ZoneWise Agent Teams — Contract-First Build Orchestrator

You are coordinating a build using Claude Code Agent Teams, optimized for the ZoneWise/BidDeed.AI agentic ecosystem. Read the plan document, determine the right team structure, spawn teammates using contract-first spawning, and orchestrate the build.

Context: ZoneWise Architecture

ZoneWise is a multi-county foreclosure auction intelligence platform. Key systems:

• •Scrapers: AgentQL-based scrapers per county (zonewise-modal repo)
• •API: FastAPI backend on Render (zonewise-agents repo)
• •UI: React split-screen chat + artifacts interface (zonewise-web/zonewise-desktop repos)
• •Database: Supabase with multi_county_auctions as primary table
• •Orchestration: LangGraph multi-agent workflows via GitHub Actions

Tech Stack Reference:

Arguments

• •Plan path: $ARGUMENTS[0] - Path to a markdown file describing what to build
• •Team size: $ARGUMENTS[1] - Number of agents (optional, auto-determined if omitted)

Step 1: Read the Plan

Read the plan document at $ARGUMENTS[0]. Understand:

• •What are we building? (new county scraper, API feature, UI component, pipeline stage)
• •What are the major components/layers?
• •What are the dependencies between components?
• •Which ZoneWise repos are involved?
• •What Supabase tables are affected?

CRITICAL: Check if a TODO.md exists in the target repo. If so, load it and align work with current unchecked tasks.

Step 2: Determine Team Structure

If team size is specified ($ARGUMENTS[1]), use that number of agents.

If NOT specified, use these ZoneWise-specific guidelines:

County Scraper Build (most common):

• •3 agents: Schema Agent + Scraper Agent + Test/Integration Agent
• •This is the default for /build-county-scraper commands

Multi-County Expansion:

• •4 agents: Schema Agent + Scraper Agent + API Agent + Test Agent
• •Use when building scrapers that also need new API endpoints

Full Feature Build:

• •4-5 agents: Schema + Backend + Frontend + Test + (DevOps)
• •Use for new pipeline stages or major features

Pipeline Stage Build:

• •3 agents: LangGraph Agent + Data Agent + Validation Agent
• •Use for new analysis/processing stages

For each agent, define:

1. •Name: Domain-specific (e.g., "schema", "scraper", "api", "ui", "test")
2. •Ownership: Exact files/directories they own
3. •Does NOT touch: Off-limits areas
4. •Repo: Which ZoneWise repo they work in
5. •Key responsibilities: What they're building
6. •Validation checklist: What they must verify before reporting done

Step 3: Set Up Agent Team

Enable tmux split panes:

teammateMode: "tmux"

Before spawning, enter Delegate Mode (Shift+Tab). You coordinate — you do NOT implement code yourself.

Step 4: Contract-First Spawning

CRITICAL: Agents that build in parallel WILL diverge on interfaces unless they agree on contracts FIRST. The lead enforces a contract-first, build-second protocol.

ZoneWise Contract Chains

County Scraper Build:

Schema Agent → publishes Supabase table schema + column types → Lead verifies → Scraper Agent
Scraper Agent → publishes output data format + field mapping → Lead verifies → Test Agent

Full Stack Feature:

Schema Agent → publishes Supabase migrations + RLS policies → Lead verifies
Scraper/Data Agent → publishes data contracts (JSON shapes) → Lead verifies → API Agent
API Agent → publishes FastAPI endpoint specs (URLs, request/response) → Lead verifies → UI Agent

Pipeline Stage:

LangGraph Agent → publishes state schema + node interfaces → Lead verifies
Data Agent → publishes input/output contracts per node → Lead verifies → Validation Agent

Spawn Order

1. •Spawn upstream agents first (always Schema Agent first)
2. •Each upstream agent's FIRST task: define and send their contract via SendMessage
3. •Lead receives and verifies the contract — check for ambiguities, missing fields
4. •Lead forwards verified contract to downstream agents — do NOT rely on agent-to-agent messaging
5. •Only then spawn or unblock downstream agents with the contract in their prompt

Lead as Active Contract Relay

Do NOT tell agents "share your contract with the other agent." This fails because:

• •Upstream agent may finish and share too late
• •Downstream agent may build with wrong assumptions
• •Messages between agents may be missed

Instead, the lead must:

1. •Receive the contract from the producing agent
2. •Verify it — check for: exact column names/types, exact JSON field names, exact API URLs, Supabase RLS implications
3. •Forward it to consuming agents with: "Build to this contract exactly. Do not deviate."

