Sample
What a Standard Code Review actually looks like
Below is a redacted excerpt from a real HRC v2.0 Standard Code Review delivered to a partner in May 2026. Full reports are 30–60 pages depending on codebase size. This excerpt shows the cover, executive summary, score dashboard, and the first per-category section — the shape of what you receive.
Cover page
Reviewed by Daniel Kemp, Hamilton Ridley Consulting
Reviewed @ 31ea3e2 · 2026-05-10
Executive Summary
The score reflects production readiness, not the value of the work to date. A D grade under this lens means the application is a working proof-of-concept that validates the product idea but needs targeted remediation before any customer-facing deployment — the gaps are well-defined and bounded.
Verdict:Together the components form a credible proof-of-concept — module boundaries inside the application are clean, the AI-enhancement flow degrades gracefully on missing keys, and the per-submittal batch handler keeps going on partial failure. The score is dragged down by the absence of authentication on any endpoint, no automated tests across the codebase, the dual-source dependency setup (pyproject.toml + requirements.txt diverging), and a half-built TypeScript workspace that the application doesn't use. Six P0/P1 items represent the path to B+ — roughly one focused week of remediation.
Score Dashboard
| Category | Weight | Score | Grade |
|---|---|---|---|
| Functional Correctness | 15% | 64 | C |
| Architecture & Structure | 10% | 58 | C- |
| Security | 18% | 32 | F |
| Reliability & Error Handling | 15% | 44 | D |
| Maintainability & Code Quality | 12% | 60 | C |
| Operational Readiness | 8% | 38 | F |
| Portability & Vendor Lock-in | 7% | 36 | F |
| Dependency Hygiene | 5% | 55 | C- |
| Performance & Cost | 5% | 62 | C |
| IP & Ownership Clarity | 5% | 28 | F |
| Overall (weighted) | 100% | 48 | D |
Three Headline Findings (excerpt)
Every API route — including the LLM-fronted analysis and PDF generation paths — accepts unauthenticated requests. Anyone with the URL can drain the configured AI provider budget and exfiltrate parsed customer specs.
Effort: 4–6 hours to add a shared API key middleware. Cost of inaction: one Twitter post away from financial damage; data exfiltration is silent until billing arrives.
Ref: main.py:51-63, ai_enhancer.py
No tests/ directory, no pytest config, no smoke tests on the critical paths (spec parsing, AI enhancement, PDF export). Every change is a regression risk; the partner has no signal between "the test suite runs green" and "a real user broke it."
Effort: 6–8 hours for a thin smoke-test suite (auth, happy-path, export). Cost of inaction: every refactor compounds risk; debugging is reactive, not preventive.
The pnpm/TypeScript workspace at lib/, artifacts/api-server, and artifacts/mockup-sandboxis scaffolded but not consumed by the Python application. Two viable paths: delete it (~30 min, lean repo) or migrate the app's API surface to the Express server (~1 week, gives typed clients + persistence). Right now it's the worst of both — half-built infra carrying real maintenance burden for zero benefit.
The full report includes:
- · All ten per-category sections with findings, recommendations, and file:line citations
- · Cross-cutting bug catalog (16 entries in this example)
- · Prioritized action list (P0/P1/P2/P3) with effort and cost-of-inaction
- · Decisions Required section — strategic forks only you can resolve
- · Open Questions section — factual gaps HRC needs your input on
- · Appendix A: Full scoring rubric
- · Appendix B: Methodology overview
- · Findings.json (machine-readable, drop into Linear / Jira / GitHub Issues)
- · Embeddable score badge for your README