Agent skill

delegation

Unified provider selection for subagent delegation. Quick decision matrix for choosing between Kimi K2.5, GLM, and MiniMax based on task type.

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/delegation

SKILL.md

Unified Delegation Skill

⛔ CRITICAL: No Claude Subagents

NEVER spawn Claude models (Haiku, Sonnet, Opus) as subagents. Enforced in .claude/settings.local.json deny rules.

Provider Selection Matrix

Task Type Best Provider Why Fallback
Complex reasoning Kimi K2.5 Most intelligent, 256K context GLM-4.7
Image/vision (batch) Kimi K2.5 Built-in vision capability GLM-4.6v
Creative/brainstorming GLM-4.7 Strong creative problem-solving Kimi
Web research MiniMax Fast, reliable, cheap GLM
Simple file exploration MiniMax Quick turnaround any
Batch operations GLM Good parallelism MiniMax
Code review MiniMax Fast blind-spot check Kimi

Quick Decision Flow

┌─ Is it reasoning/decisions? ──────────────────┐
│  YES → Claude does it directly                │
│  NO  → Delegate to subagent ↓                 │
├───────────────────────────────────────────────┤
│                                               │
│  ┌─ Does it need vision? ───────────────────┐ │
│  │  YES → Kimi K2.5 (or GLM-4.6v fallback)  │ │
│  │  NO  ↓                                   │ │
│  └──────────────────────────────────────────┘ │
│                                               │
│  ┌─ Is it complex/creative? ────────────────┐ │
│  │  Complex → Kimi K2.5                     │ │
│  │  Creative → GLM-4.7                      │ │
│  │  Simple → MiniMax                        │ │
│  └──────────────────────────────────────────┘ │
└───────────────────────────────────────────────┘

Provider Profiles

Kimi K2.5 (Most Capable)

Context: 256K tokens | Vision: Yes | Thinking mode: Yes

Best for:

  • Complex multi-step reasoning
  • Batch image analysis (10+ images)
  • Tasks requiring deep understanding
  • Fallback for failed GLM tasks

Launcher: .\scripts\start-kimi.ps1

API Config:

Base URL: https://api.moonshot.cn/anthropic/
Models: kimi-k2.5-thinking, kimi-k2-turbo-preview

GLM-4.7 (Creative)

Context: 128K tokens | Vision: GLM-4.6v variant | Thinking mode: Yes

Best for:

  • Creative brainstorming
  • Mathematical reasoning (95.7% AIME 2025)
  • Parallel batch tasks
  • Tool use orchestration

MCP: .cursor/mcp.json (GLM-4.6v configured)

MiniMax M2.1 (Fast & Cheap)

Context: 128K tokens | Vision: VLM API | Speed: Fastest

Best for:

  • Quick web searches
  • Simple file exploration
  • Structured data extraction
  • Code review for blind spots

Launcher: .\scripts\start-claude-minimax.ps1

MCP: .cursor/mcp.json (MiniMax configured)

Delegation Patterns

Pattern 1: Research → Claude Decides

1. Claude receives task requiring research
2. Claude spawns MiniMax: "Find all uses of X in codebase"
3. MiniMax returns findings
4. Claude reasons and implements

Pattern 2: Batch Vision Analysis

1. Claude needs to analyze 20 sprites
2. Claude spawns Kimi K2.5: "Analyze quality of each sprite"
3. Kimi returns analysis for all 20
4. Claude makes decisions based on report

Pattern 3: Creative Exploration

1. Claude needs alternative approaches
2. Claude spawns GLM-4.7: "Brainstorm 5 solutions for X"
3. GLM returns creative options
4. Claude selects and refines best approach

Pattern 4: Code Review

1. Claude writes code
2. Claude spawns MiniMax: "Check for bugs, edge cases, security issues"
3. MiniMax returns concerns
4. Claude addresses or dismisses with reasoning

Parallel Delegation

Launch multiple subagents in a single message:

Task(prompt="Research X", subagent_type="general-purpose")  ←─┐
Task(prompt="Research Y", subagent_type="general-purpose")  ←─┼─ Parallel
Task(prompt="Research Z", subagent_type="general-purpose")  ←─┘

Rules:

  • Independent tasks → parallel
  • Dependent tasks → sequential
  • Never chain Claude subagents

Background Execution (Token Suspension)

Problem: Claude tokens burn while waiting for subagent results. Solution: Use run_in_background=true + end turn early.

Pattern: Fire-and-Retrieve

1. Claude receives task requiring research
2. Task(prompt="...", run_in_background=true) → returns output_file
3. Claude ends turn: "Research agent dispatched. Say 'continue' for results."
4. User says "continue"
5. TaskOutput(task_id="...", block=true) → retrieves results
6. Claude synthesizes and responds

When to Use Background Execution

Scenario Background? Why
Research >30 sec ✅ Yes Saves expensive Claude wait time
Batch image analysis ✅ Yes Long-running, user can wait
Quick file lookup ❌ No Faster to wait inline
Claude needs result to continue ❌ No Would block anyway

Token Savings Calculation

Blocking:     Claude waits 60s = 60s of Opus tokens burned
Background:   Claude ends turn = 0s of Opus tokens burned
              (subagent tokens are 50x cheaper)

Example Usage

# Fire (spawn and end turn immediately)
Task(
  prompt="Analyze all 20 sprites in assets/sprites/",
  subagent_type="general-purpose",
  run_in_background=true
)
→ Returns: {task_id: "abc123", output_file: "/path/to/output"}

# ... Claude ends turn, tells user to say "continue" ...

# Retrieve (on next turn)
TaskOutput(task_id="abc123", block=true)
→ Returns: Full subagent analysis

Token Economics

Provider Relative Cost When to Use
Claude Opus 50x Final decisions, complex reasoning
Claude Sonnet 10x Medium reasoning (avoid as subagent)
Kimi K2.5 1x Complex tasks, vision
GLM-4.7 1x Creative, batch
MiniMax 1x Fast, simple

Key insight: 1 hour Claude exploration = 50 hours subagent exploration (cost).

Common Mistakes

Mistake Impact Fix
Claude spawning Haiku Expensive Use MiniMax instead
Sequential when parallel possible Slow Single message, multiple Tasks
Kimi for simple lookup Overkill Use MiniMax
MiniMax for complex reasoning Poor quality Use Kimi K2.5
Claude reading 10+ files Context bloat Delegate exploration

Integration with Other Skills

  • /skill kimi-k2.5 - Detailed Kimi setup and patterns
  • /skill minimax-mcp - MiniMax MCP integration details
  • /skill token-efficient-delegation - Full token economics
  • /skill subagent-best-practices - General subagent patterns

[Opus 4.5 - 2026-01-29]

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