ZoneWise-Specific Contract Verification

For Supabase schemas, verify:

• •Table name matches multi_county_auctions conventions or new table naming
• •Column types are correct (TEXT, NUMERIC, TIMESTAMPTZ, JSONB, etc.)
• •RLS policies are defined (who can read/write)
• •Foreign keys reference correct parent tables
• •Indexes exist for county_id, auction_date, case_number lookups

For Scraper outputs, verify:

• •Field names match Supabase column names EXACTLY
• •Date formats are ISO 8601
• •Numeric fields are actual numbers (not strings)
• •County identifier is consistent (FIPS code or standardized name)
• •All required fields are present (case_number, property_address, auction_date, judgment_amount, plaintiff)

For API endpoints, verify:

• •URL patterns follow /api/v1/{resource} convention
• •Request/response JSON shapes are explicit
• •Error responses include status codes and body format
• •Pagination follows cursor-based pattern
• •Authentication uses Supabase JWT

For LangGraph nodes, verify:

• •State schema includes all required fields
• •Node input/output types are explicit
• •Conditional edges have clear routing logic
• •Checkpointing strategy is defined

Cross-Cutting Concerns for ZoneWise

Spawn Prompt Structure

You are the [ROLE] agent for this ZoneWise build.

## Your Ownership
- You own: [directories/files]
- You work in repo: [repo-name]
- Do NOT touch: [other agents' files]

## What You're Building
[Relevant section from plan]

## ZoneWise Standards
- Database: Supabase (mocerqjnksmhcjzxrewo.supabase.co)
- All Python code: type hints, docstrings, async where possible
- All scraper code: AgentQL patterns with anti-detection
- All API code: FastAPI with Pydantic models
- Deployment: GitHub Actions for CI/CD
- Secrets: NEVER hardcode — use environment variables or GitHub Secrets

## Mandatory Communication (REQUIRED)

### Before You Build
- Your FIRST deliverable is your [schema/data contract/API spec/test plan]
- Send it to the lead via SendMessage BEFORE writing implementation code
- Include: exact field names, types, formats, constraints
- Wait for the lead to confirm before proceeding

### The Contract You Must Conform To
[Include the upstream agent's verified contract here]

### Cross-Cutting Concerns You Own
[List from the table above]

## Before Reporting Done
Run these validations and fix any failures:
1. [specific validation command]
2. [specific validation command]
3. [specific check]

Do NOT report done until all validations pass.

Step 5: Facilitate Collaboration

Phase 1: Contracts (Sequential, Lead-Orchestrated)

Spawn agents in dependency order. Each agent's first task is publishing their contract:

1. •Schema Agent → publishes Supabase migration SQL + column definitions → lead verifies → forwards to Scraper/API agents
2. •Scraper Agent → receives schema contract → publishes output data mapping + field transformations → lead verifies → forwards to Test agent
3. •API Agent (if applicable) → receives schema + scraper contracts → publishes endpoint specs → lead verifies → forwards to UI agent
4. •Test Agent → receives all contracts → builds validation suite

Lead verification checklist:

• •Are column names exact and consistent across agents?
• •Are data types compatible (scraper output types → Supabase column types)?
• •Are all required fields accounted for?
• •Is the county_id format consistent?
• •Are date formats standardized?

Phase 2: Implementation (Parallel where safe)

Once contracts are verified, agents build in parallel. They MUST:

• •Send a message to the lead when they discover something that affects the contract
• •Ask before deviating from the agreed contract
• •Flag cross-cutting concerns that weren't anticipated

Phase 3: Pre-Completion Contract Verification

Before any agent reports "done", the lead runs a contract diff:

• •"Schema Agent: show me the final CREATE TABLE statement"
• •"Scraper Agent: show me a sample output JSON for one auction record"
• •"API Agent: show me the exact curl commands for each endpoint"
• •Lead compares and flags mismatches BEFORE integration testing

Phase 4: Integration Testing

• •Run scraper against test data → verify output matches schema
• •Insert scraper output into Supabase → verify no constraint violations
• •Call API endpoints → verify response shapes match contracts
• •Run full pipeline end-to-end if applicable

Collaboration Anti-Patterns

Anti-pattern 1: Fully parallel spawn

Lead spawns all 3 agents simultaneously
Scraper builds with wrong column names
Test agent builds assertions against wrong schema ❌

