🤖 AI Analysis

Final verdict: SAFE

The package is assessed as safe with a low risk score due to lack of network calls, shell execution, obfuscation, and credential harvesting. The metadata risk is slightly elevated due to the maintainer's limited package history.

No network calls
No shell execution
No obfuscation
No credential harvesting
Maintainer has only one other package

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, indicating no immediate risk from command execution.
Obfuscation: No obfuscation patterns detected, suggesting low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which could indicate a new or less active account.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

Test runner config found: pyproject.toml
1 test file(s) detected (e.g. test_app.py)

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ 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

51 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 100 commits in takumiecd/arctx
Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ 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 takumiecd/arctx appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Takumi Ishida" 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 arctx-tui

Create a fully-functional mini-application named 'Reasoner' that leverages the 'arctx-tui' package to manage and visualize reasoning history and parallel agent work within a textual user interface. The application should enable users to input logical statements or queries, which will be processed and stored as nodes in an append-only Directed Acyclic Graph (DAG). Each node represents a piece of reasoning or an agent's work, and it should be possible to trace back the reasoning process from any given node to its predecessors.

Key Features:
1. User Interface: Implement a clean, intuitive TUI using 'arctx-tui' that allows users to add new nodes (representing reasoning steps or tasks), view the entire DAG, and navigate through it.
2. Input Handling: Users should be able to input new logical statements or queries. These inputs will be parsed and added as nodes to the DAG.
3. Node Navigation: Provide functionality to move forward and backward through the DAG, allowing users to explore the reasoning process.
4. Visualization: Display the structure of the DAG in a visually appealing way, highlighting connections between nodes.
5. Persistence: Ensure that the DAG data is saved locally so that the application can resume where it left off after closing and reopening.
6. Agent Work Management: Allow for the creation of multiple agents, each contributing to the DAG independently but interconnectedly.

Steps to Build:
1. Set up a Python environment with 'arctx-tui' installed.
2. Design the overall structure of your DAG and how it will be represented in the TUI.
3. Develop the core logic for adding, navigating, and visualizing nodes in the DAG.
4. Integrate user input handling and persistence features.
5. Extend the application to support multiple agents and their contributions.
6. Test the application thoroughly to ensure all features work as expected.