smithery.ai

subagent-optimizer

Identify opportunities to parallelize work using sub-agents.

First seen Apr 14, 2026

Installation

$ npx skills add https://smithery.ai

Summary

  • 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.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents clawdbot

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 5,746 B
  • docs SUMMARY.md 340 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

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)