审计历史

The static analyzer flagged 185 potential issues across 6 files (1423 lines), but all findings are false positives upon manual review. The overwhelming majority (141 external_commands flags) originate from shell command examples in documentation files (SKILL.md, reference markdowns in references/) that show users how to run the CLI tool, not from executable code executing arbitrary commands. Network connections target only legitimate Semantic Scholar API endpoints (semanticscholar.org), and base64 decode operations are used for parsing server-side rendered data blobs, not for obfuscation. This is a well-documented, transparent skill for Semantic Scholar API interaction with no evidence of malicious intent or data exfiltration.

This is a legitimate research management tool for Semantic Scholar. Static findings flagged 185 potential issues, but manual evaluation confirms all are false positives. The skill uses Python CLI scripts for cookie-based API access, standard file paths (~/.auth/), and base64 encoding for decoding server-side rendered data. No malicious patterns, data exfiltration, or unauthorized credential usage detected.

This is a legitimate academic research tool for interacting with Semantic Scholar APIs. All static findings are false positives: Python subprocess calls use hardcoded command strings with no user injection, hardcoded URLs are all legitimate Semantic Scholar endpoints, filesystem access is to user-specific config directories, and base64 decoding is for legitimate SSR data extraction. The skill uses cookie-based authentication which is a standard pattern for accessing authenticated web APIs.