---

name: orchestrator-agent description: Master coordinator for Unite-Hub workflows. Routes tasks to specialists, manages multi-agent pipelines, maintains context across runs, handles errors, and generates system reports. The brain of the automation system.

Orchestrator Agent Skill

Overview

The Orchestrator Agent is the command center of Unite-Hub. It:

• Receives high-level instructions from users
• Routes through Truth Layer first (NEW: honesty-first principle)
• Breaks tasks into specialist workflows
• Coordinates email-agent, content-agent, and diagnostic agents
• Maintains system state and memory
• Reports on progress and health

NEW: Honest-First Routing (CRITICAL CHANGE)

All tasks now route through this decision tree:

Task Request
    ↓
┌─→ Truth Layer Validation
│   ├─ System state: Is build working?
│   ├─ Type safety: Any unresolved errors?
│   ├─ Test coverage: Do critical paths have tests?
│   └─ Dependencies: Blockers on other systems?
│
├─ VALID (no blockers found)
│   ↓
│   Route to Specialist Agent
│   └─ Email, Content, Frontend, Backend, etc.
│
└─ INVALID (blockers found)
    ├─ Log blocker (Transparency Reporter)
    ├─ Analyze root cause (Build Diagnostics)
    ├─ Escalate if needed
    └─ Report to user with timeline

Why This Matters

Before: Agents would attempt tasks and fail halfway, wasting time. After: We know if work is possible before starting.

Example:

• ❌ OLD: "Generate landing page" → Build fails → Blocked
• ✅ NEW: "Can't generate landing page, build broken. Root cause: [X]. Estimated fix: 30min. Should we proceed?"

Core Workflows

Workflow 1: Email Processing → Content Generation Pipeline

User Input: "Process all emails and generate followup content for warm leads"

Orchestrator Steps:

1. Log workflow start

   POST audit: action="workflow_start", resource="email_content_pipeline"

2. Execute Email Agent

   - Call: npm run email-agent
   - Wait for completion
   - Capture: processed count, errors, audit logs

3. Evaluate Results

   IF processed > 0:
     Continue to step 4
   ELSE:
     Notify user "No new emails to process"
     Exit workflow

4. Update Contact Scores

   FOR each processed email:
     - Get updated contact AI score
     - Filter: aiScore >= 70 (warm leads)
     - Store in memory for content generation

5. Execute Content Agent

   - Call: npm run content-agent
   - Wait for completion
   - Capture: generated count, content types

6. Validate Output

   Query generatedContent:
   - Count drafts created
   - Verify all have status="draft"
   - Check aiModel="sonnet"

7. Generate Report

   Output summary with:
   - Emails processed: X
   - Contacts updated: X
   - Content generated: X
   - High-priority leads identified: X
   - Recommended next actions

8. Log workflow completion

   POST audit: action="workflow_complete", status="success"

Workflow 2: Content Approval → Scheduling

User Input: "Approve top 5 content drafts and schedule for sending"

Orchestrator Steps:

1. Fetch pending approvals

   GET generatedContent:
   - status="draft"
   - Sort by contact.aiScore DESC
   - Limit: 5

2. Validate contacts

   FOR each content:
     - Get contact details
     - Verify status="prospect" (ready to receive)
     - Check lastInteraction < 30 days (recent)

3. Approve content

   FOR each draft:
     POST mutation: content.approve(userId=system)

4. Update contact status

   FOR each contact:
     - Mark nextFollowUp = NOW + 7 days
     - Update lastInteraction = NOW

5. Log audit trail

   FOR each action:
     POST audit event with full details

6. Generate scheduling report

   Output:
   - Total approved: 5
   - Scheduled send time: [user preference]
   - Expected delivery: [time range]
   - Tracking enabled: yes/no

Workflow 3: System Health Check

User Input: "Run system audit"

Orchestrator Steps:

1. Check data integrity

   Verify:
   - All organizations active
   - All users have valid roles
   - All contacts have valid status
   - All emails properly linked

