🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal signs of malicious intent with low scores across all categories except metadata, where there is some concern due to the incomplete maintainer's profile and potential inactivity.

Incomplete maintainer profile
Potential inactivity of the maintainer

Per-check LLM notes

Network: No network calls detected, indicating low risk.
Shell: Git commands used for version control purposes, suggesting normal development practices.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has an incomplete profile and appears to be new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

Test runner config found: pyproject.toml
Test runner config found: conftest.py
12 test file(s) detected (e.g. __init__.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (12572 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

234 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 100 commits in suportly/ai-augmented-developer
Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

None: try: out = subprocess.check_output( ["git", "rev-parse", "--abbrev-ref", "HEAD"],
f dry_run: return subprocess.check_call( ["git", "checkout", "-b", branch], cwd=str(cwd), st
""" try: result = subprocess.run( ["git", *args], cwd=str(cwd) if cwd
try: toplevel = subprocess.check_output( ["git", "rev-parse", "--show-toplevel"],
ss try: result = subprocess.run( ["git", "rev-parse", "--abbrev-ref", "HEAD"],

✓ 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

Repository suportly/ai-augmented-developer 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 aiadev

Create a command-line utility named 'SkillMaster' using the Python package 'aiadev'. This tool aims to streamline the process of validating developer skills, scaffolding out feature specifications, and installing development presets tailored for machine learning projects. SkillMaster should perform the following steps:

1. **Skill Validation**: On startup, SkillMaster should prompt the user to validate their proficiency in Python, Git, and Docker. It should use 'aiadev' to check against predefined skill criteria.
2. **Feature Specification Scaffolding**: After validation, the user should have the option to scaffold out a new feature specification for their project. This involves generating a template file based on input from the user about the feature's requirements and objectives. Utilize 'aiadev' to generate this template.
3. **Preset Installation**: Finally, SkillMaster should allow users to install development presets, which could include setting up virtual environments, installing necessary packages, and configuring version control settings. Again, leverage 'aiadev' to handle these tasks efficiently.

In addition to these core functionalities, consider adding features such as:
- **Interactive Help**: Provide a help command that gives detailed instructions on how to use each feature of SkillMaster.
- **Custom Presets**: Allow users to create and save their own presets for future use.
- **Progress Tracking**: Implement a system that tracks the progress of skill validation and feature specification completion.

The goal is to create a user-friendly and efficient tool that enhances the workflow of developers working on machine learning projects. Use 'aiadev' to its fullest potential to ensure that all processes are automated and streamlined.