Existing App Hardening
Production Hardening Across AI-Assisted Repositories
Converted partially complete systems into locally verified release candidates while preserving the distinction between offline proof and live production readiness.
Verified evidence
- Six-repository hardening report records: CGEBS 9 unit tests + smoke + compile; SignalOps 11 unit tests + smoke + compile; Market Intelligence Pipeline 13 tests + smoke + compile.
- DriftGuard recorded 52 release checks + 5 judgment tests in the six-repo validation report.
- TraceCrumb recorded 32 static checks + 45 graph tests.
- Decision Kill Switch recorded 7 static authority checks + Python compile while remaining build-blocked on external dependencies.
- A separate five-system release verified 50 automated tests, 5 installed console entry points, 5 wheels, and 5 primary smoke workflows.
Mechanisms
authority-boundary repairtransaction/state integritytyped contractsretry/failure handlingsecret boundariesCI/release gatesdeployment-readiness documentation
Claim boundary
These checks establish deterministic/offline engineering evidence. They do not establish production scale, external adoption, client ROI, or live-service reliability.
Best-fit client work
- existing AI app
- AI-generated code cleanup
- productionize MVP
- deployment/debugging
- backend reliability
Source code
This portfolio package intentionally does not invent per-repository URLs. Open GitHub profile. Add a direct repository link here once the exact public repo URL is confirmed.