Fellowship Code ปลอดภัยหรือไม่?

Fellowship Code — Nerq Trust Score 74.8/100 (เกรด B). จากการวิเคราะห์ 5 มิติความน่าเชื่อถือ ถือว่าโดยทั่วไปปลอดภัยแต่มีข้อกังวลบางประการ อัปเดตล่าสุด: 2026-04-02

ใช่ Fellowship Code ปลอดภัยที่จะใช้งาน Fellowship Code is a software tool ด้วยคะแนนความน่าเชื่อถือ Nerq 74.8/100 (B), based on 5 independent data dimensions. It is recommended for use. Security: 0/100. Maintenance: 1/100. Popularity: 0/100. Data sourced from multiple public sources including package registries, GitHub, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-04-02. ข้อมูลที่เครื่องอ่านได้ (JSON).

Fellowship Code ปลอดภัยหรือไม่?

ใช่ — Fellowship Code มีคะแนนความน่าเชื่อถือ Nerq 74.8/100 (B). ผ่านเกณฑ์ความน่าเชื่อถือของ Nerq ด้วยสัญญาณที่แข็งแกร่งในด้านความปลอดภัย การบำรุงรักษา และการยอมรับจากชุมชน. Recommended for use — ดูรายงานฉบับเต็มด้านล่างสำหรับข้อพิจารณาเฉพาะ.

การวิเคราะห์ความปลอดภัย → รายงานความเป็นส่วนตัวของ {name} →

คะแนนความน่าเชื่อถือของ Fellowship Code คือเท่าไร?

Fellowship Code มีคะแนนความน่าเชื่อถือ Nerq 74.8/100 ได้เกรด B คะแนนนี้อิงจาก 5 มิติที่วัดอย่างอิสระ

ความปลอดภัย
0
การปฏิบัติตามกฎระเบียบ
100
การบำรุงรักษา
1
เอกสาร
1
ความนิยม
0

ผลการตรวจสอบความปลอดภัยหลักของ Fellowship Code คืออะไร?

สัญญาณที่แข็งแกร่งที่สุดของ Fellowship Code คือ การปฏิบัติตามกฎระเบียบ ที่ 100/100 ไม่พบช่องโหว่ที่ทราบ ผ่านเกณฑ์ Nerq Verified 70+

คะแนนความปลอดภัย: 0/100 (weak)
Maintenance: 1/100 — low maintenance activity
Compliance: 100/100 — covers 52 of 52 jurisdictions
Documentation: 1/100 — limited documentation
Popularity: 0/100 — community adoption

Fellowship Code คืออะไรและใครเป็นผู้ดูแล?

ผู้พัฒนาendaoment
หมวดหมู่coding
แหล่งที่มาhttps://github.com/endaoment/fellowship-code
Frameworksanthropic

การปฏิบัติตามกฎระเบียบ

EU AI Act Risk ClassMINIMAL
Compliance Score100/100
JurisdictionsAssessed across 52 jurisdictions

ทางเลือกยอดนิยมใน coding

Significant-Gravitas/AutoGPT
74.7/100 · B
github
ollama/ollama
73.8/100 · B
github
langchain-ai/langchain
86.4/100 · A
github
x1xhlol/system-prompts-and-models-of-ai-tools
73.8/100 · B
github
anomalyco/opencode
87.9/100 · A
github

What Is Fellowship Code?

Fellowship Code is a software tool in the coding category: An open-source multi-agent AI engineering team for Cursor and Claude Code.. Nerq Trust Score: 75/100 (B).

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 Fellowship Code's Safety

Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensions. Here is how Fellowship Code performs in each:

The overall Trust Score of 74.8/100 (B) reflects the weighted combination of these signals. This exceeds the Nerq Verified threshold of 70, indicating the tool meets our standards for production use.

Who Should Use Fellowship Code?

Fellowship Code is designed for:

Risk guidance: Fellowship Code meets the minimum threshold for production use, but we recommend monitoring for security advisories and keeping dependencies up to date. Consider implementing additional guardrails for sensitive workloads.

How to Verify Fellowship Code's Safety Yourself

While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:

  1. Check the source code — Review the repository's security policy, open issues, and recent commits for signs of active maintenance.
  2. Scan dependencies — Use tools like npm audit, pip-audit, or snyk to check for known vulnerabilities in Fellowship Code's dependency tree.
  3. รีวิว permissions — Understand what access Fellowship Code requires. Software tools should follow the principle of least privilege.
  4. Test in isolation — Run Fellowship Code in a sandboxed environment before granting access to production data or systems.
  5. Monitor continuously — Use Nerq's API to set up automated trust checks: GET nerq.ai/v1/preflight?target=fellowship-code
  6. ตรวจสอบ license — Confirm that Fellowship Code'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.
  7. 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 Fellowship Code

When evaluating whether Fellowship Code is safe, consider these category-specific risks:

Data handling

Understand how Fellowship Code processes, stores, and transmits your data. Review the tool's privacy policy and data retention practices, especially for sensitive or proprietary information.

