Ist Deep Research Skills sicher?

Ist Deep Research Skills sicher?

Vertrauensbewertung im Detail — Deep Research Skills has a Nerq Trust Score of 63.1/100 (C). Measured across 5 independent trust signals.

What Is Deep Research Skills?

Deep Research Skills is a software tool in the research category: Automated systematic literature review agent-skill for OpenCode with consensus quantification and gap analysis. It has 1 GitHub-Sternen. Nerq Trust Score: 63/100 (C).

Nerq independently analyzes every software tool, app, and extension across multiple trust signals including Sicherheit vulnerabilities, Wartung activity, license Konformität, and Community-Akzeptanz.

How Nerq Assesses Deep Research Skills's Safety

Nerq's Trust Score is calculated from 13+ independent signals aggregated into five Dimensionen. Here is how Deep Research Skills performs in each:

• Sicherheit (0/100): Deep Research Skills's Sicherheit posture is poor. This score factors in known CVEs, dependency vulnerabilities, Sicherheit policy presence, and code signing practices.
• Wartung (1/100): Deep Research Skills is potentially abandoned. We track commit frequency, release cadence, issue response times, and PR merge rates.
• Documentation (1/100): Documentation quality is insufficient. This includes README completeness, API Dokumentation, usage examples, and contribution guidelines.
• Compliance (100/100): Deep Research Skills is broadly compliant. Assessed against regulations in 52 jurisdictions including the EU AI Act, CCPA, and GDPR.
• Community (0/100): Community adoption is limited. Basierend auf GitHub-Sternen, forks, download counts, and ecosystem integrations.

The overall Trust Score of 63.1/100 (C) is the weighted combination of these measured signals. It is a measurement, not a pass/fail or suitability judgment — weigh the individual signals against your own requirements.

Who Typically Evaluates Deep Research Skills?

Deep Research Skills is commonly evaluated by:

• Developers and teams working with research tools
• Organizations evaluating AI tools for their stack
• Researchers exploring AI capabilities in this domain

How to read the signals: Deep Research Skills's measured signals (Sicherheit 0/100, Wartung 1/100, Dokumentation 1/100, community 0/100) are shown above. These are measurements, not a suitability judgment — weigh each signal against the requirements of your own use case and risk tolerance.

How to Verify Deep Research Skills's Safety Yourself

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

1. Check the source code — Überprüfen Sie das/die repository's Sicherheit policy, open issues, and recent commits for signs of active Wartung.
2. Scan dependencies — Use tools like npm audit, pip-audit, or snyk to check for known vulnerabilities in Deep Research Skills's dependency tree.
3. Bewertung permissions — Understand what access Deep Research Skills requires. Software tools should follow the principle of least privilege.
4. Test in isolation — Run Deep Research Skills 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=deep-research-skills
6. Überprüfen Sie das/die license — Confirm that Deep Research Skills'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 Sicherheit concerns openly. Low community engagement may indicate limited peer review of the codebase.

Common Safety Concerns with Deep Research Skills

When evaluating whether Deep Research Skills is safe, consider these category-specific risks:

Deep Research Skills and the EU AI Act

Deep Research Skills 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 Konformität assessment covers 52 jurisdictions worldwide. For organizations deploying AI tools in regulated environments, understanding these classifications is essential for legal Konformität.

Best Practices for Using Deep Research Skills Safely

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

Situations That Warrant Independent Review of Deep Research Skills

Nerq's signals are one input. In the following situations, evaluate Deep Research Skills's measured signals against your own requirements before making a decision:

• Environments handling sensitive or regulated data (healthcare, finance, government)
• Mission-critical systems where downtime has significant business impact
• Deployments with strict regulatory requirements that must be independently validated

For each situation, compare Deep Research Skills's measured trust score of 63.1/100 and its individual signals against your organization's own criteria. Nerq does not assert whether Deep Research Skills is suitable for any particular use.

How Deep Research Skills Compares to Industry Standards

Nerq indexes over 6 million software tools, apps, and packages across dozens of categories. Among research tools, the average Trust Score is 62/100. Deep Research Skills's score of 63.1/100 is above the category average of 62/100.

This positions Deep Research Skills favorably among research tools. While it outperforms the average, there is still room for improvement in certain trust Dimensionen.

Industry benchmarks matter because they contextualize a tool's safety profile. A score that looks moderat 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 Deep Research Skills 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 Wartung patterns change, Deep Research Skills'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 Sicherheit and quality. Conversely, a downward trend may signal reduced Wartung, growing technical debt, or unresolved vulnerabilities. To track Deep Research Skills's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=deep-research-skills&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 — Sicherheit, Wartung, Dokumentation, Konformität, and community — has evolved independently, providing granular visibility into which aspects of Deep Research Skills are strengthening or weakening over time.

Deep Research Skills vs Alternativen

In the research category, Deep Research Skills scores 63.1/100. There are higher-scoring alternatives available. For a detailed comparison, see:

• Deep Research Skills vs gpt_academic — Trust Score: 60.9/100
• Deep Research Skills vs LlamaFactory — Trust Score: 79.7/100
• Deep Research Skills vs unsloth — Trust Score: 77.2/100

Wichtigste Punkte

• Deep Research Skills has a measured Nerq Trust Score of 63.1/100 (C) — a composite of independent signals, not a suitability judgment.
• Among research tools, Deep Research Skills scores above the category average of 62/100 (a positional measurement relative to peers).
• The individual signals — Sicherheit, Wartung, Dokumentation, Konformität, community — are shown above. Weigh them against your own requirements.
• Query the current measured values via Nerq's Preflight API.