AI Analysis
The package exhibits moderate risks due to its network and shell execution activities, which could potentially lead to vulnerabilities. However, there's no strong evidence of malicious intent.
- Moderate network risk
- Potential shell injection risk
Per-check LLM notes
- Network: Network calls are typical for CLI tools to fetch resources or communicate with servers.
- Shell: Use of shell execution can be legitimate but also poses higher risks due to potential command injection vulnerabilities.
- Obfuscation: The use of base64 decoding may indicate an attempt to obfuscate code or data, but it is also a common practice for data encoding and transmission.
- Credentials: No clear patterns of credential harvesting were detected.
- Metadata: The package shows signs of low maintainer activity and incomplete metadata, raising some suspicion but not conclusive evidence of malice.
Package Quality Overall: Low (4.4/10)
Test suite present — 6 test file(s) found
6 test file(s) detected (e.g. test_chat_workflow.py)
Some documentation present
Detailed PyPI description (5866 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
336 type-annotated function signatures detected in source
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Found 5 network call pattern(s)
item.get("url"): with httpx.Client(timeout=180.0) as client: img_response = client.} try: with httpx.Client(timeout=180.0) as client: response = client.post} with httpx.Client(timeout=180.0) as client: response = client.ify = _ssl_verify() with httpx.Client(timeout=TIMEOUT, verify=verify) as client: resp = clast_status}") r = httpx.get(response_url, timeout=TIMEOUT, verify=verify) la
Found 3 obfuscation pattern(s)
mage_base64"): return base64.b64decode(body["image_base64"]), body.get("revised_prompt") datat("b64_json"): return base64.b64decode(first_item["b64_json"]), first_item.get("revised_prompt")self._progress_lock = __import__('threading').Lock() def execute_coarse_phase(self, file_list: L
Found 6 shell execution pattern(s)
s (vim, top, etc.) go through os.system() and return # {"message": ..., "exit_code": ...} -- nottry: exit_code = os.system(full_cmd_str) actual_exit_code = exit_code >> 8""" try: result = subprocess.run( ["git", "rev-parse", "--show-toplevel"],update(env) result = subprocess.run( cmd, cwd=self.git_root,sues result = subprocess.run( f"{command} /?", shtry: result = subprocess.run(['command', '-v', command], capture_output=True, text=True,
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: acrotron.com>
All external links appear legitimate
Repository not found (deleted or private)
Repository not found (deleted or private)
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Develop a fully-functional mini-application called 'CodeSavior' which leverages the capabilities of the 'axiomai-cli' package to assist developers in writing cleaner, more efficient code. This application will serve as a coding assistant, providing real-time suggestions, refactoring options, and documentation lookup based on user input. Here's a step-by-step guide on what the application should accomplish: 1. **Setup**: Begin by installing the 'axiomai-cli' package and setting up a basic Python environment. 2. **Real-Time Suggestions**: Implement a feature that takes a snippet of code as input and returns immediate suggestions for improvement. These could include syntax corrections, best practices, and optimization tips. 3. **Refactoring Tool**: Develop a module within 'CodeSavior' that allows users to input a piece of code and receive a refactored version according to common coding standards and efficiency guidelines. 4. **Documentation Lookup**: Integrate a feature that searches through official documentation based on user queries about specific functions or modules, providing quick access to relevant information. 5. **User Interface**: Create a simple yet intuitive command-line interface (CLI) where users can interact with 'CodeSavior', input their code snippets, and view suggestions/refactorings/documentation results. 6. **Integration Testing**: Ensure that 'CodeSavior' works seamlessly with 'axiomai-cli' by testing various functionalities such as handling different types of code inputs, accuracy of suggestions, effectiveness of refactorings, and speed of documentation retrieval. 7. **Enhancements & Feedback Loop**: After initial deployment, gather feedback from users to identify areas for improvement and potential new features, such as support for multiple programming languages or advanced code analysis tools. Throughout the development process, focus on leveraging 'axiomai-cli' to streamline coding tasks and enhance developer productivity. The goal is to create a tool that not only assists in writing better code but also promotes learning and adherence to best coding practices.
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