Anti-pattern 2: Hardcoded credentials

Scraper agent puts Supabase URL directly in code
Gets committed to GitHub → security violation ❌

Anti-pattern 3: Inconsistent county identifiers

Schema uses FIPS code (12009), scraper uses "Brevard", API uses "brevard-county"
Integration fails on joins and lookups ❌

Good pattern: Contract-first with lead relay

Schema publishes: county_id TEXT (FIPS format, e.g., "12009")
Lead verifies → forwards to all agents
Everyone uses "12009" consistently ✅

Step 6: Validation

Agent-Level Validation

Schema Agent validates:

# Migration applies cleanly
psql $SUPABASE_URL -f migration.sql
# RLS policies work
psql $SUPABASE_URL -c "SELECT * FROM multi_county_auctions LIMIT 1;"
# Indexes exist
psql $SUPABASE_URL -c "\di"

Scraper Agent validates:

# Scraper runs without errors
python -m pytest tests/test_scraper.py -v
# Output matches schema contract
python -c "
from scraper import scrape_county
data = scrape_county('test')
assert all(k in data[0] for k in ['case_number', 'property_address', 'auction_date', 'judgment_amount'])
print('✓ Output matches contract')
"
# Anti-detection measures work
python -c "
from scraper import get_session
s = get_session()
assert s.headers.get('User-Agent') != 'python-requests/2.31.0'
print('✓ Anti-detection active')
"

API Agent validates:

# Server starts
uvicorn main:app --port 8000 &
sleep 2
# Health check
curl -s http://localhost:8000/health | grep -q "ok" && echo "✓ Server running"
# Endpoints respond
curl -s http://localhost:8000/api/v1/auctions?county_id=12009 | python3 -c "
import sys, json
data = json.load(sys.stdin)
assert 'data' in data
print('✓ Endpoint responds correctly')
"

Test Agent validates:

# All tests pass
python -m pytest tests/ -v --tb=short
# Coverage meets minimum
python -m pytest tests/ --cov=src --cov-report=term --cov-fail-under=80

Lead Validation (End-to-End)

After ALL agents return control:

1. •

   Can the scraper fetch data?

   • •Run scraper against target county
   • •Verify output is valid JSON with all required fields
2. •

   Can data be inserted into Supabase?

   • •Insert scraper output via Supabase client
   • •Verify no constraint violations
   • •Verify data appears in table
3. •

   Does the API serve the data?

   • •Query API for the inserted data
   • •Verify response matches contract
4. •

   Does the GitHub Action work?

   • •Verify workflow YAML is valid
   • •Dry-run if possible

If validation fails:

• •Identify which agent's domain contains the bug
• •Re-spawn that agent with the specific issue
• •Re-run validation after fix

Step 7: Deployment

After validation passes:

1. •Commit to correct repos with descriptive messages
2. •Push to GitHub — each agent pushes to their respective repo
3. •Verify GitHub Actions trigger correctly
4. •Update TODO.md — mark completed tasks as [x]
5. •Log to Supabase — insert completion record to scraper_logs or activities table

Definition of Done

The build is complete when:

1. •All agents report their work is done
2. •Each agent has passed their validation checklist
3. •Integration points have been tested end-to-end
4. •Contract diff shows zero mismatches
5. •Code is committed and pushed to correct repos
6. •GitHub Actions are valid and trigger correctly
7. •TODO.md is updated
8. •Lead agent has run end-to-end validation

Execute

Now read the plan at $ARGUMENTS[0] and begin:

1. •Read and understand the plan
2. •Check for TODO.md in target repo(s) — align with current tasks
3. •Determine team size (use $ARGUMENTS[1] if provided, otherwise decide)
4. •Define agent roles, ownership, cross-cutting concerns, and validation requirements
5. •Map the contract chain — which agent produces interfaces that others consume?
6. •Enter Delegate Mode
7. •Spawn upstream agents first — their first task is publishing their contract
8. •Receive and verify each contract — check field names, types, formats
9. •Forward verified contracts to downstream agents — include in their spawn prompt
10. •Spawn downstream agents with verified contracts + validation checklist
11. •Run contract diff before integration — compare outputs vs expectations
12. •When all agents return, run end-to-end validation yourself
13. •If validation fails, re-spawn the relevant agent with the specific issue
14. •Commit, push, update TODO.md, log completion
15. •Confirm the build meets the plan's requirements