Is Mathlens Safe?
Mathlens — Nerq Trust Score 47.0/100 (D grade). Based on analysis of 3 trust dimensions, it is has notable safety concerns. Last updated: 2026-04-27.
Exercise caution with Mathlens. Mathlens is a software tool with a Nerq Trust Score of 47.0/100 (D), based on 3 independent data dimensions. Below the recommended threshold of 70. Maintenance: 0/100. Popularity: 1/100. Data sourced from multiple public sources including package registries, GitHub, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-04-27. Machine-readable data (JSON).
Is Mathlens safe?
NO — USE WITH CAUTION — Mathlens has a Nerq Trust Score of 47.0/100 (D). It has below-average trust signals with significant gaps in security, maintenance, or documentation. Not recommended for production use without thorough manual review and additional security measures.
What is Mathlens's trust score?
Mathlens has a Nerq Trust Score of 47.0/100, earning a D grade. This score is based on 3 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Mathlens?
Mathlens's strongest signal is popularity at 1/100. No known vulnerabilities have been detected. It has not yet reached the Nerq Verified threshold of 70+.
What is Mathlens and who maintains it?
| Author | shuyicc |
| Category | Education |
| Stars | 186 |
| Source | https://github.com/shuyicc/MathLens |
Popular Alternatives in education
What Is Mathlens?
Mathlens is a software tool in the education category: MathLens 是一个专注于数学题目的自动讲解 Agent Skill。. It has 186 GitHub stars. Nerq Trust Score: 47/100 (D).
Nerq independently analyzes every software tool, app, and extension across multiple trust signals including security vulnerabilities, maintenance activity, license compliance, and community adoption.
How Nerq Assesses Mathlens's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensions. Here is how Mathlens performs in each:
- Maintenance (0/100): Mathlens is potentially abandoned. We track commit frequency, release cadence, issue response times, and PR merge rates.
- Documentation (0/100): Documentation quality is insufficient. This includes README completeness, API documentation, usage examples, and contribution guidelines.
- Community (1/100): Community adoption is limited. Based on GitHub stars, forks, download counts, and ecosystem integrations.
The overall Trust Score of 47.0/100 (D) reflects the weighted combination of these signals. This is below the Nerq Verified threshold of 70. We recommend additional due diligence before production deployment.
Who Should Use Mathlens?
Mathlens is designed for:
- Developers and teams working with education tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
Risk guidance: We recommend caution with Mathlens. The low trust score suggests potential risks in security, maintenance, or community support. Consider using a more established alternative for any production or sensitive workload.
How to Verify Mathlens's Safety Yourself
While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:
- Check the source code — Review the repository's security policy, open issues, and recent commits for signs of active maintenance.
- Scan dependencies — Use tools like
npm audit,pip-audit, orsnykto check for known vulnerabilities in Mathlens's dependency tree. - Review permissions — Understand what access Mathlens requires. Software tools should follow the principle of least privilege.
- Test in isolation — Run Mathlens in a sandboxed environment before granting access to production data or systems.
- Monitor continuously — Use Nerq's API to set up automated trust checks:
GET nerq.ai/v1/preflight?target=shuyicc/MathLens - Review the license — Confirm that Mathlens's license is compatible with your intended use case. Pay attention to restrictions on commercial use, redistribution, and derivative works. Some AI tools use dual licensing or have separate terms for enterprise customers that differ from the open-source license.
- Check community signals — Look at the project's issue tracker, discussion forums, and social media presence. A healthy community actively reports bugs, contributes fixes, and discusses security concerns openly. Low community engagement may indicate limited peer review of the codebase.
Common Safety Concerns with Mathlens
When evaluating whether Mathlens is safe, consider these category-specific risks:
Understand how Mathlens processes, stores, and transmits your data. Review the tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Mathlens's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher security risk.
Regularly check for updates to Mathlens. Security patches and bug fixes are only effective if you're running the latest version.
If Mathlens connects to external APIs or services, each integration point is a potential attack surface. Audit all third-party connections, verify that data shared with external services is minimized, and ensure that integration credentials are rotated regularly.
Verify that Mathlens's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Mathlens in violation of its license can expose your organization to legal liability.
