Parallel Orchestrator

Parallel Orchestrator Skill

When to Activate This Skill

Trigger keywords:

• "parallel", "concurrent", "simultaneously"
• "multiple agents", "spawn agents", "Task tool"
• "work in parallel", "parallelize"
• "speed up", "faster completion"

Trigger situations:

• Planning work that could run concurrently
• Multiple independent features ready to implement
• Using Claude Code's Task tool for sub-agents
• Coordinating 2+ agents on different tasks

Core Principle: 6-Phase Parallel Workflow

HtmlGraph provides ParallelWorkflow for optimal parallel agent execution:

┌─────────────────────────────────────────────────────────────────┐
│  1. ANALYZE     →  Check dependencies, assess risks            │
│  2. PREPARE     →  Cache shared context, isolate tasks         │
│  3. DISPATCH    →  Generate prompts, spawn agents              │
│  4. MONITOR     →  Track health, detect anti-patterns          │
│  5. AGGREGATE   →  Collect results, check conflicts            │
│  6. VALIDATE    →  Verify outputs, update dependencies         │
└─────────────────────────────────────────────────────────────────┘

Phase 1: Pre-Flight Analysis (REQUIRED)

Always run analysis before dispatching agents!

from htmlgraph import SDK

sdk = SDK(agent="orchestrator")

# Method 1: Quick check
parallel = sdk.get_parallel_work(max_agents=5)
print(f"Max parallelism: {parallel['max_parallelism']}")
print(f"Ready now: {parallel['ready_now']}")

# Method 2: Full analysis with ParallelWorkflow
plan = sdk.plan_parallel_work(max_agents=3)

if plan["can_parallelize"]:
    print(f"✅ Parallelize {plan['task_count']} tasks")
    print(f"   Speedup: {plan['speedup_factor']:.1f}x")
    print(f"   Ready: {plan['ready_tasks']}")
else:
    print(f"⚠️ {plan['recommendation']}")

Decision Criteria

Phase 2: Context Preparation

Reduce redundant file reads by pre-caching shared context:

# Identify files ALL agents need
shared_files = [
    "src/models.py",      # Data models
    "src/config.py",      # Configuration
    "tests/conftest.py",  # Test fixtures
]

# Plan with shared context
plan = sdk.plan_parallel_work(
    max_agents=3,
    shared_files=shared_files
)

What Preparation Does

1. Reads shared files once (not per-agent)
2. Generates summaries for agent context
3. Identifies file conflicts before dispatch
4. Creates isolation rules (which files each agent owns)

Phase 3: Dispatch with Task Tool

CRITICAL: Send ALL Task calls in a SINGLE message for true parallelism!

# Get ready-to-use prompts
prompts = plan["prompts"]

# CORRECT: All in one message (parallel)
for p in prompts:
    Task(
        subagent_type="general-purpose",
        prompt=p["prompt"],
        description=p["description"]
    )

# WRONG: Sequential messages (not parallel)
# result1 = Task(...)  # Wait for completion
# result2 = Task(...)  # Then next one

Prompt Structure (Auto-Generated)

Each prompt includes:

## Task: {feature_id}
Title: {title}
Priority: {priority}

## Your Assignment
{specific_instructions}

## Pre-Cached Context (DO NOT re-read these)
- models.py: Contains User, Session, Feature classes
- config.py: DATABASE_URL, API_KEY settings

## Files to AVOID (other agents editing)
- {files_assigned_to_other_agents}

## Efficiency Guidelines
- Use Grep before Read (search then read)
- Batch Edit operations
- Mark feature complete when done

Phase 4: Monitor (During Execution)

Agents track their own health via transcript analytics:

Health Metrics Tracked

Anti-Patterns Detected

# These patterns trigger warnings:
("Read", "Read", "Read")     # Cache instead
("Edit", "Edit", "Edit")     # Batch edits
("Bash", "Bash", "Bash", "Bash")  # Check errors
("Grep", "Grep", "Grep")     # Read results first

Phase 5: Aggregate Results

After all agents complete:

# Collect agent IDs from Task tool responses
agent_ids = ["agent-abc123", "agent-def456", "agent-ghi789"]

# Aggregate with SDK
results = sdk.aggregate_parallel_results(agent_ids)

print(f"Successful: {results['successful']}/{results['total_agents']}")
print(f"Health: {results['avg_health_score']:.0%}")
print(f"Speedup: {results['parallel_speedup']:.1f}x")
print(f"Conflicts: {results['conflicts']}")
print(f"Anti-patterns: {results['total_anti_patterns']}")

Result Structure

{
    "total_agents": 3,
    "successful": 3,
    "failed": 0,
    "total_duration_seconds": 450.0,
    "parallel_speedup": 2.3,
    "avg_health_score": 0.80,
    "total_anti_patterns": 4,
    "files_modified": ["auth.py", "api.py", "tests/..."],
    "conflicts": [],  # Empty = good!
    "recommendations": [...],
    "validation": {
        "no_conflicts": True,
        "all_successful": True,
        "healthy_execution": True,
    },
    "all_passed": True
}

Phase 6: Validate

if results["all_passed"]:
    print("✅ Parallel execution validated!")
    # Commit all changes together
else:
    # Handle issues
    for rec in results["recommendations"]:
        print(f"⚠️ {rec}")

Optimal Tool Patterns

DO (Efficient)

DON'T (Anti-Patterns)

When NOT to Parallelize

Integration with Other Skills

With htmlgraph-tracker

• Parallel agents each track their own session
• Activities attributed to features automatically
• Drift detection per agent

With strategic-planning

• Use find_bottlenecks() before parallel dispatch
• Prioritize work that unlocks most downstream tasks
• Assess risks before large parallel batches

Quick Reference

from htmlgraph import SDK

sdk = SDK(agent="orchestrator")

# 1. Plan
plan = sdk.plan_parallel_work(max_agents=3)

# 2. Check
if plan["can_parallelize"]:
    # 3. Dispatch (all at once!)
    for p in plan["prompts"]:
        Task(prompt=p["prompt"], ...)

    # 4. Aggregate (after completion)
    results = sdk.aggregate_parallel_results(agent_ids)

    # 5. Validate
    if results["all_passed"]:
        print("✅ Success!")

Troubleshooting

"Not enough independent tasks"

• Check dependency graph: sdk.get_parallel_work()
• Resolve bottlenecks first: sdk.find_bottlenecks()

High anti-pattern count

• Add shared file caching
• Review agent prompts for efficiency guidelines

File conflicts detected

• Improve task isolation in Phase 2
• Consider sequential execution for overlapping work

Low health scores

• Check retry rates (consecutive same-tool usage)
• Add more context to agent prompts
• Use Grep before Read pattern