AI Analysis
Final verdict: SAFE
The package shows moderate risks due to potential shell execution, but no network calls are made and there are no significant metadata red flags. The overall risk is low enough to consider it safe.
- Shell risk due to potential shell execution
- No network activity detected
- Single package from maintainer
Per-check LLM notes
- Network: No network calls detected.
- Shell: Shell execution detected may be for legitimate purposes but requires scrutiny to ensure it does not lead to unauthorized actions.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.
Package Quality Overall: Low (2.0/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (9670 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 2.0
Found 1 shell execution pattern(s)
pe nucl -out {db_folder}' os.system(command) # delete tmp file if temp_fasta.exists():
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: uni-trier.de
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Till-Hendrik Macher" 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 apscale-blast
Create a mini-application called 'Metabarcoder' that leverages the 'apscale-blast' package to analyze DNA metabarcoding data. The app should allow users to upload a FASTA file containing DNA sequences, which will then be processed through BLAST analysis using the 'apscale-blast' package. The results should include taxonomic classifications for each sequence, as well as any relevant metadata extracted from the BLAST database. Additionally, the application should offer features such as filtering results based on similarity scores, exporting results in various formats (e.g., CSV, JSON), and visualizing the distribution of identified taxa. The goal is to provide a user-friendly interface for biologists and researchers to quickly analyze and interpret DNA metabarcoding datasets.
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