AI Analysis
Final verdict: SUSPICIOUS
The package exhibits moderate obfuscation practices and has low maintainer activity, raising concerns about its transparency and maintenance.
- Moderate obfuscation risk due to base64 encoding and gzip compression
- Low maintainer activity and poor metadata quality
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell execution patterns detected, indicating no immediate risk of executing system commands.
- Obfuscation: The observed pattern suggests data obfuscation through base64 encoding followed by gzip compression, which could be used for various legitimate purposes but also might indicate an attempt to hide code or data.
- Credentials: No clear signs of credential harvesting are present based on the provided snippet.
- Metadata: The package shows signs of low maintainer activity and poor metadata quality, but lacks clear indicators of malicious intent.
Package Quality Overall: Medium (6.2/10)
✦ High
Test Suite
9.0
Test suite present — 2 test file(s) found
Test runner config found: pyproject.toml2 test file(s) detected (e.g. test_autocompact_sqlite.py)
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://docs.axio-agent.comDetailed PyPI description (6126 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
31 type-annotated function signatures detected in source
✦ High
Multiple Contributors
8.0
Active multi-contributor project
3 unique contributor(s) across 100 commits in mosquito/axio-agentSmall but multi-author team (3–4 contributors)
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
return gzip.decompress(base64.b64decode(data[5:])).decode() if data.startswith("plain:"):
Shell / Subprocess Execution
No shell execution patterns detected
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
Repository mosquito/axio-agent appears legitimate
Maintainer History
score 6.0
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)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with axio-context-sqlite
Develop a simple task management application using Python that leverages the 'axio-context-sqlite' package for storing and managing user contexts related to their tasks. This application will allow users to create, update, delete, and view tasks while keeping track of their progress and associated notes. The application should have a command-line interface (CLI) for easy interaction. ### Core Features: 1. **Task Creation**: Users should be able to add new tasks with a title, description, due date, and priority level. 2. **Task Updates**: Tasks can be updated with new information, including changing their status (e.g., from 'in progress' to 'completed'). 3. **Task Deletion**: Users must be able to remove tasks they no longer need. 4. **View Tasks**: Display all tasks sorted by due date or priority level. 5. **Context Management**: Utilize 'axio-context-sqlite' to manage the context of each task, such as the current user's working state on a task (e.g., last edited time, current section being worked on). ### Additional Features: - Implement a feature to mark tasks as completed or incomplete. - Allow users to search for tasks based on keywords within the title or description. - Integrate a simple logging mechanism to track changes made to tasks over time. ### Implementation Steps: 1. Set up a Python environment and install necessary packages, including 'axio-context-sqlite'. 2. Design the database schema using SQLite to store tasks and their contexts. 3. Create functions to handle CRUD operations (Create, Read, Update, Delete) for tasks. 4. Implement the context management functionality using 'axio-context-sqlite', ensuring it stores relevant data about the user's interaction with tasks. 5. Develop a CLI to interact with the application, allowing users to perform various actions like adding, updating, deleting, and viewing tasks. 6. Test the application thoroughly to ensure all features work as expected and that the context management is accurately reflecting user interactions. 7. Document the code and provide usage instructions for other developers or end-users.
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