🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high levels of obfuscation and potential credential harvesting, along with incomplete metadata and network requests that have not been fully vetted.

High obfuscation risk
Potential credential harvesting
Incomplete metadata

Per-check LLM notes

Network: The use of httpx for network requests is common and not inherently suspicious; however, the context of these requests should be reviewed to ensure they are legitimate.
Shell: No shell execution patterns were detected.
Obfuscation: The code shows signs of obfuscation through unconventional syntax and comments, which may indicate an attempt to hide the true purpose of the code.
Credentials: The code attempts to collect passwords and uses base64 and hashing imports, which could be indicative of credential harvesting.
Metadata: The missing repository and short author details raise concerns about the legitimacy of the package.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

Test runner config found: pyproject.toml
14 test file(s) detected (e.g. test_action_engine.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://github.com/your-org/alpha-id#readme
Detailed PyPI description (1382 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

356 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 3.0

Found 2 network call pattern(s)

try: async with httpx.AsyncClient(timeout=60.0) as client: async with client.strea
httpx _llm_client = httpx.Client(timeout=60.0, limits=httpx.Limits(max_keepalive_connections=

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

eturn alpha_id key_path = __import__("pathlib").Path.home() / ".aid" / "identity.did" if key_path.exist
获取 Alpha-ID""" key_path = __import__("pathlib").Path.home() / ".aid" / "identity.did" if key_path.exist

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

# 加密 pwd = password or getpass.getpass("加密密码: ") try: import base64 import hash
bytes() pwd = password or getpass.getpass("解密密码: ") try: import base64 import has

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ 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 alpha-id-zix

Create a personalized AI profile manager called 'DigitalSoul' using the Python package 'alpha-id-zix'. This mini-app will allow users to consolidate their interactions with various AI tools into a single, cohesive identity, thereby ensuring consistency and continuity across different platforms. Here’s how you can build it:

1. **User Registration**: Allow users to register and create a unique DigitalSoul profile. During registration, collect basic information such as username, email, and a brief bio.
2. **AI Interaction Logging**: Implement functionality to log user interactions with different AI services (e.g., ChatGPT, DALL-E, etc.). These logs should capture the context of the interaction, the response from the AI, and any feedback provided by the user.
3. **Profile Consolidation**: Utilize the 'alpha-id-zix' package to ensure that all logged interactions contribute to a unified AI profile. This profile should reflect the user’s preferences, interests, and communication style across all AI interactions.
4. **Profile Export and Import**: Enable users to export their consolidated AI profiles to share with other services or import profiles from other sources. This feature should support common data formats like JSON.
5. **Personalized Recommendations**: Based on the consolidated AI profile, generate personalized recommendations for content, services, or even potential questions to ask in future interactions with AI tools.
6. **Privacy Settings**: Provide users with granular control over privacy settings, allowing them to decide which aspects of their profile are shared with specific AI tools or services.
7. **User Interface**: Design a simple yet intuitive web interface for users to manage their DigitalSoul profiles, view interaction logs, and access recommendations.

Utilize the 'alpha-id-zix' package to handle the backend logic for consolidating user interactions into a coherent AI profile. This includes processing logged interactions, updating the user’s consolidated profile, and generating personalized recommendations based on the accumulated data.