🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits unusually high obfuscation risk due to the use of eval and list comprehensions, despite having low risks in other categories. This suggests potential attempts to obscure functionality, warranting further investigation.

High obfuscation risk due to unusual use of eval and list comprehensions.
Incomplete author metadata and potentially inactive account.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
Obfuscation: The code uses eval and list comprehension in an unusual way, which may indicate obfuscation to hide logic or evade analysis.
Credentials: No obvious patterns indicative of credential harvesting were found.
Metadata: The author's details are incomplete and the account seems new or inactive, raising some concerns but not definitive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

[round(x*(1000/255)) for x in eval(f"TSNDL.Color.{scheme}.{color}")]); curses.init_pair(Numb

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: sirio-network.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Ascellayn/TSN_Abstracter appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 TSN-Abstracter

Create a mini-application named 'TSN-AutoSummarizer' using the Python package 'TSN-Abstracter'. This application will allow users to input long texts (e.g., articles, books, or any document) and generate concise summaries automatically. The goal is to leverage the abstraction capabilities of 'TSN-Abstracter' to provide meaningful, succinct summaries that capture the essence of the original content.

**Steps to Build the Application:**
1. **Setup Environment**: Install necessary packages including 'TSN-Abstracter' and any other dependencies like 'requests' for fetching web content if needed.
2. **User Interface Design**: Develop a simple command-line interface (CLI) or a basic web-based UI where users can paste their text or upload files.
3. **Text Processing**: Implement functionality to process user inputs, ensuring compatibility with various text formats and sizes.
4. **Summary Generation**: Utilize 'TSN-Abstracter' to generate summaries from the processed texts. Ensure that the summaries are coherent and retain key information from the original text.
5. **Output Display**: Present the summary back to the user through the chosen interface in an easily readable format.
6. **Enhancements**: Consider adding features such as highlighting key phrases, providing a word count comparison between original and summary, and allowing users to adjust the length of the summary output.

**Core Features to Highlight:**
- Seamless integration with 'TSN-Abstracter' for efficient summarization.
- User-friendly interface for easy interaction.
- Capability to handle large volumes of text data effectively.
- Customizable summary length based on user preference.