2. Audit recent activities

   Query auditLogs (last 24h):
   - Total actions: X
   - Errors: X
   - Error rate: X%
   - Failed agents: [list]

3. Database health

   Check:
   - All indexes working
   - No orphaned records
   - Data consistency
   - Storage usage

4. Agent performance

   FOR each agent:
     - Last run: [timestamp]
     - Success rate: X%
     - Avg processing time: Xms
     - Last error: [if any]

5. Generate health report

   Output:
   ✅ System Status: [HEALTHY|WARNING|CRITICAL]

   Data Integrity: ✅
   - Organizations: X (active)
   - Users: X
   - Contacts: X
   - Emails: X

   Recent Performance (24h):
   - Actions processed: X
   - Success rate: X%
   - Errors: X

   Agent Status:
   - email-agent: ✅ (last run: Xh ago)
   - content-agent: ✅ (last run: Xh ago)
   - orchestrator: ✅ (self-check)

   Recommendations:
   1. [Action 1]
   2. [Action 2]

Memory Management

The Orchestrator uses persistent memory to track state across runs:

Memory keys stored in aiMemory table:

orchestrator:workflow_state
  - Current workflow ID
  - Status (running, completed, error)
  - Started at timestamp
  - Expected duration

orchestrator:last_email_run
  - Timestamp of last email agent run
  - Emails processed count
  - Errors encountered

orchestrator:last_content_run
  - Timestamp of last content agent run
  - Content generated count
  - Content types distribution

orchestrator:pipeline_cache
  - Contact scores after email run
  - High-priority contacts identified
  - Contacts needing followup

Error Handling Strategy

Error Levels

Level 1: Recoverable

• Single email fails to process
• Claude API timeout (retry)
• Network blip

Action: Log error, skip item, continue

Level 2: Significant

• Contact data missing/invalid
• Email agent fails 50% of batch
• Content generation rate < 80%

Action: Log error, retry with reduced batch, alert user

Level 3: Critical

• Database connection lost
• Claude API down
• All agents failing

Action: Log error, halt workflow, alert immediately

Error Logging

FOR each error:
  POST audit mutation:
  - action: "[agent]_error"
  - status: "error"
  - details: { error_message, stack_trace, context }
  - errorMessage: [human readable]

Command Reference

Start Full Pipeline

User: "Run full workflow: process emails and generate content"

Orchestrator:
1. Execute email-agent
2. Wait for completion
3. Evaluate results
4. Execute content-agent
5. Generate report
6. Log completion

Check Status

User: "What's the status of pending content?"

Orchestrator:
1. Query generatedContent (status="draft")
2. Count by contentType
3. List by contact aiScore
4. Report summary

Health Check

User: "Run system audit"

Orchestrator:
1. Check all tables
2. Verify data integrity
3. Query recent audit logs
4. Check agent health
5. Generate report

Manual Approval

User: "Approve all content for John and Lisa"

Orchestrator:
1. Find content for specified contacts
2. Validate readiness
3. Approve each draft
4. Update contact records
5. Generate audit trail

Report Templates

Pipeline Completion Report

✅ Pipeline Execution Complete

Timeline:
- Start: [timestamp]
- Email processing: [duration]
- Content generation: [duration]
- Total runtime: [duration]

Results:
- Emails processed: X
- New contacts created: X
- Contacts updated: X
- Content generated: X
- Errors: X

By type:
- Followup emails: X
- Proposals: X
- Case studies: X

High-Priority Leads (>80 score):
1. John Smith (TechStartup) - proposal generated
2. Lisa Johnson (eCommerce) - followup generated

Next Actions Recommended:
1. Review and approve X pending content drafts
2. Schedule sends for X contacts
3. Track performance metrics for X campaigns

System Health: ✅ All systems nominal

Integration Points

The Orchestrator coordinates with:

• Email Agent - email processing pipeline
• Content Agent - content generation pipeline
• Convex Database - state persistence
• Claude API - advanced reasoning (future)
• Audit System - compliance tracking
• Memory System - workflow state