b2b-revenue-forecasting

v0.4.0 safe
3.0
Low Risk

A Python framework for hierarchical B2B sales quota cascading and pipeline reconciliation.

🤖 AI Analysis

Final verdict: SAFE

The package appears safe with no detected network calls, shell executions, obfuscations, or credential risks. However, the low activity and community engagement from the maintainer raise some concerns.

  • No network calls or shell executions detected.
  • Low community engagement and possibly inactive maintainer.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on revenue forecasting without real-time data updates or external service integration.
  • Shell: No shell execution patterns detected, indicating the package does not attempt to execute system commands, which is typical for a safe Python library.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The package maintainer seems new or inactive, and the repository lacks community engagement.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

  • 4 test file(s) detected (e.g. test_commit.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (21084 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 36 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 48 commits in shreyasrkarwa/Analytics
  • Two distinct contributors found

🔬 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 score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Shreyas Karwa" 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 b2b-revenue-forecasting 
Create a comprehensive mini-application called 'SalesQuotaPro' using the Python package 'b2b-revenue-forecasting'. This application will serve as a tool for sales managers to manage and forecast their team's revenue targets more effectively. The goal is to provide a user-friendly interface where users can input their current sales data, set future sales goals, and receive accurate forecasts based on historical performance and current market trends. Here’s a detailed breakdown of the application's requirements and functionalities:

1. **User Interface**: Develop a simple yet intuitive web-based UI using Flask or Django for the front-end.
2. **Data Input**: Allow users to upload their sales data in CSV format. The application should support various types of sales data such as monthly sales figures, product categories, and geographic regions.
3. **Sales Goal Setting**: Provide functionality for setting quarterly and annual sales goals for different departments within the company. Users should be able to adjust these goals dynamically based on new information or changing business conditions.
4. **Forecasting Engine**: Utilize the 'b2b-revenue-forecasting' package to process the uploaded data and generate sales forecasts. The engine should take into account hierarchical structures within the organization, allowing for accurate forecasting at both departmental and individual salesperson levels.
5. **Reconciliation Reports**: Generate detailed reports that reconcile actual sales against forecasted numbers, highlighting discrepancies and potential areas for improvement.
6. **Interactive Dashboard**: Implement an interactive dashboard that visualizes key performance indicators (KPIs) such as sales growth rate, conversion rates, and revenue per customer. Use libraries like Plotly or Matplotlib for dynamic charts and graphs.
7. **Notifications and Alerts**: Set up automated notifications for significant deviations from the forecasted values. These alerts can be sent via email or SMS to ensure that sales teams are always aware of any issues.
8. **Security and Privacy**: Ensure that all user data is securely stored and processed, adhering to GDPR and other relevant privacy regulations. Implement robust authentication mechanisms to protect access to sensitive sales data.
9. **Documentation and Support**: Provide thorough documentation for both end-users and developers, including tutorials, FAQs, and a support forum. Make sure that users can easily understand how to use the application and troubleshoot common issues.

The 'b2b-revenue-forecasting' package will be utilized primarily in the forecasting engine component. It will handle the complex calculations needed to predict future sales based on historical data, taking into account the hierarchical nature of the sales structure. Additionally, the package will help in reconciling the forecasted sales with the actual sales figures, providing valuable insights for decision-making.

💬 Discussion Feed

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