This application will allow users to monitor conversations across different AI chat platforms and analyze the sentiment of each message in real time. The application will use the 'aiograpi-rest' Python package to interact with various RESTful APIs of the AI chat services.

The core functionalities of the 'AI Chat Monitor' include:
1. Connecting to multiple AI chat platforms via their RESTful APIs using the 'aiograpi-rest' package.
2. Real-time monitoring of conversations, where new messages trigger sentiment analysis.
3. Displaying the sentiment score (positive, neutral, negative) for each message.
4. Providing a summary of the overall conversation sentiment over time.
5. Alerting users if the sentiment score crosses a predefined threshold.

Steps to build the application:
1. Set up the environment by installing the required packages including 'aiograpi-rest'.
2. Use 'aiograpi-rest' to connect to the RESTful APIs of the chosen AI chat platforms.
3. Implement real-time message fetching from these platforms using webhooks or polling methods.
4. Integrate a sentiment analysis module to process incoming messages and calculate sentiment scores.
5. Design a user interface to display the conversation and sentiment analysis results in real-time.
6. Add functionality to set and track sentiment thresholds for alerts.
7. Test the application thoroughly with different scenarios to ensure reliability and accuracy.

Some suggested features for enhancement:
- Support for multiple languages in sentiment analysis.
- Historical data storage for trend analysis.
- Customizable alert rules based on keywords or specific users.
- Integration with external tools for further processing of data.

By following these steps and utilizing the capabilities of 'aiograpi-rest', you will create a powerful and versatile tool for monitoring and analyzing AI chat conversations.

💬 Discussion Feed

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