Trust Signal Breakdown medium 23 sub-signals across 6 dimensions
Vulnerability & Safety ×0.25 5.0 CVEs, dependency health, and supply chain integrity
1 of 3 sub-signals with data
Known CVEs 100% 5.0
No known CVEs
via OSV.dev
Dependency Health no data —
Weight redistributed to sub-signals with data
Supply Chain no data —
Weight redistributed to sub-signals with data
Operational Reliability ×0.15 4.8 Uptime, latency, error rates, and incident history
4 of 4 sub-signals with data
Uptime 35% 5.0
100.00% over 2 checks
via Health checks
Response Latency 25% 5.0
p99: 156ms, p50: 156ms
via Health checks
Error Rate 20% 5.0
0.00% error rate (0/2)
via Health checks
Incident History 20% 4.0
1 incidents in last 90 days
via Incidents table
Maintenance Activity ×0.15 0.0 Commit recency, release cadence, issue response, CI/CD
0 of 4 sub-signals with data
Commit Recency no data —
Weight redistributed to sub-signals with data
Release Cadence no data —
Weight redistributed to sub-signals with data
Issue Response no data —
Weight redistributed to sub-signals with data
CI/CD Presence no data —
Weight redistributed to sub-signals with data
Adoption ×0.15 1.0 Downloads, stars, dependents, and growth trajectory
2 of 4 sub-signals with data
Download Volume 67% 1.0
30 weekly downloads
via npm / PyPI
GitHub Stars no data —
Weight redistributed to sub-signals with data
Dependent Packages no data —
Weight redistributed to sub-signals with data
Growth Trend 33% 1.0
-67.7% week-over-week
via npm
Transparency ×0.15 0.0 License, documentation, security policy, changelog
0 of 4 sub-signals with data
Open Source no data —
Weight redistributed to sub-signals with data
Documentation no data —
Weight redistributed to sub-signals with data
Security Policy no data —
Weight redistributed to sub-signals with data
Changelog no data —
Weight redistributed to sub-signals with data
Publisher Trust ×0.15 2.1 Track record, org maturity, community standing
4 of 4 sub-signals with data
Track Record 30% 1.0
Internal: 1.0 (0 services), External: 1.0 (7 followers, 0 stars)
via Fabric index
Org Maturity 30% 3.5
Organization, 1.1 years old
via GitHub
Community Standing 20% 1.0
0 public repositories
via GitHub
Cross-Platform 20% 3.0
Present on 2 platform(s): github, npm
via Registry scan
Scored across 23 sub-signals in 6 dimensions Scoring engine v1 (beta) — actively being expanded Phase 1: Core sub-signal architecture (live) Phase 2: Permission scope & expanded collection (in progress)
Trust Assessment AI Assessment
Lovable is an AI-powered full-stack application builder published by Lovable with an unknown license that generates production-ready code from natural language prompts. The service shows strong operational reliability with perfect uptime and no known vulnerabilities, but operates with minimal transparency—no public maintenance metrics, limited adoption data, and a single-maintainer structure. With only 30 weekly downloads and zero maintenance/transparency scores, this tool lacks the community validation and observable development practices typical of established infrastructure services.
Generated by Fabric AI · Mar 4, 2026 at 4:54 AM
Package Availability (30d)
100.00%
p50: 156ms · p99: 156ms
Avg Latency
154ms
averaged across 30d health checks
Weekly Downloads
30-68%
npm weekly
Incidents & Alerts last 90 days
Score History 4 snapshots
Feb 26 Mar 2
Community & Ecosystem adoption signals
Data Sources 6 indexed
◎
OSV.dev CVE database · vulnerability scanning for npm & PyPI packages
◈
GitHub API Commits, issues, releases, repo metadata, transparency checks
⬡
npm Registry Package metadata, weekly downloads, maintainers, dependencies
⬡
PyPI Package metadata, weekly downloads, dependency tree
△
HTTP Health Checks 15-min pings · uptime, latency, status monitoring
◎
PyPI Stats Download statistics and trends
Are you the publisher? Claim this profile to unlock deeper evaluation, real-time monitoring,
Claim Provider Report Issue
Share this Trust Score Generate a scorecard image optimised for X, LinkedIn and other social platforms.
⬇ Download Score Card