auto-data-alerts

v0.0.2 suspicious
5.0
Medium Risk

Automated Data Alerts for Slack

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to its metadata risk and network usage, despite having low risks in other categories.

  • Metadata risk is high due to missing repository and sparse maintainer information.
  • Network risk exists as the package uses Slack API calls.
Per-check LLM notes
  • Network: The package appears to use Slack API calls, which could be legitimate for sending alerts, but may require further investigation into the context and permissions.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or credential theft.
  • Metadata: The repository is not found and the maintainer's information is sparse, raising concerns about potential malicious intent.

📦 Package Quality Overall: Low (3.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://cooper-richason.github.io/auto-data-alerts
  • Detailed PyPI description (3431 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

  • 60 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • fails. """ response = requests.get('https://slack.com/api/users.list', headers=slack_headers, p
  • er_id. """ response = requests.post('https://slack.com/api/conversations.open', headers=slack_he
  • metadata response = requests.post( "https://slack.com/api/chat.postMessage", headers=headers,
✓ 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: users.noreply.github.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ 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 auto-data-alerts 
Create a fully functional mini-application named 'DataGuard' using Python that leverages the 'auto-data-alerts' package to monitor and alert on critical data changes via Slack. The application should be designed to continuously track specific data points from a predefined source (such as a CSV file or a database table) and send real-time alerts to a Slack channel when these data points exceed certain thresholds or show significant changes. Here’s a detailed plan for the project:

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with the necessary libraries including 'auto-data-alerts', pandas, and any other required packages.
2. **Define Data Sources**: Define at least two different data sources such as a local CSV file and a SQLite database table. Each data source will have its own set of monitored data points.
3. **Threshold Configuration**: Allow users to configure threshold values for each data point. For instance, if monitoring sales figures, users could set a threshold where they want to be alerted if sales drop below a certain amount.
4. **Alert Mechanism**: Utilize the 'auto-data-alerts' package to set up an alert system that triggers whenever a monitored data point exceeds its configured threshold. These alerts should be sent to a specified Slack channel using the Slack API.
5. **User Interface**: Develop a simple command-line interface (CLI) for users to interact with the application. This CLI should allow users to start/stop monitoring, configure thresholds, and view status updates.
6. **Logging and Reporting**: Implement logging to record all activities and events within the application. Additionally, provide a reporting feature that generates a summary report of all alerts sent during a specified period.
7. **Testing**: Thoroughly test the application with various scenarios to ensure it functions correctly under different conditions. Pay special attention to edge cases like network disruptions or data anomalies.
8. **Documentation**: Write comprehensive documentation explaining how to install, configure, and use the 'DataGuard' application. Include examples and best practices for setting up alerts.

The goal is to create a robust, user-friendly tool that makes it easy for anyone to monitor critical data points and receive immediate alerts via Slack.

💬 Discussion Feed

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