🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks associated with network usage, shell command execution, and code obfuscation. While these elements do not definitively point to malicious intent, they raise significant concerns about the package's integrity and the potential for misuse.

moderate network risk
high shell risk
medium obfuscation risk

Per-check LLM notes

Network: The use of httpx for network calls to an API endpoint may be legitimate depending on the package's functionality, but requires further investigation into its intended purpose.
Shell: Execution of commands using subprocess.run and subprocess.Popen could indicate legitimate functionality such as system checks or data processing, but also raises concerns about potential unauthorized actions or system manipulation.
Obfuscation: The observed obfuscation patterns suggest an attempt to hide the import of modules or their versions, which could indicate an attempt to avoid detection or tampering.
Credentials: No clear patterns indicative of credential harvesting were detected.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate potential risks.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

import httpx r = httpx.get(f"http://{host}:{port}/api/status", timeout=0.8) ret
import httpx r = httpx.get(f"http://{host}:{port}/api/status", timeout=0.8) if

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

{"name": "cortex", "version": __import__("cortex").__version__}, }) if method == "tools/l
try: module = __import__(f"skills.{name}.skill", fromlist=["*"]) except Exception: continue # Lo

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

n <cap>` (or since-date) via `subprocess.Popen(text=True, bufsize=1)` so memory stays bounded on large repo
o"]) try: proc = subprocess.Popen( args, stdout=subprocess.PIPE,
one try: result = subprocess.run( [lsof, "-iTCP", "-sTCP:LISTEN", "-P", "-n", "-p
None: try: return subprocess.run( cmd, cwd=str(cwd), capt

✓ Credential Harvesting

No credential harvesting patterns detected

✓ 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 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 agentcortex

Your task is to develop a Python-based mini-application named 'CodeCompanion' that leverages the capabilities of the 'agentcortex' package to assist developers in writing cleaner, more efficient code. This application will serve as a coding assistant, providing real-time suggestions and corrections based on semantic understanding of the code being written. Here’s a detailed plan on how to proceed:

1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have the latest version of Python installed along with necessary libraries such as `agentcortex`.
2. **Understand 'agentcortex'**: Familiarize yourself with the functionalities provided by the `agentcortex` package. It offers tools for semantic analysis, code suggestion, and refactoring. Understand how these tools can be integrated into a live coding session.
3. **Design Application Interface**: Design a simple yet effective interface for 'CodeCompanion'. This could be a command-line interface (CLI) or a graphical user interface (GUI). For simplicity, start with a CLI interface using standard Python input/output mechanisms.
4. **Implement Core Features**:
- **Real-Time Code Analysis**: Implement a feature where 'CodeCompanion' reads the code as it's being written and provides immediate feedback on syntax errors, potential improvements, and best practices.
- **Code Suggestions**: Use 'agentcortex' to generate suggestions for improving code efficiency and readability. These could include refactoring suggestions, library imports, and better variable naming conventions.
- **Error Handling and Correction**: Integrate 'agentcortex' to detect common programming mistakes and suggest corrections. This includes identifying and fixing issues like unused variables, redundant code blocks, and incorrect function calls.
5. **User Interaction**: Ensure the application allows for smooth interaction between the user and the AI. Users should be able to request specific analyses or suggestions at any point during their coding session.
6. **Testing and Validation**: Test 'CodeCompanion' thoroughly with various types of code snippets to ensure it performs well across different scenarios. Validate its accuracy in suggesting improvements and handling errors.
7. **Enhancements**: Once the basic functionality is working, consider adding advanced features such as integration with version control systems (like Git), support for multiple programming languages, or even a machine learning model trained on user-specific coding styles.
8. **Documentation**: Finally, document your application comprehensively. Include setup instructions, usage guidelines, and examples of how 'CodeCompanion' can enhance the coding process.

By following these steps, you'll create a valuable tool that not only assists developers in writing better code but also demonstrates the power of semantic understanding in AI-assisted coding.