Make the assumptions visible
Provide the rule document and field definitions. Ask for conflicts and hand-checkable examples before implementation.
Turn domain knowledge into a tool you can run, inspect, and improve with Codex.
For data analysts & developers working with complex rules.
Change a shipment. See what applies, what is skipped, and what changes when a new rule takes effect.
Validate → match scope and dates → resolve each rate group → apply targeted exemptions → sum → round once.
| ID / source | Scope | Action / priority | Rate |
|---|---|---|---|
| B-S · Training Schedule §1 | Sensor / A or B | Base / 10 | 5% |
| B-T · Training Schedule §2 | Textile / A or B | Base / 10 | 8% |
| P-B · Training Preference §1 | Sensor / B | Replace base / 20 | 2% |
| S-A · Training Surcharge §1 | Sensor / A | Add surcharge / 10 | v1: 10%; v2: 12% from July 1, otherwise 10% |
| X-A · Training Exemption §1 | Sensor / A / verified certificate | Exempt S-A only / 100 | Base duty remains |
| C-S · Training Conflict §1 | Sensor / A or B / toggle enabled | Competing base / 10 | 7% |
All rules are limited to January 1–December 31, 2026, inclusive. Only the highest-priority matching rule in each rate group wins. A tie at the winning priority means REVIEW. Priority does not compare unrelated groups or establish a legal hierarchy.
USD, ad valorem only; one shared value base. Integer cents and basis points, final total rounded half up to cents. No fees, quotas, quantity duties, or real-world classification. Unknown inputs and out-of-range dates require review.
Provide the rule document and field definitions. Ask for conflicts and hand-checkable examples before implementation.
Specify the artifact, scope and acceptance criteria. Let Codex inspect files, implement the change and run relevant checks.
Review the file changes and expected values. Try an effective-date boundary and a case that must refuse to calculate.
Read demo-brief.md. Build a self-contained index.html with Chinese as the default language and an English switch using only the supplied fictional rules. Show v1/v2 results, applied and skipped rules with reasons, and REVIEW for unresolved inputs or conflicts. Use no external dependencies or API calls. Verify the acceptance examples. Report the checks you actually ran and any remaining limitations.
Use company-approved tools and accounts. Start with synthetic data. Local file access does not mean inference stays on your machine.
Have authorized people confirm classification, applicability and effective dates. Preserve sources, versions and review records.
Work in an isolated project. Keep production writes, deployments and external sharing behind explicit authorization.
Keep inputs, rule versions, traces and known-answer checks. Escalate ambiguity. A confident explanation is not proof.
Your turn: What recurring task could you pilot with synthetic data this week?
Measure time to a reviewed result, corrections needed, and known-case accuracy.