Is Ccclodash Safe?
Ccclodash — Nerq Trust Score 48.2/100 (D grade). Score based on 2 independent trust signals. Last analyzed: 2026-03-25
Ccclodash is a npm 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 npm registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-03-25. Machine-readable data (JSON).
Is Ccclodash safe?
Trust Score Breakdown — Ccclodash has a Nerq Trust Score of 48.2/100 (D). Measured across 2 independent trust signals (as of 2026-03-25).
What is Ccclodash's trust score?
Ccclodash 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 Ccclodash?
Ccclodash's strongest signal is security at 90/100. No known vulnerabilities have been detected.
What is Ccclodash and who maintains it?
| Author | ccckmit |
| Category | npm Packages |
| Source | N/A |
Ccclodash Across Platforms
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Safety Guide: Ccclodash
What is Ccclodash?
Ccclodash is a Node.js package — Reimplement some function of lodash.
How to Verify Safety
Run npm audit to check for vulnerabilities. Review the package's GitHub repository for recent commits.
You can also check the trust score via API: GET /v1/preflight?target=ccclodash
Key Safety Concerns for Node.js package
When evaluating any Node.js package, watch for: dependency vulnerabilities, malicious packages, typosquatting.
Measured Signals
Ccclodash 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
- Ccclodash 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.