Är Deepthinklite Skill säker?
Deepthinklite Skill — Nerq Trust Score 70.3/100 (Betyg B). Baserat på analys av 5 tillitsdimensioner bedöms det som generellt säkert men med vissa farhågor. Senast uppdaterad: 2026-04-23.
Ja, Deepthinklite Skill är säker att använda. Deepthinklite Skill är en programvara med ett Nerq-förtroendepoäng på 70.3/100 (B), baserat på 5 oberoende datadimensioner. Rekommenderas för användning. Säkerhet: 0/100. Underhåll: 1/100. Popularitet: 0/100. Data hämtad från flera offentliga källor inklusive paketregister, GitHub, NVD, OSV.dev och OpenSSF Scorecard. Senast uppdaterad: 2026-04-23. Maskinläsbar data (JSON).
Är Deepthinklite Skill säker?
YES — Deepthinklite Skill has a Nerq Trust Score of 70.3/100 (B). Uppfyller Nerqs förtroendetröskel med starka signaler inom säkerhet, underhåll och communityanvändning. Rekommenderas för användning — se hela rapporten nedan för specifika överväganden.
Vad är Deepthinklite Skills förtroendepoäng?
Deepthinklite Skill har ett Nerq-förtroendepoäng på 70.3/100 med betyget B. Denna poäng baseras på 5 oberoende mätta dimensioner inklusive säkerhet, underhåll och communityanvändning.
Vilka är de viktigaste säkerhetsresultaten för Deepthinklite Skill?
Deepthinklite Skills starkaste signal är regelefterlevnad på 90/100. Inga kända sårbarheter har upptäckts. Uppfyller Nerqs verifieringströskel på 70+.
Vad är Deepthinklite Skill och vem underhåller det?
| Utvecklare | VirajSanghvi1 |
| Kategori | Research |
| Källa | https://github.com/VirajSanghvi1/deepthinklite-skill |
Regelefterlevnad
| EU AI Act Risk Class | MINIMAL |
| Compliance Score | 90/100 |
| Jurisdiktions | Assessed across 52 jurisdiktions |
Populära alternativ inom research
What Is Deepthinklite Skill?
Deepthinklite Skill is a programvara in the research category: DeepthinkLite OpenClaw skill for structured deep research.. Nerq Trust Score: 70/100 (B).
Nerq independently analyzes every programvara, app, and extension across multiple trust signals including säkerhet vulnerabilities, underhåll activity, license regelefterlevnad, and communityanvändning.
How Nerq Assesses Deepthinklite Skill's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensioner. Here is how Deepthinklite Skill performs in each:
- Säkerhet (0/100): Deepthinklite Skill's säkerhet posture is poor. This score factors in known CVEs, dependency vulnerabilities, säkerhet policy presence, and code signing practices.
- Underhåll (1/100): Deepthinklite Skill 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 dokumentation, usage examples, and contribution guidelines.
- Compliance (90/100): Deepthinklite Skill is broadly compliant. Assessed against regulations in 52 jurisdiktions including the EU AI Act, CCPA, and GDPR.
- Community (0/100): Community adoption is limited. Baserad på GitHub-stjärnor, forks, download counts, and ecosystem integrations.
The overall Trust Score of 70.3/100 (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 Deepthinklite Skill?
Deepthinklite Skill is designed for:
- Developers and teams working with research tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
Risk guidance: Deepthinklite Skill meets the minimum threshold for production use, but we recommend monitoring for säkerhet advisories and keeping dependencies up to date. Consider implementing additional guardrails for sensitive workloads.
How to Verify Deepthinklite Skill's Safety Yourself
While Nerq provides automated trust analysis, we recommend these additional steps before adopting any programvara:
- Check the source code — Granska repository's säkerhet policy, open issues, and recent commits for signs of active underhåll.
- Scan dependencies — Use tools like
npm audit,pip-audit, orsnykto check for known vulnerabilities in Deepthinklite Skill's dependency tree. - Recension permissions — Understand what access Deepthinklite Skill requires. Software tools should follow the principle of least privilege.
- Test in isolation — Run Deepthinklite Skill 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=deepthinklite-skill - Granska license — Confirm that Deepthinklite Skill'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 säkerhet concerns openly. Low community engagement may indicate limited peer review of the codebase.
Common Safety Concerns with Deepthinklite Skill
When evaluating whether Deepthinklite Skill is safe, consider these category-specific risks:
Understand how Deepthinklite Skill processes, stores, and transmits your data. Granska tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Deepthinklite Skill's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher säkerhet risk.
Regularly check for updates to Deepthinklite Skill. Säkerhet patches and bug fixes are only effective if you're running the latest version.
If Deepthinklite Skill 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 Deepthinklite Skill's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Deepthinklite Skill in violation of its license can expose your organization to legal liability.
Deepthinklite Skill and the EU AI Act
Deepthinklite Skill 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 regelefterlevnad assessment covers 52 jurisdiktions worldwide. For organizations deploying AI tools in regulated environments, understanding these classifications is essential for legal regelefterlevnad.