Best Practices for Using Mathlens Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Mathlens while minimizing risk:
Periodically review how Mathlens is used in your workflow. Check for unexpected behavior, permissions drift, and compliance with your security policies.
Ensure Mathlens and all its dependencies are running the latest stable versions to benefit from security patches.
Grant Mathlens only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Mathlens's security advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Mathlens is used within your organization, including data handling guidelines and acceptable use cases.
When Should You Avoid Mathlens?
Even promising tools aren't right for every situation. Consider avoiding Mathlens in these scenarios:
- Production environments handling sensitive customer data
- Regulated industries (healthcare, finance, government) without additional compliance review
- Mission-critical systems where downtime has significant business impact
For each scenario, evaluate whether Mathlens's trust score of 47.0/100 meets your organization's risk tolerance. We recommend running a manual security assessment alongside the automated Nerq score.
How Mathlens Compares to Industry Standards
Nerq indexes over 6 million software tools, apps, and packages across dozens of categories. Among education tools, the average Trust Score is 62/100. Mathlens's score of 47.0/100 is below the category average of 62/100.
This suggests that Mathlens trails behind many comparable education tools. Organizations with strict security requirements should evaluate whether higher-scoring alternatives better meet their needs.
Industry benchmarks matter because they contextualize a tool's safety profile. A score that looks moderate in isolation may actually represent strong performance within a challenging category — or vice versa. Nerq's category-relative analysis helps teams make informed decisions by showing not just absolute quality, but how a tool ranks against its direct peers.
Trust Score History
Nerq continuously monitors Mathlens and recalculates its Trust Score as new data becomes available. Our scoring engine ingests real-time signals from source repositories, vulnerability databases (NVD, OSV.dev), package registries, and community metrics. When a new CVE is published, a major release ships, or maintenance patterns change, Mathlens's score is updated within 24 hours.
Historical trust trends reveal whether a tool is improving, stable, or declining over time. A tool that consistently maintains or improves its score demonstrates ongoing commitment to security and quality. Conversely, a downward trend may signal reduced maintenance, growing technical debt, or unresolved vulnerabilities. To track Mathlens's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=shuyicc/MathLens&include=history
Nerq retains trust score snapshots at regular intervals, enabling trend analysis across weeks and months. Enterprise users can access detailed historical reports showing how each dimension — security, maintenance, documentation, compliance, and community — has evolved independently, providing granular visibility into which aspects of Mathlens are strengthening or weakening over time.
Mathlens vs Alternatives
In the education category, Mathlens scores 47.0/100. There are higher-scoring alternatives available. For a detailed comparison, see:
- Mathlens vs Mr.-Ranedeer-AI-Tutor — Trust Score: 58.8/100
- Mathlens vs hello-agents — Trust Score: 63.3/100
- Mathlens vs owl — Trust Score: 68.4/100
Key Takeaways
- Mathlens has a Trust Score of 47.0/100 (D) and is not yet Nerq Verified.
- Mathlens has significant trust gaps. Consider higher-rated alternatives unless specific requirements mandate its use.
- Among education tools, Mathlens scores below the category average of 62/100, suggesting room for improvement relative to peers.
- Always verify safety independently — use Nerq's Preflight API for automated, up-to-date trust checks before integration.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Maintenance | 0/100 |
| Popularity | 1/100 |
Based on 2 dimensions. Data from multiple public sources including package registries, GitHub, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Mathlens collect?
Privacy assessment for Mathlens is not yet available. See our methodology for how Nerq measures privacy, or the public privacy review for any community-contributed notes.
Is Mathlens secure?
Security score: under assessment. Review security practices and consider alternatives with higher security scores for sensitive use cases.
Nerq monitors this entity against NVD, OSV.dev, and registry-specific vulnerability databases for ongoing security assessment.
Full analysis: Mathlens Security Report
How we calculated this score
Mathlens's trust score of 47.0/100 (D) is computed from multiple public sources including package registries, GitHub, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 2 independent dimensions: maintenance (0/100), popularity (1/100). 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 April 27, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON API)
Frequently Asked Questions
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See Also
Disclaimer: Nerq trust scores are automated assessments based on publicly available signals. They are not endorsements or guarantees. Always conduct your own due diligence.