Is Argot Lsp Safe?
Argot Lsp — Nerq Trust Score 48.2/100 (D grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-25
Argot Lsp is a Python package with a Nerq Trust Score of 48.2/100 (D), based on 3 independent data dimensions. Last analyzed: 2026-03-25 Security: 90/100. Popularity: 0/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-25. Machine-readable data (JSON).
Is Argot Lsp safe?
Trust Score Breakdown — Argot Lsp has a Nerq Trust Score of 48.2/100 (D). Measured across 2 independent trust signals (as of 2026-03-25).
What is Argot Lsp's trust score?
Argot Lsp has a Nerq Trust Score of 48.2/100, earning a D grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Argot Lsp?
Argot Lsp's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Argot Lsp and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Argot Lsp
What is Argot Lsp?
Argot Lsp is a Python package — Pure Python LSP server and CLI tools for SystemVerilog, powered by pyslang.
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=argot-lsp
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Argot Lsp has a Nerq Trust Score of 48/100 (D). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Argot Lsp has a Trust Score of 48/100 (D).
- The score is a measured composite — it is not a suitability judgment. Evaluate the individual signals against your own requirements.
- Query the current measured values via the Nerq API.
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.