autolecture

v0.1.0 suspicious
4.0
Medium Risk

Official Python SDK for AutoLecture (https://autolecture.ai). Generate explainer videos from script or audio via a clean HTTP client.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal activity and incomplete metadata, raising concerns about its legitimacy. However, no direct malicious activities such as shell execution or credential harvesting were observed.

  • Incomplete author profile and low activity level
  • Potential unauthorized network communication
Per-check LLM notes
  • Network: The network call pattern suggests the package may be performing HTTP requests to fetch resources, which is common but should be reviewed to ensure it's not communicating with unauthorized servers.
  • Shell: No shell execution patterns were detected, indicating that the package does not appear to execute system commands directly.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package is new with minimal activity and an incomplete author profile, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 6 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://autolecture.ai/docs/dsl
  • Detailed PyPI description (5096 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 73 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 5 commits in scao7/autolecture-python
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • p("/") self._client = httpx.Client( timeout=timeout, 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: gmail.com>

✓ 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 6.0

3 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)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with autolecture 
Create a desktop application using Python that allows users to easily generate explainer videos from their written scripts or recorded audio files. This app, named 'VideoMint', will utilize the 'autolecture' package to interact with the AutoLecture API and streamline the process of video creation.

Step 1: Design the User Interface
- Develop a simple and intuitive GUI using PyQt5 or Tkinter.
- Include options for uploading scripts or audio files.
- Provide fields for specifying video title, description, and tags.
- Offer customization options such as background style, character appearance, and video length.

Step 2: Implement Script/Audio Processing
- Allow users to input or upload a script directly into the application.
- Enable users to select an audio file from their device.
- Ensure the application supports common audio formats like MP3, WAV, etc.

Step 3: Integrate autolecture Package
- Use the autolecture package to convert the uploaded content into an explainer video.
- Utilize the autolecture SDK to send requests to the AutoLecture API with user-specified parameters.
- Handle the response from the API to download and save the generated video locally.

Step 4: Add Additional Features
- Incorporate a preview feature where users can see a thumbnail of the video before finalizing.
- Implement a progress bar to show the status of video generation.
- Allow users to save their projects and resume them later if needed.
- Include an option to share the video directly to social media platforms.

Step 5: Testing and Deployment
- Thoroughly test the application to ensure all functionalities work as expected.
- Optimize performance and fix any bugs found during testing.
- Deploy the application as a standalone executable that can be run on Windows, macOS, and Linux.

💬 Discussion Feed

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