🤖 AI Analysis

Final verdict: SAFE

The package appears to be safe with low risks across most categories, though there is a moderate obfuscation risk that warrants further investigation.

moderate obfuscation risk
low activity from maintainer

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external communications.
Shell: No shell executions detected, reducing the risk of executing arbitrary commands.
Obfuscation: The observed patterns suggest potential obfuscation techniques, but they could also be part of normal operations involving JWT decoding and data processing.
Credentials: No clear signs of credential harvesting were detected.
Metadata: The maintainer has only one package, which could indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

json.loads(base64.b64decode(issued_jwt.split(".")[1] + "==").decode("utf-8"))["iss"]
olumn_name) .list.eval(pl.when(within_tolerance).then(pl.lit(ge_value).cast(inner_d
olumn_name) .list.eval(pl.when(within_tolerance).then(pl.lit(le_value).cast(inner_d

✓ 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: ecco.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository SneaksAndData/adapta appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "ECCO Sneaks & Data" 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 adapta

Create a mini-application named 'DataLogAnalyzer' using Python, which leverages the 'adapta' package to streamline data logging, analysis, and monitoring processes. This tool aims to simplify the life of data scientists and engineers by providing a suite of functionalities including real-time data logging, automated data cleaning, basic statistical analysis, and anomaly detection. Here’s a detailed breakdown of the project steps and suggested features:

1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have Python installed and create a virtual environment. Install necessary packages including 'adapta', pandas, and numpy.

2. **Data Ingestion**: Use 'adapta' to connect to various data sources such as databases, APIs, or CSV files. Implement a feature that allows users to specify their data source and retrieve data seamlessly.

3. **Real-Time Logging**: Develop a module that logs incoming data in real-time using 'adapta'. Ensure that the logging process is efficient and does not hinder performance.

4. **Data Cleaning**: Incorporate 'adapta' features for data cleaning and preprocessing. This should include handling missing values, removing duplicates, and converting data types as needed.

5. **Statistical Analysis**: Utilize 'adapta' for generating summary statistics and visualizations. Users should be able to get insights like mean, median, mode, standard deviation, and more.

6. **Anomaly Detection**: Implement a feature that uses 'adapta' to detect anomalies in the data. This could involve threshold-based detection or more advanced methods like clustering or machine learning models.

7. **Monitoring & Alerts**: Set up monitoring capabilities within 'adapta' to track the health and performance of the data pipeline. Integrate alert systems to notify users via email or SMS when anomalies or errors occur.

8. **Secret Handling**: Ensure secure handling of sensitive information like API keys or database passwords using 'adapta'.

9. **User Interface**: Although optional, consider developing a simple command-line interface or a web-based UI using Flask or Django to interact with the application.

Throughout the development process, ensure that 'adapta' is utilized efficiently for its intended purposes, making the application robust and user-friendly.