Mar 4, 2026 at 11:29 PM 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
3 of 3 sub-signals with data
Known CVEs 40% 5.0
No known CVEs
via OSV.dev
Dependency Health 30% 5.0
5 dependencies (minimal)
via npm / PyPI
Supply Chain 30% 5.0
Supply chain analyzed, no transitive CVEs
via npm provenance
Operational Reliability ×0.15 4.6 Uptime, latency, error rates, and incident history
4 of 4 sub-signals with data
Uptime 35% 5.0
100.00% over 5 checks
via Health checks
Response Latency 25% 5.0
p99: 164ms, p50: 145ms
via Health checks
Error Rate 20% 5.0
0.00% error rate (0/5)
via Health checks
Incident History 20% 3.0
2 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 3.7 Downloads, stars, dependents, and growth trajectory
2 of 4 sub-signals with data
Download Volume 67% 3.0
4,389 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
+202.9% 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% 3.5
Internal: 1.0 (0 services), External: 3.5 (1723 followers, 1734 stars)
via Fabric index
Org Maturity 30% 5.0
Organization, 14.6 years old
via GitHub
Community Standing 20% 5.0
627 public repositories
via GitHub
Cross-Platform 20% 3.0
Present on 2 platform(s): github, npm
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
clawdbot-dingtalk is an MIT-licensed npm package by jerryyoung that connects Clawdbot AI agents to DingTalk messaging via the Stream API. The package shows no known vulnerabilities and maintains perfect uptime, but has zero maintenance activity and lacks transparency data, which raises questions about long-term support. With only 2.6K weekly downloads and a single maintainer, adoption remains limited and continuity risk exists for production deployments.
Generated by Fabric AI · Mar 4, 2026 at 4:18 AM
Package Availability (30d)
100.00%
p50: 145ms · p99: 164ms
Avg Latency
143ms
averaged across 30d health checks
Weekly Downloads
4.4k+203%
npm weekly
Incidents & Alerts last 90 days
Score History 84 snapshots
Feb 21 Mar 4
Community & Ecosystem adoption signals
Supply Chain & Dependencies trust chain
Showing 5 of 5 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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