Mastra AI Land Entitlement and Permit Risk Scoring Workflow with Claude Code
Suburban land developments suffer expensive timeline delays due to undetected municipal entitlement restrictions and zoning code conflicts. Construct a Mastra AI workflow with Claude Code integration to cross-evaluate local zoning codes against parcel geometry. Achieve automated zoning error detection across 100 test parcels with 90% precision.
What you are building
The core problem, expected build, and operating context for this challenge.
Implement a Mastra AI graph workflow utilizing Claude Code CLI execution to validate land parcel zoning regulations and generate municipal permit risk scores.
How work is evaluated
Evaluates accuracy of zoning risk scoring and restriction compliance logic across land parcels.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
far_compliance_check
Validates FAR calculation and compliance decision
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
risk_precision
Accuracy of permit risk score assignment • target: 0.9 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build workflow graphs in Mastra AI (`@mastra/core`) with sequential steps for zoning verification and risk calculation
Integrate Claude Code automated workspace execution tools to generate dynamic GeoJSON constraint checkers
Ingest municipal setback requirements, floor-area ratios (FAR), and allowed land use classifications
Produce structured permit risk scores and variance mitigation recommendations
Reference links and supporting material
Suburban zoning district specification records mapped with land parcel geometry and building envelopes.
Challenge setup
Evaluates accuracy of zoning risk scoring and restriction compliance logic across land parcels.
Challenge input
JSON containing parcel_sqft, proposed_building_sqft, max_far, zoning_district
Mastra AI
TypeScript AI workflow framework for sequential agent graphs.
Claude Code
Automated AI engineering tooling for executing local CLI scripts.
Evaluated output
JSON containing compliant, calculated_far, permit_risk_score, violations
- Validates FAR calculation and compliance decision
- Accuracy of permit risk score assignment • target: 0.9 • range: 0-1
- Benchmark: Zoning Compliance Code Benchmark
- Risk Precision target: 0.9
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- Claude Code · Required
- Mastra AI · Optional
- Kore.ai · Optional
- Claude Code · Required
- Mastra AI · Optional
Evaluation contract
- far_compliance_check · Weight 1
- risk_precision · Weight 1
Metrics
- risk_precision · Target 0.9
Benchmarks
- Zoning Compliance Code Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing parcel_sqft, proposed_building_sqft, max_far, zoning_district
Output: JSON containing compliant, calculated_far, permit_risk_score, violations
Execution harness: JavaScript
Evaluation tools
- Mastra AI · agent_framework
- Claude Code · ai_engineering_tool
Recipe freeze marker
No recipe freeze time is recorded.