analytic-workbench-clients

v0.7.2 suspicious
4.0
Medium Risk

E360 Analytic Workbench Clients for Python

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to incomplete metadata, including missing maintainer information and insecure external links, which raises concerns about its provenance and reliability.

  • Missing maintainer information
  • Insecure external links
Per-check LLM notes
  • Network: No network calls suggest normal behavior unless the package's intended functionality requires external communication which is not indicated here.
  • Shell: No shell executions indicate that the package does not perform any system-level command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret theft.
  • Metadata: The package shows some red flags such as missing maintainer information and non-secure external links, but there's no clear evidence of typosquatting or direct malicious intent.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1230 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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

Email domain looks legitimate: iqvia.com>

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://rwes-gitlab01.internal.imsglobal.com/python-microservice-clients/e360-ana
  • Non-HTTPS external link: http://rwes-gitlab01.internal.imsglobal.com/python-microservice-clients/e360-ana
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 analytic-workbench-clients 
Create a Python-based dashboard application that leverages the 'analytic-workbench-clients' package to fetch, analyze, and visualize data from an E360 Analytic Workbench. This application will serve as a real-time data monitoring tool for businesses, allowing users to track key performance indicators (KPIs) such as sales trends, customer engagement metrics, and operational efficiency scores.

### Step 1: Setting Up the Environment
- Ensure you have Python installed on your machine.
- Install necessary libraries including 'analytic-workbench-clients', 'pandas', 'matplotlib', and 'streamlit'.

### Step 2: Data Fetching
- Use the 'analytic-workbench-clients' package to authenticate and connect to the E360 Analytic Workbench.
- Define functions to pull specific datasets related to KPIs such as sales data, customer feedback, and operational metrics.

### Step 3: Data Processing
- Clean and preprocess the fetched data using pandas to ensure it's ready for analysis.
- Implement basic statistical analyses like mean, median, mode, standard deviation, etc., to understand the dataset better.

### Step 4: Data Visualization
- Create visualizations such as line graphs, bar charts, and heatmaps using matplotlib or any other visualization library of your choice.
- Design these visualizations to reflect the trends and patterns identified during the data processing phase.

### Step 5: Building the Dashboard
- Utilize Streamlit to develop an interactive web application where users can view the visualized data.
- Include filters and dropdown menus to allow users to select different datasets and time periods.
- Ensure the dashboard is user-friendly and provides actionable insights through its visualizations.

### Suggested Features:
- **Real-Time Updates**: Automatically refresh data at regular intervals.
- **Customizable Views**: Allow users to customize which KPIs they want to monitor.
- **Export Options**: Provide options to export visualizations and raw data in CSV or Excel formats.
- **Alert System**: Implement a system that sends email alerts based on predefined thresholds for certain KPIs.

💬 Discussion Feed

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