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xyanwert/usage-monitor

https://github.com/xyanwert/usage-monitor · scanned 2026-06-16 01:03 UTC (2 months, 1 week ago)

30 raw signals (0 security + 30 graph)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 2 months, 1 week ago · v1 · 15 actionable findings from 1 signal source. 15 repeated signals grouped for readability. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

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66.7% cov · 15 findings

Severity distribution — click a segment to filter

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Severity: Critical 0 High 0 Medium 4 Low 8 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 0 System graph 15 Crowd 0 Layer: Software 1 Frontend 1 Cicd 1 Quality 12

Exclude dismissed / FP Exclude test files

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Scan summary Repository scanned at 94.8/100 with 66.7% coverage. It contains 141 nodes across 0 cross-layer flows, written primarily in mixed languages. Engine surfaced 30 findings — concentrated in quality (27), software (1), frontend (1). Risk profile is low: 0 critical, 0 high, 4 medium. Recommended next step: open the quality layer findings first — that's where the highest-impact wins live.

Showing 12 of 15 actionable findings. 30 raw detector signals were grouped into reader-sized issues. Click TP / FP to vote on a finding's accuracy — votes adjust the confidence weighting and improve detection across the platform.

medium System graph quality Placeholder conf 1.00 Critical user flow still appears backed by mock or placeholder data

A payment/auth/admin/order/billing-style flow contains mock, fake, TODO, dummy, or placeholder markers in runtime source. In the Fable corpus this is a high-leverage completeness smell: the app can look finished while the money, identity, or tenant flow is still scaffolded.

Mock dataCritical flowGenerated repo pattern

medium System graph quality Integrity conf 1.00 Network/subprocess call without timeout or try/except — claude_monitor.py:176

`subprocess.run(...)` here lacks both a `timeout=` arg and an enclosing try/except. This is exactly the class of bug that took down our git-clone earlier (HTTP/2 stream cancel surfaced as a fatal). Add a `timeout=` and wrap in try/except, or use a wrapper that retries.

runtime safetyRobustness

medium System graph cicd CI/CD security conf 1.00 No CI/CD pipelines detected

No GitHub Actions, GitLab CI, or CircleCI configs found. Without CI you can't gate deploys on tests/lints.

CI/CD securityCoverage

medium System graph quality Tests conf 1.00 Very low test-to-source ratio

0 test file(s) for 3 source file(s) (ratio 0.00). Consider adding integration or unit tests for critical paths.

Coverage

low System graph quality Production readiness conf 1.00 Composite production-readiness gap

Multiple low-cost hardening controls are missing together: ci, tests. Opus verification showed these co-occurring gaps are a better readiness signal than reading each flag in isolation.

Repo hardeningGenerated repo pattern

low System graph quality Debug conf 1.00 Debug logging residue appears in source files

Found 10 console/debugger/print-style debug statements in non-test source. This is a common fast-generation residue before production cleanup.

CleanupRepo hardeningGenerated repo pattern

low System graph quality Integrity conf 1.00 11 occurrences Near-duplicate function bodies in 2 places

Functions with the same first-5-line body hash: demo_tokenfall.py:clamp, claude_monitor.py:clamp This is *the* AI-coder failure mode (4× more duplication in vibe-coded repos — see https://jw.hn/ai-code-hygiene). Consolidate or document why they're separate.

11 occurrences

repo-level (11 hits)

duplicatesduplication

low System graph quality Integrity conf 1.00 2 occurrences Near-duplicate function bodies in 4 places

Functions with the same first-5-line body hash: demo_tokenfall.py:line_px, demo_tokenfall.py:line, claude_monitor.py:line_px, claude_monitor.py:line This is *the* AI-coder failure mode (4× more duplication in vibe-coded repos — see https://jw.hn/ai-code-hygiene). Consolidate or document why they'r…

2 occurrences

repo-level (2 hits)

duplicatesduplication

low System graph quality Integrity conf 1.00 5 occurrences Near-duplicate function bodies in 5 places

Functions with the same first-5-line body hash: claude_monitor.py:resize, claude_monitor.py:resize, claude_monitor.py:resize, claude_monitor.py:resize This is *the* AI-coder failure mode (4× more duplication in vibe-coded repos — see https://jw.hn/ai-code-hygiene). Consolidate or document why they…

5 occurrences

repo-level (5 hits)

duplicatesduplication

low System graph software Dead code conf 1.00 Possibly dead Python function: demo_burnout

No callers detected by AST scan in this repo. Could be exported for external callers or a framework handler.

claude_monitor.py:2032

low System graph quality Provenance conf 1.00 Shallow git history limits provenance confidence

The repository is a shallow clone. Origin/evolution analysis cannot distinguish fresh generation, imported legacy code, or long-lived human code with high confidence.

Git historyGenerated repo pattern

low System graph quality Complexity conf 1.00 Very large file: claude_monitor.py (2850 lines)

Files with >800 lines often hide complexity hotspots and discourage tests.

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