🤖 AI Analysis

Final verdict: SAFE

Based on the analysis, the package appears to be safe with no indications of malicious activities or supply-chain attacks.

Low risk scores across all categories.
No clear signs of obfuscation, shell execution, or credential mishandling.

Per-check LLM notes

Network: The detected network call patterns are typical for SDKs that interact with REST APIs, suggesting normal operation rather than malicious intent.
Shell: No shell execution patterns were detected, indicating no risk associated with executing arbitrary commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
Metadata: The package shows some low-effort signs but lacks clear indicators of malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

t self._rest_client = httpx.Client(headers=headers, base_url=base_url + Routes.REST.value, time
t self._rest_client = httpx.AsyncClient(headers=headers, base_url=base_url + Routes.REST.value, time
nses = [] async with httpx.AsyncClient() as client: for entry in entries:
s://") self._client = httpx.AsyncClient( headers=headers, base_url=self._get
: return ServerInfo(**httpx.get(self._client.base_url.join(INFO_PATH), timeout=self._client.
atchedResponse( **httpx.post( self._client.base_url.join(BATCH_SCORE_PATH

✓ 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

Email domain looks legitimate: adaptive-ml.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 adaptive-sdk

Your task is to create a real-time sentiment analysis tool using the 'adaptive-sdk' Python package. This tool will allow users to input text snippets or URLs of social media posts and receive immediate feedback on the overall sentiment of the content. The application should be designed to integrate seamlessly with various social media platforms such as Twitter, Reddit, and Facebook, fetching recent posts from specified user accounts or communities. Here’s a detailed plan on how to build this mini-app:

1. **Project Setup**: Begin by setting up your development environment. Ensure you have Python installed along with the necessary packages including 'adaptive-sdk'. Also, install additional libraries like 'requests' for handling HTTP requests and 'tweepy' for integrating with Twitter.

2. **Integration with Social Media Platforms**: Use OAuth authentication to securely connect your app with social media APIs. For each platform, fetch the latest posts from predefined accounts or communities and display them in a readable format.

3. **Sentiment Analysis Module**: Utilize the 'adaptive-sdk' to process and analyze the fetched content. The core functionality of 'adaptive-sdk' involves real-time data processing and adaptive learning models which can be employed to gauge sentiments effectively. Implement a function that takes in raw text and outputs a sentiment score ranging from -1 (extremely negative) to +1 (extremely positive).

4. **User Interface Design**: Develop a simple yet effective UI where users can input text directly or enter a URL pointing to a social media post. Upon submission, the tool should instantly show the sentiment analysis results.

5. **Advanced Features**: Consider adding advanced features such as a historical sentiment trend graph over time, a comparison feature between different users or topics, and a word cloud highlighting frequently used words in positive/negative contexts.

6. **Testing & Optimization**: Rigorously test your application to ensure it accurately reflects the sentiment of the analyzed texts. Optimize the performance and accuracy of sentiment analysis using the adaptive learning capabilities provided by 'adaptive-sdk'.

7. **Deployment**: Finally, deploy your application either as a web service or a desktop application so that others can use it too. Ensure all API keys and sensitive information are properly secured.

This project not only showcases the power of 'adaptive-sdk' in real-world applications but also provides valuable insights into public opinion trends across different social media platforms.