🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks associated with network calls, shell executions, and obfuscation. While the metadata quality and maintenance level are concerning, there's no concrete evidence of malicious intent or supply-chain attack.

Low network and shell risk
No signs of obfuscation or credential harvesting
Metadata and maintenance concerns

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution patterns detected, indicating no immediate signs of executing system 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 low maintenance and metadata quality, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aditiputtur/abforge appears legitimate

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
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 abforge

Your task is to develop a mini-application that helps digital marketers manage their A/B testing campaigns more effectively using the 'abforge' Python package. This tool will provide insights into campaign performance, suggest optimal times for decision-making based on statistical significance, and offer recommendations for improving campaign outcomes through variance reduction techniques.

**Core Features:**
1. **Campaign Setup:** Users can input details of their A/B tests including the number of variants, expected conversion rates, and desired confidence levels.
2. **Power Analysis:** Utilize 'abforge' to perform power analysis to determine the sample size needed to achieve a statistically significant result given the user's inputs.
3. **Hypothesis Testing:** After the test runs, use 'abforge' to conduct hypothesis tests to determine if there's a significant difference between the control and variant groups.
4. **Sequential Testing:** Implement sequential testing capabilities allowing users to make decisions as data comes in, without waiting for the test to reach its pre-defined end date.
5. **Variance Reduction:** Apply CUPED (Covariate-Adjusted Response Measurement) techniques to reduce the variance in the experiment results, providing clearer insights.
6. **Dashboard Generation:** Create a simple dashboard that visualizes key metrics such as conversion rates, p-values, and effect sizes.

**User Interface Requirements:**
- The application should have a clean, user-friendly interface that allows users to easily input campaign details and view results.
- Include interactive elements like sliders for adjusting parameters and real-time updates of estimated sample sizes.
- Provide clear explanations of each feature and the underlying statistics in a non-technical manner.

**Additional Features (Optional):**
- Email notifications when the test reaches statistical significance.
- Historical data storage and comparison.
- Integration with popular marketing analytics tools.

Use the 'abforge' package to handle the statistical heavy lifting behind these features, ensuring that your application provides accurate and actionable insights for users managing their A/B tests.