Mar 3, 2026 at 7:29 AM 6 signals analysed No manual reviews · fully automatedTrust 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 3.8 Uptime, latency, error rates, and incident history
4 of 4 sub-signals with data
Uptime 35% 5.0
100.00% over 4 checks
via Health checks
Response Latency 25% 4.0
p99: 212ms, p50: 204ms
via Health checks
Error Rate 20% 1.0
25.00% error rate (1/4)
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 2.3 Downloads, stars, dependents, and growth trajectory
2 of 4 sub-signals with data
Download Volume 67% 1.0
29 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% 5.0
+222.2% 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 4.2 Track record, org maturity, community standing
4 of 4 sub-signals with data
Track Record 30% 3.0
Internal: 3.0 (4 services), External: 1.0 (6 followers, 2 stars)
via Fabric index
Org Maturity 30% 5.0
User account, 10.0 years old
via GitHub
Community Standing 20% 4.0
73 public repositories
via GitHub
Cross-Platform 20% 5.0
Present on 3 platform(s): github, npm, pypi
via Registry scan
About this scoreScored 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
Think is an agent service published by vmanoilov on Smithery with an unknown license, averaging 29 weekly downloads and maintaining 100% uptime with no detected vulnerabilities. The service shows strong operational reliability and clean security posture, but lacks transparency signals including maintenance documentation and a public README. Users should note the unknown licensing terms and limited visibility into ongoing maintenance practices, despite the publisher's moderate trust score.
Generated by Fabric AI · Mar 3, 2026 at 7:29 AM
Package Availability (30d)
100.00%
p50: 204ms · p99: 212ms
Avg Latency
126ms
averaged across 30d health checks
Weekly Downloads
29+222%
npm + PyPI weekly
Incidents & Alerts last 90 days
Score History 4 snapshots
Feb 24 Mar 2
Community & Ecosystem adoption signals
29
Weekly Downloads
npm + PyPI
Supply Chain & Dependencies trust chain
Showing 1 of 1 dependencies
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
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