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orchestrator-agent

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.

231 stars

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/orchestrator-agent/SKILL.md --create-dirs "https://raw.githubusercontent.com/aiskillstore/marketplace/main/skills/cleanexpo/orchestrator-agent/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/orchestrator-agent/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How orchestrator-agent Compares

Feature / Agentorchestrator-agentStandard Approach
Platform SupportmultiLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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.

Which AI agents support this skill?

This skill is compatible with multi.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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