ase-qsm

v0.2.3 suspicious
4.0
Medium Risk

QSM-style chain-of-states path optimizer for ASE with tangent-smoothed chain updates, climbing-image saddle search, adaptive redistribution, and SCF-failure resilience.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network calls, shell execution, obfuscation, and credential harvesting. However, the metadata risk score of 7 due to suspicious low activity and a single contributor raises concerns about potential supply-chain risks.

  • Suspiciously low activity and single contributor increase suspicion.
  • No immediate malicious activities detected, but potential supply-chain attack cannot be ruled out.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no suspicious system command executions.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Suspicious low activity and single contributor indicate potential risk.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: pyproject.toml
  • 6 test file(s) detected (e.g. test_basic.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7436 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 203 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 1 commits in ss0832/ase_qsm
  • Single author with few commits — possibly a personal or throwaway project

🔬 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 score 7.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Very few commits: 1 total
  • Single contributor with only 1 commit(s) — possibly throwaway account
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "ss0832" 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 ase-qsm 
Develop a molecular dynamics simulation tool that leverages the 'ase-qsm' package to optimize reaction pathways in chemical systems. This tool will allow users to input molecular structures and conditions, then simulate the most probable reaction pathways using advanced optimization techniques provided by 'ase-qsm'. The application should include the following key features:

1. **User Interface**: A simple, intuitive interface where users can input initial and final molecular states.
2. **Path Optimization**: Utilize 'ase-qsm' to find the lowest energy pathway between the initial and final states, employing its chain-of-states method with tangent smoothing and climbing image techniques.
3. **Adaptive Redistibution**: Implement the adaptive redistribution feature of 'ase-qsm' to refine the pathway optimization iteratively, ensuring the most efficient route is found.
4. **SCF Failure Handling**: Incorporate resilience against SCF failures during simulations, utilizing 'ase-qsm's adaptive strategies to continue simulations seamlessly.
5. **Visualization**: Provide visual representations of the optimized pathways, showing energy profiles and molecular configurations along the path.
6. **Output Analysis**: Offer detailed analysis of the simulation results, including energy barriers, transition states, and other critical points along the reaction pathway.

The application should guide users through setting up their simulations, executing them using 'ase-qsm', and interpreting the results, making it a comprehensive tool for studying chemical reactions.

💬 Discussion Feed

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