🤖 AI Analysis

Final verdict: SAFE

The package appears to be a legitimate bot adapter with low risks across multiple categories. However, the low maintainer activity and incomplete metadata raise some concerns about its quality and long-term support.

Low network, shell, obfuscation, and credential risks.
Incomplete metadata and low maintainer activity suggest potential issues with quality and support.

Per-check LLM notes

Network: Network calls are expected for a bot adapter to communicate with a gateway or API.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: Low maintainer activity and incomplete metadata suggest potential low-quality or malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

self): self.session = aiohttp.ClientSession() try: await self._ensure_token()
self): self.session = aiohttp.ClientSession() gateway_url = self.adapter.config.get("gateway_url

✓ 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 score 3.0

Suspicious email domain flags: Very short email domain: qq.com>

Very short email domain: qq.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

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ErisPulse-QQBotAdapter

Your task is to create a fully-functional mini-application that leverages the 'ErisPulse-QQBotAdapter' package to integrate with QQ's official bot API. This application will serve as a versatile chatbot capable of performing various tasks within QQ groups and private chats. Here’s a detailed guide on what your application should achieve and how you can utilize the package effectively:

1. **Setup**: Begin by installing the necessary packages including 'ErisPulse-QQBotAdapter'. Ensure your development environment is set up with Python and the latest version of the package.

2. **Authentication**: Use the 'ErisPulse-QQBotAdapter' package to authenticate your bot with QQ's servers. This involves setting up a configuration file with your bot's access token or credentials provided by QQ.

3. **Basic Functionality**: Implement basic functionalities such as joining QQ groups based on user commands or invitations. Your bot should also be able to send messages back to users when they initiate conversations.

4. **Command Handling**: Design a command system where users can type specific commands to trigger actions from the bot. For example, '!help' could display a list of available commands, '!weather [city]' could fetch weather updates for specified cities using an external API, and '!joke' could return a random joke.

5. **Event Listeners**: Utilize event listeners provided by 'ErisPulse-QQBotAdapter' to respond to different types of events like messages, member joins/leaves, and group events. Each event handler should be designed to perform specific actions based on the event type.

6. **Custom Features**: Introduce custom features that add value to the QQ community. These could include automated reminders, trivia questions, or even a simple game like 'Rock-Paper-Scissors'.

7. **Logging & Monitoring**: Implement logging mechanisms to track bot activities and user interactions. This will help in debugging and improving the bot over time.

8. **Security Measures**: Ensure that all sensitive information such as tokens and keys are securely stored and handled. Also, consider adding rate-limiting to prevent abuse.

9. **Testing & Deployment**: Before deploying, thoroughly test the bot in a sandbox environment to ensure it works as expected. Once ready, deploy the bot to QQ and monitor its performance.

Throughout the development process, make sure to leverage the 'ErisPulse-QQBotAdapter' package efficiently to handle bot operations, interactions, and integrations with QQ's ecosystem.