Is Annotated Doc Safe?
Annotated Doc — Nerq Trust Score 64.2/100 (C+ grade). Score based on 2 independent trust signals. Last analyzed: 2026-08-18
Annotated Doc is a Python package with a Nerq Trust Score of 64.2/100 (C+), based on 3 independent data dimensions. Last analyzed: 2026-08-18 Security: 90/100. Popularity: 100/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-08-18. Machine-readable data (JSON).
Is Annotated Doc safe?
Trust Score Breakdown — Annotated Doc has a Nerq Trust Score of 64.2/100 (C+). Measured across 2 independent trust signals (as of 2026-08-18).
What is Annotated Doc's trust score?
Annotated Doc has a Nerq Trust Score of 64.2/100, earning a C+ grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Annotated Doc?
Annotated Doc's strongest signal is popularity at 100/100. No known vulnerabilities have been detected.
What is Annotated Doc and who maintains it?
| Author | Unknown |
| Category | Python Packages |
| Source | N/A |
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Safety Guide: Annotated Doc
What is Annotated Doc?
Annotated Doc is a Python package — Document parameters, class attributes, return types, and variables inline, with Annotated..
How to Verify Safety
Run pip audit or safety check. Review on PyPI for download stats.
You can also check the trust score via API: GET /v1/preflight?target=annotated-doc
Key Safety Concerns for Python package
When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.
Measured Signals
Annotated Doc has a Nerq Trust Score of 64/100 (C+). This score is a composite of automated measurements of security, maintenance, community, and quality signals.
Key Takeaways
- Annotated Doc has a Trust Score of 64/100 (C+).
- 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.
Detailed Score Analysis
| Dimension | Score |
|---|---|
| Security | 90/100 |
| Maintenance | 54/100 |
| Popularity | 100/100 |
| Quality | 40/100 |
| Community | 35/100 |
Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.
What data does Annotated Doc collect?
Annotated Doc is a Python package maintained by Unknown. It receives approximately 126,271,383 weekly downloads.
As a development package, Annotated Doc does not directly collect end-user personal data. However, applications built with it may collect data depending on implementation. Privacy score: 80/100.
Review the package's dependencies for potential supply chain risks. Run your package manager's audit command regularly.
Full analysis: Annotated Doc Privacy Report · Privacy review
Is Annotated Doc secure?
Security score: 90/100. Annotated Doc has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.
License information not available. Open-source packages allow independent security review of the source code.
Run your package manager's audit command (`npm audit`, `pip audit`, `cargo audit`) to check for known vulnerabilities in your dependency tree.
Full analysis: Annotated Doc Security Report
How we calculated this score
Annotated Doc's trust score of 64.2/100 (C+) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (54/100), popularity (100/100), quality (40/100), community (35/100). 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.
Signals last measured on August 18, 2026. Data version: 1.0.
Full methodology documentation · Machine-readable data (JSON 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.