🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its interaction with external services and use of shell commands without clear documentation. While there's no evidence of malicious intent, the combination of these factors warrants further scrutiny.

Moderate network risk due to interaction with external services
Use of shell commands without clear documentation

Per-check LLM notes

Network: The detected network calls suggest the package may interact with external services, which could be legitimate if documented and necessary for its functionality.
Shell: The use of shell commands to execute scripts and generate checksums is somewhat concerning without clear documentation, as it could potentially be used for unintended purposes.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer seems new or inactive, and the repository shows low engagement.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

, Any] | None, str]: with httpx.Client(base_url=base_url, timeout=30.0) as http: response =
, Any] | None, str]: with httpx.Client(timeout=30.0) as http: response = http.post(recovery
X-API-Key": api_key} with httpx.Client(base_url=base_url, timeout=timeout_s) as http: respo
-> str | None: response = httpx.get( PYPI_URL, timeout=timeout_s, header

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

r = temp_dir / "spec" subprocess.run( build_pyinstaller_command(dist_dir=dist_dir, bu
ake-archive") checksum = subprocess.check_output( ["shasum", "-a", "256", str(archive_path)],
.st_mode | stat.S_IXUSR) subprocess.run( ["bash", str(harness_path)], check=True,

✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Alexander Schneider" 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 adanos-cli

Create a financial sentiment analysis tool using the 'adanos-cli' package. This tool will fetch market sentiment data from the Adanos API via the command line interface provided by 'adanos-cli', analyze the sentiment of recent market news articles, and present the results in an easy-to-understand format. The application should allow users to specify a ticker symbol for a specific stock or cryptocurrency, and it should output a sentiment score indicating whether the recent news is generally positive, negative, or neutral about the asset.

### Core Features:
1. **Command Line Interface**: Users should be able to interact with the application through a simple CLI. Commands should include fetching sentiment data for a given ticker symbol and viewing historical sentiment trends.
2. **Sentiment Scoring**: Implement a system that converts raw sentiment data into a human-readable score. For example, scores could range from -10 (extremely negative) to +10 (extremely positive).
3. **Data Visualization**: Provide basic visualization capabilities to help users understand the sentiment trends over time. This could be as simple as plotting a line graph showing sentiment scores over the last week or month.
4. **Real-Time Alerts**: Allow users to set up alerts based on sentiment changes. For instance, if the sentiment score drops below a certain threshold, the user should receive a notification.
5. **Integration with External Tools**: Offer the ability to export sentiment data in common formats such as CSV or JSON for further analysis in other tools.

### Utilizing 'adanos-cli':
- Use 'adanos-cli' to fetch sentiment data by calling its API endpoints. Ensure that you handle authentication correctly if required by the API.
- Parse the returned JSON data to extract relevant information for your application's needs.
- Explore the 'adanos-cli' documentation to understand all available options and flags for customizing requests and responses.
- Consider implementing error handling to gracefully deal with issues like network errors or invalid inputs.

This project aims to provide a powerful yet accessible tool for investors and traders who want to gauge public opinion about their investments based on recent market news.