Is Aiml Bot Safe?
Aiml Bot — Nerq Trust Score 54.0/100 (C- grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-20
Aiml Bot is a Python package with a Nerq Trust Score of 54.0/100 (C-), based on 3 independent data dimensions. Last analyzed: 2026-03-20 Security: 90/100. Popularity: 0/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-20. Machine-readable data (JSON).
Is Aiml Bot safe?
Trust Score Breakdown — Aiml Bot has a Nerq Trust Score of 54.0/100 (C-). Measured across 2 independent trust signals (as of 2026-03-20).
What is Aiml Bot's trust score?
Aiml Bot has a Nerq Trust Score of 54.0/100, earning a C- grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Aiml Bot?
Aiml Bot's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Aiml Bot and who maintains it?
| Author | Aaron Hosford |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Aiml Bot
What is Aiml Bot?
Aiml Bot is a Python package — An interpreter package for AIML, the Artificial Intelligence Markup Language.
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=AIML-Bot
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Aiml Bot has a Nerq Trust Score of 54/100 (C-). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Aiml Bot has a Trust Score of 54/100 (C-).
- 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.