delegation

name: Delegation description: Parallelize work via background/foreground agents, built-in types, custom agents, or agent teams/swarms. USE WHEN 3+ independent workstreams, parallel execution, agent specialization, Extended+ effort, agent team, swarm, create an agent team.

Delegation — Agent Orchestration & Parallelization

Auto-invoked by the Algorithm when work can be parallelized or requires agent specialization.

🚨 CRITICAL ROUTING — Two COMPLETELY Different Systems

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

When the Algorithm Should Use This Skill

• 3+ independent workstreams exist at Extended+ effort level
• Multiple identical non-serial tasks need parallel execution
• Specialized expertise needed (architecture design, implementation, ISC optimization)
• Large codebase changes spanning 5+ files benefit from parallel workers
• Research + execution can proceed simultaneously
• "Create an agent team" — use TeamCreate for persistent coordinated teams

Delegation Patterns

1. Built-In Agents

Use Task(subagent_type="AgentType") with these specialized agents:

Always include: Full context, effort budget, expected output format.

2. Worktree-Isolated Agents

Run agents in their own git worktree with isolation: "worktree" for file-safe parallelism:

Task(subagent_type="Engineer", isolation: "worktree", prompt="...")

• Each agent gets its own working tree — no file conflicts with other agents
• Worktree auto-created on spawn, auto-cleaned when agent finishes (unless changes made)
• Use when multiple agents edit the same files or for competing approaches
• Can combine with run_in_background: true for non-blocking isolated work
• Built-in agents with isolation: worktree in frontmatter (Engineer, Architect) auto-isolate on every spawn

3. Background Agents

Run agents with run_in_background: true for non-blocking parallel work:

Task(subagent_type="Engineer", run_in_background: true, prompt="...")

• Use when results aren't needed immediately
• Check output with Read tool on the output_file path
• Ideal for: research, long builds, parallel investigations

3. Foreground Agents

Standard Task() calls that block until complete:

• Use when you need the result before proceeding
• Use for sequential dependencies
• Default mode — most common

4. Custom Agents (via Agents Skill)

Trigger: "custom agents", "spin up agents", "launch agents", "specialized agents" Action: Invoke the Agents skill → run ComposeAgent.ts → launch with Task(subagent_type="general-purpose")

# Step 1: Compose agent identity
bun run ~/.claude/skills/Agents/Tools/ComposeAgent.ts --traits "security,skeptical,thorough" --task "Review auth" --output json

# Step 2: Launch with composed prompt
Task(subagent_type="general-purpose", prompt=<ComposeAgent JSON .prompt field>)

• Each agent gets unique personality, voice, and color via ComposeAgent
• Use DIFFERENT trait combinations for each agent to get unique voices
• Never use built-in agent types (Engineer, Architect) for custom work
• Ideal for: domain experts, adversarial reviewers, creative brainstormers, parallel analysis

5. Agent Teams (via TeamCreate)

Trigger: "create an agent team", "agent team", "swarm", "team of agents" Action: Use TeamCreate tool → TaskCreate → spawn teammates via Task(team_name=...) → coordinate via SendMessage

1. TeamCreate(team_name="my-project")           # Creates team + task list
2. TaskCreate(subject="Implement auth module")   # Create team tasks
3. Task(subagent_type="Engineer", team_name="my-project", name="auth-engineer")  # Spawn teammate
4. TaskUpdate(taskId="1", owner="auth-engineer") # Assign task
5. SendMessage(type="message", recipient="auth-engineer", content="...")  # Coordinate

This is a COMPLETELY DIFFERENT system from custom agents:

• Custom agents (Agents skill) = fire-and-forget parallel workers, no shared state
• Agent teams (TeamCreate) = persistent coordinated teams with shared task lists, messaging, multi-turn

Team Guidelines:

• Use for 3+ independently workable criteria at Extended+
• Large complex coding tasks benefit most
• Each teammate works independently on assigned tasks via shared task list
• Parent coordinates via SendMessage, reconciles results
• Teammates go idle between turns — send messages to wake them

6. Parallel Task Dispatch

For N identical operations (e.g., updating 10 files with the same pattern):

1. Create N Task() calls in a single message (parallel launch)
2. Each agent gets one unit of work
3. Results collected when all complete

Effort-Level Scaling

Two-Tier Delegation (Lightweight vs Full)

Not all delegation needs a full agent. Match delegation weight to task complexity:

Lightweight Delegation

For: One-shot extraction, classification, summarization, simple Q&A against provided content.

Task(subagent_type="general-purpose", model="haiku", max_turns=3, prompt="...")

• Use model="haiku" for cost/speed efficiency
• Set max_turns=3 — if it can't finish in 3 turns, it needs full delegation
• Provide all input inline in the prompt (no tool use expected)
• Examples: "Classify this text as X/Y/Z", "Extract the 5 key points from this", "Summarize this in 2 sentences"

Full Delegation

For: Multi-step reasoning, tasks requiring tool use (file reads, searches, web), tasks that need their own iteration loop.

Task(subagent_type="general-purpose", prompt="...")  # or specialized agent type

• Default model (sonnet/opus inherited from parent)
• No max_turns restriction — agent iterates until done
• Agent uses tools autonomously (Read, Grep, Bash, etc.)
• Examples: "Research X and produce a report", "Refactor these 5 files", "Debug why test Y fails"

Decision Rule

Ask: "Can this be answered in one LLM call with no tool use?" → Lightweight. Otherwise → Full.

Why this matters: Spawning a full agent for a one-shot extraction wastes ~10-30s of startup overhead and unnecessary context. Lightweight delegation returns in 2-5s. Over an Extended+ Algorithm run with 10+ delegations, this saves minutes. Inspired by RLM's llm_query() vs rlm_query() two-tier pattern (Zhang/Kraska/Khattab 2025).

Anti-Patterns (Don't Do These)

• Don't delegate what Grep/Glob/Read can do in <2 seconds
• Don't spawn agents for single-file changes
• Don't create teams for fewer than 3 independent workstreams
• Don't send agents work without full context — they start fresh
• Don't use built-in agent names for custom agents
• Don't use full delegation for one-shot extraction/classification — use lightweight tier