Dependency security

Check Fellowship Code's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher security risk.

Update frequency

Regularly check for updates to Fellowship Code. Security patches and bug fixes are only effective if you're running the latest version.

Third-party integrations

If Fellowship Code 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.

License and IP compliance

Verify that Fellowship Code's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Fellowship Code in violation of its license can expose your organization to legal liability.

Fellowship Code and the EU AI Act

Fellowship Code is classified as Minimal Risk under the EU AI Act. This is the lowest risk category, meaning it faces minimal regulatory requirements. However, transparency obligations still apply.

Nerq's compliance assessment covers 52 jurisdictions worldwide. For organizations deploying AI tools in regulated environments, understanding these classifications is essential for legal compliance.

Best Practices for Using Fellowship Code Safely

Whether you're an individual developer or an enterprise team, these practices will help you get the most from Fellowship Code while minimizing risk:

Conduct regular audits

Periodically review how Fellowship Code is used in your workflow. Check for unexpected behavior, permissions drift, and compliance with your security policies.

Keep dependencies updated

Ensure Fellowship Code and all its dependencies are running the latest stable versions to benefit from security patches.

Follow least privilege

Grant Fellowship Code only the minimum permissions it needs to function. Avoid granting admin or root access.

Monitor for security advisories

Subscribe to Fellowship Code's security advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.

Document usage policies

Create and maintain a clear policy for how Fellowship Code is used within your organization, including data handling guidelines and acceptable use cases.

When Should You Avoid Fellowship Code?

Even well-trusted tools aren't right for every situation. Consider avoiding Fellowship Code in these scenarios:

คะแนนความน่าเชื่อถือของ

For each scenario, evaluate whether Fellowship Code 74.8/100 meets your organization's risk tolerance. The Nerq Verified status indicates general production readiness, but sector-specific requirements may apply.

How Fellowship Code Compares to Industry Standards

Nerq indexes over 6 million software tools, apps, and packages across dozens of categories. Among coding tools, the average Trust Score is 62/100. Fellowship Code's score of 74.8/100 is significantly above the category average of 62/100.

This places Fellowship Code in the top tier of coding tools that Nerq tracks. Tools scoring this far above average typically demonstrate mature security practices, consistent release cadence, and broad community adoption.

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 Fellowship Code 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, Fellowship Code'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 Fellowship Code's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=fellowship-code&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 Fellowship Code are strengthening or weakening over time.

Fellowship Code vs Alternatives

ในหมวดหมู่ coding, Fellowship Code ได้คะแนน 74.8/100 There are higher-scoring alternatives available. For a detailed comparison, see:

ประเด็นสำคัญ

คำถามที่พบบ่อย

Fellowship Code ปลอดภัยที่จะใช้งานหรือไม่?
ใช่ ปลอดภัยที่จะใช้งาน fellowship-code มีคะแนนความน่าเชื่อถือ Nerq 74.8/100 (B). สัญญาณที่แข็งแกร่งที่สุด: การปฏิบัติตามกฎระเบียบ (100/100). คะแนนอิงจาก security (0/100), maintenance (1/100), popularity (0/100), documentation (1/100).
คะแนนความน่าเชื่อถือของ
Fellowship Code คือเท่าไร?
fellowship-code: 74.8/100 (B). คะแนนอิงจาก: security (0/100), maintenance (1/100), popularity (0/100), documentation (1/100). Compliance: 100/100. คะแนนจะอัปเดตเมื่อมีข้อมูลใหม่ API: GET nerq.ai/v1/preflight?target=fellowship-code
ทางเลือกที่ปลอดภัยกว่า Fellowship Code มีอะไรบ้าง?
ในหมวดหมู่ coding, ทางเลือกที่มีคะแนนสูงกว่าได้แก่ Significant-Gravitas/AutoGPT (75/100), ollama/ollama (74/100), langchain-ai/langchain (86/100). fellowship-code ได้คะแนน 74.8/100
How often is Fellowship Code's safety score updated?
Nerq continuously monitors Fellowship Code and updates its trust score as new data becomes available. Data sourced from multiple public sources including package registries, GitHub, NVD, OSV.dev, and OpenSSF Scorecard. Current: 74.8/100 (B), last verified 2026-04-02. API: GET nerq.ai/v1/preflight?target=fellowship-code
ฉันสามารถใช้ Fellowship Code ในสภาพแวดล้อมที่มีการควบคุมหรือไม่?
Yes — Fellowship Code meets the Nerq Verified threshold (70+). Combine this with your internal security review for regulated deployments.
API: /v1/preflight Trust Badge API Docs

Disclaimer: คะแนนความน่าเชื่อถือของ Nerq เป็นการประเมินอัตโนมัติจากสัญญาณที่เปิดเผยต่อสาธารณะ ไม่ใช่คำแนะนำหรือการรับประกัน กรุณาตรวจสอบด้วยตนเองเสมอ

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