Best Practices for Using Deepthinklite Skill Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Deepthinklite Skill while minimizing risk:
Periodically review how Deepthinklite Skill is used in your workflow. Check for unexpected behavior, permissions drift, and regelefterlevnad with your säkerhet policies.
Ensure Deepthinklite Skill and all its dependencies are running the latest stable versions to benefit from säkerhet patches.
Grant Deepthinklite Skill only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Deepthinklite Skill's säkerhet advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Deepthinklite Skill is used within your organization, including data handling guidelines and acceptable use cases.
When Should You Avoid Deepthinklite Skill?
Even well-trusted tools aren't right for every situation. Consider avoiding Deepthinklite Skill in these scenarios:
- Scenarios where Deepthinklite Skill's specific capabilities exceed your actual needs — simpler tools may be safer
- Air-gapped environments where the tool cannot receive säkerhet updates
- Projects with strict regulatory requirements that haven't been explicitly validated
For each scenario, evaluate whether Deepthinklite Skill's trust score of 70.3/100 meets your organization's risk tolerance. The Nerq Verified status indicates general production readiness, but sector-specific requirements may apply.
How Deepthinklite Skill Compares to Industry Standards
Nerq indexes over 6 million programvaras, apps, and packages across dozens of categories. Among research tools, the average Trust Score is 62/100. Deepthinklite Skill's score of 70.3/100 is above the category average of 62/100.
This positions Deepthinklite Skill favorably among research tools. While it outperforms the average, there is still room for improvement in certain trust dimensioner.
Industry benchmarks matter because they contextualize a tool's safety profile. A score that looks måttlig 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 Deepthinklite Skill 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 underhåll patterns change, Deepthinklite Skill'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 säkerhet and quality. Conversely, a downward trend may signal reduced underhåll, growing technical debt, or unresolved vulnerabilities. To track Deepthinklite Skill's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=deepthinklite-skill&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 — säkerhet, underhåll, dokumentation, regelefterlevnad, and community — has evolved independently, providing granular visibility into which aspects of Deepthinklite Skill are strengthening or weakening over time.
Deepthinklite Skill vs Alternativ
In the research category, Deepthinklite Skill scores 70.3/100. There are higher-scoring alternatives available. For a detailed comparison, see:
- Deepthinklite Skill vs gpt_academic — Trust Score: 71.3/100
- Deepthinklite Skill vs LlamaFactory — Trust Score: 65.5/100
- Deepthinklite Skill vs unsloth — Trust Score: 66.7/100
Viktigaste slutsatser
- Deepthinklite Skill has a Trust Score of 70.3/100 (B) and is Nerq Verified.
- Deepthinklite Skill meets the minimum threshold for production deployment, though monitoring and additional guardrails are recommended.
- Among research tools, Deepthinklite Skill scores above the category average of 62/100, demonstrating above-average reliability.
- Always verify safety independently — use Nerq's Preflight API for automated, up-to-date trust checks before integration.
Detaljerad poänganalys
| Dimension | Poäng |
|---|---|
| Säkerhet | 0/100 |
| Underhåll | 1/100 |
| Popularitet | 0/100 |
Baserad på 3 dimensioner. Data från flera offentliga källor inklusive paketregister, GitHub, NVD, OSV.dev och OpenSSF Scorecard.
Vilka data samlar Deepthinklite Skill in?
Integritet assessment for Deepthinklite Skill is not yet available. See our methodology for how Nerq measures privacy, or the public privacy review for any community-contributed notes.
Är Deepthinklite Skill säker?
Säkerhetspoäng: 0/100. Review säkerhet practices and consider alternatives with higher säkerhet scores for sensitive use cases.
Nerq övervakar denna entitet mot NVD, OSV.dev och registerspecifika sårbarhetsdatabaser för löpande säkerhetsbedömning.
Fullständig analys: Deepthinklite Skill säkerhetsrapport
Så beräknade vi denna poäng
Deepthinklite Skill's trust score of 70.3/100 (B) beräknas utifrån flera offentliga källor inklusive paketregister, GitHub, NVD, OSV.dev och OpenSSF Scorecard. Poängen speglar 3 oberoende dimensioner: säkerhet (0/100), underhåll (1/100), popularitet (0/100). Varje dimension ges lika vikt för att producera den sammansatta förtroendepoängen.
Nerq analyserar över 7,5 miljoner entiteter i 26 register med samma metodik, vilket möjliggör direkt jämförelse mellan entiteter. Poäng uppdateras löpande när ny data finns tillgänglig.
Den här sidan granskades senast April 23, 2026. Dataversion: 1.0.
Fullständig metodikdokumentation · Maskinläsbar data (JSON API)
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Disclaimer: Nerqs förtroendepoäng är automatiserade bedömningar baserade på offentligt tillgängliga signaler. De utgör inte rekommendationer eller garantier. Gör alltid din egen verifiering.