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three-cubes/fitness-engine

https://github.com/three-cubes/fitness-engine · scanned 2026-06-16 00:35 UTC (2 months, 2 weeks ago)

19 raw signals (4 security + 15 graph)

UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 2 months, 2 weeks ago · v1 · 17 actionable findings from 2 signal sources. 5 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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Scan summary Repository scanned at 98.8/100 with 55.6% coverage. It contains 317 nodes across 0 cross-layer flows, written primarily in mixed languages. Engine surfaced 15 findings — concentrated in quality (12), frontend (1), security (1). Risk profile is low: 0 critical, 0 high, 3 medium. Recommended next step: open the quality layer findings first — that's where the highest-impact wins live.

Showing 9 of 17 actionable findings. 22 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 Security checks quality Practices conf 0.62 4 occurrences Foundry mined assumption checks: three-cubes/fitness-engine
Comment chain pattern product: assumption_checks Repo: three-cubes/fitness-engine Thread: three-cubes/fitness-engine#4 Outcome: ambiguous_needs_more_evidence Thread label: thread_has_human_issue_and_fix_context Source graph label: source_artifact_without_verification_graph Reasons: source_graph_has…
4 occurrences
repo-level (4 hits)
medium Security checks quality maintenance conf 0.70 3 occurrences Foundry mined blueprint gap alignment: three-cubes/fitness-engine
Graph query export: Human feedback aligned with blueprint or architecture gaps Query id: blueprint_gap_alignment Query type: motif_query Intent: Curriculum-gap examples connecting issue threads to helicopter-view gaps. Motif: blueprint_gap_alignment Training usage: curriculum_gap Graph gold label: …
3 occurrences
repo-level (3 hits)
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 security Coverage conf 1.00 No auth library detected
The scanner did not find any standard auth library (JWT, OAuth, NextAuth, Auth0, etc.). The repo has auth/admin/session surface indicators, so auth may live in custom code, in a separate service, or be missing.
auth
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
low System graph quality Production readiness conf 1.00 Composite production-readiness gap
Multiple low-cost hardening controls are missing together: license, ci. 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 59 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 License conf 1.00 No license file detected
No LICENSE/COPYING/NOTICE file was found. Generated repositories often omit licensing, which blocks reuse and automated intake.
Repo hardeningGenerated repo pattern
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
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