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Tranquil-Flow/hermes-neurovision

https://github.com/Tranquil-Flow/hermes-neurovision · scanned 2026-06-19 16:03 UTC (1 month, 1 week ago) · 10 languages

233 raw signals (80 security + 153 graph) 37th percentile · Python · medium (20-100K LoC)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 1 month, 1 week ago · v1 · 144 actionable findings from 2 signal sources. 89 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 · 100 findings

Score breakdown â 2026-05-18-v5

Component	Sub-score	Weight	Contribution
`structure_score`	75.0	0.15	11.25
`security_score`	100.0	0.25	25.00
`testing_score`	87.0	0.20	17.40
`documentation_score`	97.0	0.15	14.55
`practices_score`	42.0	0.15	6.30
`code_quality`	42.1	0.10	4.21
Overall		1.00	78.7

Severity distribution — click a segment to filter

Active filters: excluding tests × Reset all

Severity: Critical 0 High 16 Medium 33 Low 55 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 44 System graph 100 Crowd 0 Layer: Security 50 Quality 85 Software 9

Exclude dismissed / FP Exclude test files

Bug-class explainers. Each card groups findings of the same shape — these are the patterns most likely to ship to prod and reappear in future scans unless you systematically fix the cause, not just the instance.

Duplicates & near-duplicates 5 findings

What it is: Same function copy-pasted into multiple modules with minor variations.

Why it matters: Each copy drifts independently — bug fixes apply to one, miss the others.

How AI causes it: AI completes the same pattern in each file rather than refactoring to a shared helper.

Fix approach: Extract the duplicated logic into the most general module both call sites already import. Add tests at the helper level.

5 matching findings on this repo

low Near-duplicate function bodies in 2 places repo-level
low Near-duplicate function bodies in 3 places repo-level
low Near-duplicate function bodies in 4 places repo-level
low Near-duplicate function bodies in 8 places
low Near-duplicate function bodies in 5 places repo-level

View all duplicates & near-duplicates findings →

Legacy markers 8 findings

What it is: TODO, FIXME, XXX, HACK comments. Often indicate a known-broken path the author meant to fix.

Why it matters: Each marker is an unfinished thought. Production code shouldn't ship with debt that's documented but not tracked.

How AI causes it: AI mirrors the style of the codebase, so existing TODOs propagate into new code.

Fix approach: Convert each into a ticket. Delete the comment when the ticket lands. Use a pre-commit hook to block new TODOs without an issue link.

8 matching findings on this repo

low Very large file: hermes_neurovision/theme_plugins/legacy_v2_screens.py (2061 li…
low Very large file: hermes_neurovision/theme_plugins/legacy_v2b_screens.py (2730 l…
low Old/deprecated-named symbol `include_legacy` in tests/test_new_features.py:20
low Old/deprecated-named symbol `include_legacy` in tests/test_cli.py:48
low Old/deprecated-named symbol `toggle_legacy` in hermes_neurovision/command_menu.…
low Old/deprecated-named symbol `include_legacy` in hermes_neurovision/app.py:81
low Old/deprecated-named symbol `list_legacy` in hermes_neurovision/cli.py:251
low Stub function `star_glyph` (body is just `pass`/`return`) — hermes_neurovision/…

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Commented-out code 2 findings

What it is: Lines of source that were intentionally disabled but never deleted.

Why it matters: Git already remembers history — commented code rots, becomes wrong, and adds noise to diffs.

How AI causes it: AI sometimes comments out broken code instead of fixing it. Reviewers approve out of inertia.

Fix approach: Delete. Trust `git log`. If you really need to remember, save it in a notes file under `docs/`.

2 matching findings on this repo

info Commented-code block (6 lines) in demo_video.py:1101
info Commented-code block (5 lines) in hermes_neurovision/scene.py:243

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Config drift 1 finding

What it is: Settings duplicated across env files, Docker compose, K8s, and code defaults, all with slightly different values.

Why it matters: Production behaviour depends on whichever copy your loader reads first. Subtle bugs in staging that don't reproduce in dev.

How AI causes it: AI writes new config from memory rather than reading the existing source.

Fix approach: Pick one source of truth (env vars + a settings module). Have every other place import from there. Lint for duplicates in CI.

1 matching finding on this repo

medium No CI/CD configuration found

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