Is Ascii Colors Safe?
Ascii Colors — Nerq Trust Score 69.2/100 (B- grade). Based on analysis of 2 trust dimensions, it is generally safe but has some concerns. Last updated: 2026-05-09.
Use Ascii Colors with some caution. Ascii Colors is a Python package with a Nerq Trust Score of 69.2/100 (B-), based on 3 independent data dimensions. Below the recommended threshold of 70. Security: 90/100. Popularity: 60/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-21. Machine-readable data (JSON).
Is Ascii Colors safe?
CAUTION — Ascii Colors has a Nerq Trust Score of 69.2/100 (B-). It has moderate trust signals but shows some areas of concern that warrant attention. Suitable for development use — review security and maintenance signals before production deployment.
What is Ascii Colors's trust score?
Ascii Colors has a Nerq Trust Score of 69.2/100, earning a B- grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Ascii Colors?
Ascii Colors's strongest signal is security at 90/100. No known vulnerabilities have been detected. It has not yet reached the Nerq Verified threshold of 70+.
What is Ascii Colors and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
Similar Pypi by Trust Score
Safety Guide: Ascii Colors
What is Ascii Colors?
Ascii Colors is a Python package — A Python library for rich terminal output with advanced logging features..
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=ascii-colors
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Trust Assessment
Ascii Colors has a Nerq Trust Score of 69/100 (B-) and has not yet reached Nerq trust threshold (70+). This score is based on automated analysis of security, maintenance, community, and quality signals.
Key Takeaways
- Ascii Colors has a Trust Score of 69/100 (B-).
- Review carefully before use — below trust threshold.
- Always verify independently using the Nerq API.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Maintenance | 98/100 |
| Popularity | 60/100 |
| Quality | 40/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Ascii Colors collect?
Privacy assessment for Ascii Colors is not yet available. See our methodology for how Nerq measures privacy, or the public privacy review for any community-contributed notes.
Is Ascii Colors secure?
Security score: 90/100. Ascii Colors has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
License information not available. Open-source packages allow independent security review of the source code.
Run your package manager's audit command (`npm audit`, `pip audit`, `cargo audit`) to check for known vulnerabilities in your dependency tree.
Full analysis: Ascii Colors Security Report
How we calculated this score
Ascii Colors's trust score of 69.2/100 (B-) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (98/100), popularity (60/100), quality (40/100), community (35/100). Each dimension is weighted equally to produce the composite trust score.
Nerq analyzes over 7.5 million entities across 26 registries using the same methodology, enabling direct cross-entity comparison. Scores are updated continuously as new data becomes available.
This page was last reviewed on May 09, 2026. Data version: 0.0.
Full methodology documentation · Machine-readable data (JSON API)
Frequently Asked Questions
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Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.