agentic-compiler

v0.1.0 suspicious
6.0
Medium Risk

Runtime-adaptive compilation for Python — auto-profile, auto-compile, hot-swap.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits behaviors that could potentially be used for evasion techniques, such as dynamic imports and handling ImportErrors. Although there are no direct signs of malicious activities like network or shell abuse, the lack of detailed maintainer information and the potential for obfuscation raise concerns.

  • Obfuscation risk due to dynamic imports and error handling
  • Lack of detailed maintainer information
Per-check LLM notes
  • Network: No network calls detected, indicating low risk.
  • Shell: Shell execution is used to check the availability of 'rustc', suggesting it's part of the package's functionality rather than malicious activity.
  • Obfuscation: The observed pattern is suspicious as it attempts to dynamically import a module and handle an ImportError, possibly to evade detection or analysis.
  • Credentials: No clear patterns indicative of credential harvesting were found.
  • Metadata: The package is new and lacks detailed maintainer information, raising some suspicion.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • try: mod = __import__(module_name, fromlist=["_"]) except ImportError: return {"success":
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • _available = False if os.system("which rustc > /dev/null 2>&1") == 0: self._avai
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: cocapn.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository SuperInstance/agentic-compiler appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 agentic-compiler 
Create a real-time performance optimization tool for Python scripts using the 'agentic-compiler' package. This tool will automatically profile running Python scripts, identify bottlenecks, compile the code for optimized performance, and then swap out the original script with the compiled version without interrupting the runtime execution. Here’s a step-by-step guide on how to build this tool:

1. **Setup Environment**: Begin by setting up a virtual environment for your project. Install the necessary dependencies including 'agentic-compiler'.
2. **Design User Interface**: Develop a simple command-line interface (CLI) where users can input their Python script file paths and optionally set profiling intervals.
3. **Script Profiling**: Implement a feature within the tool that runs the user's script under the profiler provided by 'agentic-compiler', collecting data on function execution times.
4. **Optimization Analysis**: Analyze the collected data to determine which parts of the script could benefit most from compilation.
5. **Automatic Compilation**: Use 'agentic-compiler' to compile the identified sections of the script for better performance. Ensure that the tool can handle partial compilation if not all of the script needs to be compiled.
6. **Hot-Swap Mechanism**: Develop a mechanism within the tool that allows it to replace the original script with the compiled version seamlessly while the script is still running. This should minimize downtime and disruption.
7. **Performance Reporting**: After the compilation and swapping process, generate a report showing the performance improvements achieved through the optimization.
8. **User Feedback Loop**: Allow users to provide feedback on the tool's performance and suggestions for improvement.
9. **Documentation & Testing**: Write comprehensive documentation explaining how to use the tool effectively. Conduct thorough testing to ensure reliability and accuracy of the performance optimizations.

Suggested Features:
- Customizable profiling intervals to balance between overhead and detail of profiling data.
- Support for multiple compilation targets (e.g., different Python versions).
- A dashboard or CLI that displays real-time performance metrics before and after optimization.
- Ability to save and compare performance metrics across different optimization sessions.