🤖 AI Analysis

Final verdict: SAFE

The package appears safe with low risks across all categories except for potential obfuscation, which is moderately concerning but does not conclusively indicate malicious activity.

No network or shell risks detected.
Potential obfuscation techniques used, but no evidence of credential harvesting or other malicious activities.

Per-check LLM notes

Network: No network calls detected, which is not necessarily suspicious for a data source connector like Mixpanel.
Shell: No shell executions detected, aligning with expectations for a data integration tool.
Obfuscation: The code suggests potential obfuscation through dynamic evaluation of configuration properties, which could be used for hiding logic but may not necessarily indicate malicious intent.
Credentials: No clear evidence of credential harvesting was found in the provided code snippets.
Metadata: The author has only one package, which might indicate a new or less active account, but no other red flags are present.

📦 Package Quality Overall: Medium (5.0/10)

○ 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://docs.airbyte.com/integrations/sources/mixpanel
Brief PyPI description (458 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

51 type-annotated function signatures detected in source

✦ High Multiple Contributors 10.0

Active multi-contributor project

14 unique contributor(s) across 100 commits in airbytehq/airbyte
Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

rent_stream_config.parent_key.eval(self.config) # type: ignore # parent_key is always casted t
stream_config.partition_field.eval(self.config) # type: ignore # partition_field is always cas

✓ 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: airbyte.io

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository airbytehq/airbyte appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Airbyte" 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 airbyte-source-mixpanel

Create a mini-application that integrates with Mixpanel using the 'airbyte-source-mixpanel' Python package. Your application should allow users to authenticate with their Mixpanel account, select specific event types and time ranges, and then extract data from Mixpanel into a local SQLite database for further analysis or reporting purposes. Here are the key steps and features your application should include:

1. User Authentication: Implement a secure way for users to authenticate with their Mixpanel account via API token.
2. Event Selection: Provide an interface for users to choose which event types they want to extract data from. This could be a dropdown menu or checkbox list of events available in their Mixpanel account.
3. Time Range Picker: Allow users to specify the date range for the data extraction, ensuring that the application respects the selected start and end dates.
4. Data Extraction: Utilize the 'airbyte-source-mixpanel' package to connect to Mixpanel and pull out the selected event data within the specified timeframe.
5. Data Storage: Store the extracted data locally in an SQLite database for easy querying and analysis.
6. Reporting Interface: Develop a simple reporting feature where users can run queries on the stored data to generate insights, such as event counts over time or user engagement metrics.
7. Error Handling and Logging: Ensure your application handles errors gracefully, logs important actions and errors, and provides feedback to the user about the status of their data extraction process.

Optional Features:
- Add a feature to visualize the extracted data using charts or graphs.
- Implement scheduling capabilities so users can set up regular data extractions.
- Offer the ability to export the queried results to CSV or Excel files for sharing or importing into other tools.