name: subagent-optimizer description: Identify opportunities to parallelize work using sub-agents. Use when receiving multi-item tasks, batch operations, or work that could benefit from concurrent execution. Triggers on phrases like "research these 5 companies", "create 3 variations", "check all of these", or any task with multiple independent items.

Sub-Agent Optimizer

Recognize when to spawn sub-agents for parallel execution, and audit agent configs to enable sub-agent capabilities.

Two Modes

1. Runtime Mode (Pattern Recognition)

Triggers automatically when receiving parallelizable tasks.

2. Audit Mode (Config Optimization)

Triggers on: "optimize agents", "audit agent config", "check subagent permissions"

Runtime Mode: Decision Framework

Spawn sub-agents when:

Task has multiple independent items (5 leads, 3 drafts, 10 links)
Items don't depend on each other's results
Time savings justify the overhead
Each item takes >30 seconds of work

Do it yourself when:

Items depend on previous results
Only 1-2 items
Task requires your accumulated context
Coordination overhead exceeds time savings

Pattern Recognition

Immediate Spawn Triggers

Pattern	Example	Action
"Research these N items"	"Research these 5 companies"	Spawn N research workers
"Create N variations"	"Write 3 headline options"	Spawn N content workers
"Check/verify all of X"	"Verify all 10 links work"	Spawn workers per batch
"For each X, do Y"	"For each competitor, analyze pricing"	Spawn per item
"In parallel" / "simultaneously"	"Research both simultaneously"	Explicit parallel request

Batch Sizing

Small batch (2-3 items): Consider doing sequentially
Medium batch (4-10 items): Spawn sub-agents
Large batch (10+ items): Spawn in waves of 5-8 to avoid overwhelming

Spawn Pattern

# Standard parallel spawn
for item in items:
    sessions_spawn(
        agentId="research-agent",  # or same type for parallel work
        task=f"[Specific task for {item}]. Return: [expected output format]."
    )

# Results announce back → aggregate → deliver

Sub-Agent Selection

Task Type	Spawn Agent Type	Why
Research, fact-finding	Research-specialized agent	Optimized for discovery
Content drafts, copy	Content agent (or self)	Voice-consistent
Data processing	Self (parallel instances)	Same capabilities needed
Personalization at scale	Self	Context-aware

Audit Mode: Config Optimization

When triggered with "optimize agents" or "audit agent config":

Step 1: Read Current Config

cat ~/.openclaw/openclaw.json | jq '.agents.list[] | {id: .id, name: .name, subagents: .subagents}'

Step 2: Analyze Each Agent

For each agent, check:

Can it spawn sub-agents of its own type? (parallel self-work)
Can it spawn research agents? (delegation)
Can it escalate to orchestrator? (getting help)

Step 3: Identify Gaps

Common issues:

Agent can only spawn "main" (can't parallelize own work)
Research agent can't spawn itself (can't parallel research)
Content agent can't spawn research (can't delegate discovery)

Step 4: Recommend Fixes

AUDIT RESULTS:

✅ orchestrator: Can spawn [content, research, sales] — Good
⚠️  content-agent: Can only spawn [main] — Limited
   → Recommend: Add [content, research] for parallel drafts + research delegation
⚠️  research-agent: Can only spawn [main] — Limited  
   → Recommend: Add [research] for parallel deep-dives
✅ sales-agent: Can spawn [sales, research, main] — Good

Apply recommended fixes? [y/n]

Step 5: Apply Fixes (with confirmation)

Edit ~/.openclaw/openclaw.json to update subagents.allowAgents arrays, then:

# Restart gateway to apply
openclaw gateway restart --reason "Updated subagent permissions"

Note (OpenClaw 2026.1.30+): Sub-agent announce routing now prefers requesterOrigin over stale session entries, improving result delivery reliability.

Example Audit Script

# Pseudo-code for audit logic
def audit_agents():
    config = read_config("~/.openclaw/openclaw.json")
    recommendations = []
    
    for agent in config.agents.list:
        allowed = agent.subagents.allowAgents or []
        agent_type = agent.id
        
        # Check: Can agent parallelize its own work?
        if agent_type not in allowed:
            recommendations.append({
                "agent": agent_type,
                "add": agent_type,
                "reason": "Enable parallel self-spawning"
            })
        
        # Check: Can agent delegate research?
        if "research" not in allowed and agent_type != "research":
            recommendations.append({
                "agent": agent_type,
                "add": "research",
                "reason": "Enable research delegation"
            })
    
    return recommendations

Integration

Add to AGENTS.md for persistent behavior:

### Sub-Agent Parallelization

When receiving tasks with multiple items:
1. Check if items are independent
2. Spawn sub-agents for parallel execution
3. Aggregate results when all complete

Use `sessions_spawn` with appropriate agentId. Batch large requests (5-8 at a time).

To audit permissions: "optimize agents" or "audit agent config"

Anti-Patterns

❌ Spawning for single items (overhead not worth it) ❌ Spawning when items depend on each other ❌ Spawning 20+ workers simultaneously (rate limits) ❌ Forgetting to aggregate results after spawn ❌ Agents that can't spawn themselves (limits parallelization)

Name	subagent-optimizer
Description	Identify opportunities to parallelize work using sub-agents. Use when receiving multi-item tasks, batch operations, or work that could benefit from concurrent execution. Triggers on phrases like "research these 5 companies", "create 3 variations", "check all of these", or any task with multiple independent items.

subagent-optimizer

SKILL.md