Token-Efficient Delegation

⛔ CRITICAL: No Claude Subagents

HARD STOP: Never use model="haiku", model="sonnet", or model="opus" in Task tool calls.

• Always use: model parameter omitted (routes to MiniMax/GLM)
• Or explicitly: general-purpose agent (uses cheaper default)
• Enforced in: .claude/settings.local.json deny rules

Core Rule: Claude = reasoning/decisions, MiniMax/GLM = research/exploration

The Three Goals (Balance All Three)

Quick Decision Tree

Before ANY task, ask:

1. Research/exploration? → Delegate to MiniMax (fast) or GLM (creative)
2. Web search needed? → Delegate to MiniMax
3. Reading specific known file? → Claude direct (Read tool)
4. Analyzing 1-3 images? → Claude direct
5. Analyzing 10+ images? → Parallel subagents (GLM for quality, MiniMax for speed)
6. Complex reasoning/code? → Claude direct
7. Broad codebase search? → Delegate to general-purpose agent (cheaper default)

Model Characteristics (Official Guidance)

When to Use Each Model

MiniMax M2.1 - Fast Instruction-Follower

Source: MiniMax Official Docs

• Quick web searches
• Structured data extraction
• File reading with specific questions
• Tasks with clear instructions
• 8% of Claude cost, 2x speed
• Preserve reasoning chains (return full response with thinking field)

GLM-4.7 - Creative Problem-Solver

Source: Z.AI GLM-4.7 Docs

• Complex problem exploration
• Mathematical reasoning (95.7% on AIME 2025)
• Creative solutions needed
• Tool use orchestration (42.8%, ties GPT-5.1)
• Parallel batch tasks (latency doesn't matter)
• Use thinking mode for complex tasks, disable for simple queries

GLM-4V - Vision Tasks

Source: GLM-V GitHub

• 66K-token multimodal context
• Screenshot/GUI analysis
• Document parsing (PDFs, charts)
• Video understanding with timestamps
• Multi-image analysis

Claude (Haiku/Sonnet/Opus) - Reasoning & Decisions

Source: Anthropic Subagents Docs

• Haiku: Fast exploration, read-only tasks, classification
• Sonnet: Coding, medium-complexity reasoning
• Opus: Architecture decisions, synthesis, complex reasoning
• Use subagents to isolate verbose output from main context

Quality Preservation Patterns

Pattern 1: Orchestrator-Worker Split

Claude (Orchestrator) → Spawns subagents → Subagents research → Claude synthesizes

Quality preserved because: Claude makes all decisions based on subagent research.

Pattern 2: Parallel Research + Single Synthesis

5 MiniMax agents research in parallel → Claude receives summaries → Claude decides

Quality preserved because: Multiple perspectives, Claude does final reasoning.

Pattern 3: Two-Stage Review

Subagent implements → Reviewer subagent checks → Claude integrates

Quality preserved because: Cross-validation catches errors before Claude acts.

Pattern 4: Specific Prompts

Source: MiniMax Best Practices

"State the 'why' behind your request - when models understand purpose, they provide more accurate answers."

BAD:  "Research authentication"
GOOD: "Find how Godot 4.5 handles input authentication. I need to implement player login. Return: method name, file location, example usage."

Speed Optimization Patterns

Pattern 1: Parallel Execution

Launch independent tasks simultaneously:

Task(prompt="Research X")  ←─┐
Task(prompt="Research Y")  ←─┼─ Single message, parallel execution
Task(prompt="Research Z")  ←─┘

Pattern 2: Right Model for Latency

Pattern 3: Batch Over Sequential

BAD:  Analyze image 1 → wait → Analyze image 2 → wait → ...
GOOD: Spawn 10 parallel agents → each analyzes 1 image → aggregate

Cost Optimization Patterns

Pattern 1: Delegate Exploration

Source: Anthropic Advanced Tool Use

"Delegate verbose operations (tests, log processing, documentation fetching) to subagents so output stays isolated from main conversation."

Pattern 2: Context Isolation

Subagents prevent context bloat:

Main context: 5000 tokens (stays lean)
Subagent reads 10 files: 15000 tokens (isolated, discarded after)
Result passed back: 500 tokens (only what matters)

Pattern 3: Cascade Routing

Source: Anthropic Model Selection

60-70% of queries → Haiku (cheapest)
20-30% of queries → Sonnet (medium)
10-15% of queries → Opus (only when needed)

Pattern 4: Token Suspension via Background Execution

The Problem: Claude tokens burn while waiting for subagent results.

The Solution: Use run_in_background=true and end the turn early.

┌─────────────────────────────────────────────────────────┐
│ BLOCKING (expensive)                                    │
├─────────────────────────────────────────────────────────┤
│ Claude spawns subagent → waits 60s → gets result       │
│ Cost: 60 seconds of Opus tokens BURNED                  │
├─────────────────────────────────────────────────────────┤
│ BACKGROUND + END TURN (cheap)                           │
├─────────────────────────────────────────────────────────┤
│ Claude spawns with run_in_background=true → ends turn  │
│ Cost: ~0 seconds of Opus tokens (turn ended)           │
│ Subagent runs: 60s of cheap tokens                      │
│ User says "continue" → Claude retrieves with TaskOutput│
└─────────────────────────────────────────────────────────┘

When to suspend (use background):

• Research/exploration tasks >30 seconds
• Batch image analysis (10+ images)
• Web searches with multiple queries
• Code review by subagent

When NOT to suspend:

• Quick lookups (<5 seconds)
• Claude needs result to continue current reasoning
• Interactive debugging requiring rapid iteration

Fire-and-Retrieve Workflow:

1. Task(prompt="Research X", run_in_background=true)
   → Returns immediately with task_id and output_file
2. Claude responds: "Research dispatched. Say 'continue' when ready."
3. User prompts again
4. TaskOutput(task_id="...", block=true)
   → Returns full results
5. Claude synthesizes and delivers answer

Key phrase: For tasks >30 seconds, use background execution and end turn early.

Anti-Patterns to Avoid

Integration with Project Workflows

HPV (Playtesting)

• Use MCP for game state inspection (local, fast)
• Delegate screenshot analysis to GLM-4V for batch checks

Visual Development

• 1-3 sprites: Claude direct
• 10+ sprites: Parallel GLM-4V agents
• Quality gate: Claude reviews subagent findings

Code Implementation

• Research patterns: MiniMax subagents
• Write code: Claude direct
• Review: Haiku subagent for spec compliance

Official Documentation Sources

• Anthropic Subagents
• Anthropic Advanced Tool Use
• Anthropic Token-Efficient Tools
• MiniMax Best Practices
• MiniMax Agent Learnings
• Z.AI GLM-4.7 Docs
• GLM-V Vision Model

[Claude Opus 4.5 - 2026-01-29]