Delegation & Parallelization Reference
Quick reference in SKILL.md → For full details, see this file
🤝 Delegation & Parallelization (Always Active)
WHENEVER A TASK CAN BE PARALLELIZED, USE MULTIPLE AGENTS!
Model Selection for Agents (CRITICAL FOR SPEED)
The Task tool has a model parameter - USE IT.
Agents default to inheriting the parent model (often Opus). This is SLOW for simple tasks. Each inference with 30K+ context takes 5-15 seconds on Opus. A simple 10-tool-call task = 1-2+ minutes of pure thinking time.
Model Selection Matrix:
| Task Type | Model | Why |
|---|---|---|
| Deep reasoning, complex architecture, strategic decisions | opus | Maximum intelligence needed |
| Standard implementation, moderate complexity, most coding | sonnet | Good balance of speed + capability |
| Simple lookups, file reads, quick checks, parallel grunt work | haiku | 10-20x faster, sufficient intelligence |
Examples:
// WRONG - defaults to Opus, takes minutes
Task({ prompt: "Check if blue bar exists on website", subagent_type: "general-purpose" })
// RIGHT - Haiku for simple visual check
Task({ prompt: "Check if blue bar exists on website", subagent_type: "general-purpose", model: "haiku" })
// RIGHT - Sonnet for standard coding task
Task({ prompt: "Implement the login form validation", subagent_type: "Engineer", model: "sonnet" })
// RIGHT - Opus for complex architectural planning
Task({ prompt: "Design the distributed caching strategy", subagent_type: "Architect", model: "opus" })Rule of Thumb:
- If it’s grunt work or verification →
haiku - If it’s implementation or research →
sonnet - If it requires deep strategic thinking →
opus(or let it default)
Parallel tasks especially benefit from haiku - launching 5 haiku agents is faster AND cheaper than 1 Opus agent doing sequential work.
Agent Types
Default for parallel work: Custom agents via Agents skill (ComposeAgent).
Use the Agents skill to compose task-specific agents with unique traits, voices, and expertise:
- Use a SINGLE message with MULTIPLE Task tool calls
- Each agent gets FULL CONTEXT and DETAILED INSTRUCTIONS via ComposeAgent prompt
- Launch as many as needed (no artificial limit)
- ALWAYS launch a spotcheck agent after parallel work completes
Agent routing by task type:
- Research tasks → Use the Research skill (has dedicated researcher agents)
- Code implementation → Use Engineer agents (
subagent_type: "Engineer") - Architecture/design → Use Architect agents (
subagent_type: "Architect") - Everything else → Use Agents skill → ComposeAgent →
subagent_type: "general-purpose"
🚨 AGENT ROUTING (Always Active)
Two COMPLETELY Different Systems — custom agents vs agent teams:
| User Says | System | Tool | What Happens |
|---|---|---|---|
| ”custom agents”, “spin up agents”, “launch agents” | Agents Skill (ComposeAgent) | Task(subagent_type="general-purpose", prompt=<ComposeAgent output>) | Unique personalities, voices, colors via trait composition |
| ”create an agent team”, “agent team”, “swarm” | Claude Code Teams | TeamCreate → TaskCreate → SendMessage | Persistent team with shared task list, message coordination, multi-turn collaboration |
These are NOT the same thing:
- Custom agents = one-shot parallel workers with unique identities, launched via
Task(), no shared state - Agent teams = persistent coordinated teams with shared task lists, messaging, and multi-turn collaboration via
TeamCreate
Additional routing by task type:
| User Says | What to Use | Why |
|---|---|---|
| ”custom agents”, “spin up custom agents” | ComposeAgent → general-purpose | Unique prompts, unique voices |
| ”spin up agents”, “bunch of agents”, “launch agents” | ComposeAgent → general-purpose | Task-specific agents with proper expertise |
| ”research X”, “investigate Y” | Research skill | Dedicated researcher agents |
| Code implementation tasks | Engineer agent | Specialized for TDD/code |
| Architecture/design tasks | Architect agent | Specialized for system design |
For ALL parallel work:
- Invoke the Agents skill → ComposeAgent for EACH agent with appropriate traits
- Use DIFFERENT trait combinations to get unique voices and expertise
- Launch with the full ComposeAgent-generated prompt as
subagent_type: "general-purpose" - Each agent gets a personality-matched ElevenLabs voice
For research specifically: Use the Research skill, which has dedicated researcher agents (ClaudeResearcher, GeminiResearcher, etc.)
Reference: Agents skill (~/.claude/skills/Agents/SKILL.md)
Full Context Requirements: When delegating, ALWAYS include:
- WHY this task matters (business context)
- WHAT the current state is (existing implementation)
- EXACTLY what to do (precise actions, file paths, patterns)
- SUCCESS CRITERIA (what output should look like)
- TIMING SCOPE (fast|standard|deep) — controls agent output verbosity
Timing Scope in Agent Prompts
Every agent prompt MUST include a ## Scope section that matches the validated timing tier from the Algorithm’s THINK phase. This prevents agents from over-producing on simple tasks or under-delivering on complex ones.
Timing + Model Selection:
| Timing | Model | Agent Output | Example |
|---|---|---|---|
| fast | haiku | <500 words, direct answer | ”Check if server is running” |
| standard | sonnet | <1500 words, focused work | ”Implement login validation” |
| deep | opus | No limit, thorough analysis | ”Comprehensive security audit” |
Examples:
// FAST — simple check, haiku model, minimal output
Task({
prompt: `Check if the auth middleware exports are correct.
## Scope
Timing: FAST — direct answer only.
- Under 500 words
- Answer the question, report the result, done`,
subagent_type: "Explore",
model: "haiku"
})
// STANDARD — typical implementation work
Task({
prompt: `Implement input validation for the login form.
## Scope
Timing: STANDARD — focused implementation.
- Under 1500 words
- Stay on task, deliver the work, verify it works`,
subagent_type: "Engineer",
model: "sonnet"
})
// DEEP — comprehensive analysis
Task({
prompt: `Perform a thorough security review of all auth flows.
## Scope
Timing: DEEP — comprehensive analysis.
- No word limit
- Explore alternatives, consider edge cases
- Thorough verification and documentation`,
subagent_type: "Pentester",
model: "opus"
})See Also:
- SKILL.md > Delegation (Quick Reference) - Condensed trigger table
- Workflows/Delegation.md - Operational delegation procedures
- Workflows/BackgroundDelegation.md - Background agent patterns
- skills/Agents/SKILL.md - Custom agent creation system
Subagent Depth Cap (harness constraint, v2.1.172+)
Subagents may spawn their own subagents to a maximum of 5 levels deep (foreground and background alike, enforced since v2.1.181). Design delegation chains (TeamLead → specialist → helper → …) within this ceiling; a chain that would exceed it fails at spawn, not gracefully. Prefer flat fan-out from a coordinator over deep nesting — depth spends the cap and hides context.