RepoTrust

lionhylra/cc-usage-bar

Minimal macOS menu bar app to check Claude Code usage at a glance — accurate, safe, zero setup

https://github.com/lionhylra/cc-usage-bar · commit c483481c0fab · scored 2026-11-13 · scoring prov-4+qual-2+sec-2

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Scores

Pillar Score Signal Confidence Coverage
AI provenance (estimated AI-generated share) 0 none-detected 43% 100%
Code quality 52 weak 90% 100%
Security 82 adequate 85% 100%

AI provenance

No AI attribution or AI-consistent velocity signals detected.

Month Lines added Attributed to AI
2026-03 1,248 0%

AI provenance describes how code was produced; it is not a quality judgment. Unless attribution is explicit, these are signals consistent with AI generation, not proof. Squash/rebase workflows, imported code, and rewritten history reduce accuracy.

Code quality

Code quality is weak: test-to-source ratio 0.00, no CI test run detected; 44% of function code in complex or long functions; 0% duplicated lines. Weakest area: testing.

Area Score
Complexity (lizard) 27
Duplication 100
Code smells (Opengrep) 100
Tests and CI 0
Documentation 85
Type safety n/a
Process (Scorecard review/maintenance) n/a

Most complex functions:

Security

Security posture is adequate: no high-impact findings. Weakest area: posture.

Area Score
Secrets (gitleaks, tree + history) 100
Static analysis (Opengrep) 100
Vulnerable dependencies (OSV) n/a
CI workflows (zizmor) n/a
Posture (policy, updates, signing, lockfiles) 0
OpenSSF Scorecard n/a

Static signals only: the scorer never runs the project's code. Secret findings are pattern matches that were not verified, and secret values are never stored.

Distribution signals

Signals consistent with a repository that exists to distribute a download. They are heuristics, not a verdict; release files were not downloaded or inspected.

Repository profile

Language Lines
Swift 1,125

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