🤖 AI Analysis

Final verdict: SAFE

The package has a moderate risk due to its execution of shell commands and use of network calls, but these actions are not indicative of malicious behavior based on the provided analysis notes.

Moderate shell risk due to potential for executing commands.
Minor network risk from HTTP requests.

Per-check LLM notes

Network: The network call pattern suggests the package might be using HTTP requests to communicate with external services, which is not inherently suspicious but should be reviewed for context.
Shell: Executing shell commands can pose significant risks if not properly sanitized or controlled, especially given the potential for unauthorized actions like creating repositories or interacting with git.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some minor red flags, but no strong indicators of malicious intent.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (12470 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

424 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 100 commits in visserle/AnkiOps
Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

eNotesNotetype"}) _session = requests.Session() class AnkiConnectionError(Exception): """Raised when

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

if gh_path: result = subprocess.run( [gh_path, "repo", "view", slug], cw
itory %s", slug) result = subprocess.run( [gh_path, "repo", "create", slug, _visibility_flag(
err) return gh = subprocess.run( [ "gh", "pr", "
".join(args)) return subprocess.run( ["git", *args], cwd=self.collection

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://host.docker.internal:8766

✓ Git Repository History

Repository visserle/AnkiOps appears legitimate

⚠ 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 ankiops

Create a Python-based mini-application called 'AnkiSyncer' that integrates the 'ankiops' package to facilitate seamless synchronization between Anki flashcards and Markdown notes. This application will enable users to manage their study materials more efficiently by allowing them to create, edit, and sync notes directly from their Markdown files into Anki's database, and vice versa. Additionally, it will incorporate an AI-driven feature that suggests new card types based on the content of the Markdown file and user preferences.

### Core Features:
1. **Markdown to Anki Synchronization:** Users can specify a directory containing Markdown files. Your application reads these files, extracts relevant information, and creates corresponding Anki notes based on predefined templates.
2. **Anki to Markdown Export:** Similarly, users can export their Anki decks into Markdown format, preserving the structure and content of each card.
3. **Custom Note Types:** Users can define their own note types within the Markdown files using specific syntax, which your application translates into Anki's custom note types.
4. **LLM Integration:** Utilize an LLM (Language Learning Model) to suggest optimal card layouts and additional information based on the content of the Markdown files. This could include adding images, links, or multimedia elements to enrich learning.
5. **User Interface:** Develop a simple command-line interface (CLI) for users to interact with the application easily. The CLI should allow commands such as 'sync', 'export', and 'generate', each performing the respective functions mentioned above.
6. **Configuration Management:** Allow users to configure settings like default note types, preferred LLM services, and sync intervals through a configuration file.

### How 'ankiops' is Utilized:
- Use 'ankiops' to establish a connection with the Anki API, ensuring secure and efficient data transfer.
- Leverage 'ankiops' functionalities to create and manage custom note types, facilitating the import/export process.
- Employ 'ankiops' to integrate LLM capabilities, enabling dynamic content generation and customization of Anki cards based on advanced AI analysis of the Markdown content.