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.

View SKILL.md on GitHub Repository
Stars 163
Forks 31

Install this agent skill to your Project

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

SKILL.md

Unified Delegation Skill

⛔ CRITICAL: No Claude Subagents (MANUAL ENFORCEMENT)

NEVER spawn Claude models (Haiku, Sonnet, Opus) as subagents.

⛔ The deny rules in .claude/settings.local.json DO NOT enforce this. ⛔ Manual verification required before EVERY Task() call.

See CLAUDE.md for the pre-Task() checklist and fallback protocol.

Provider Selection Matrix

Task Type Best Provider Why Fallback 1 Fallback 2 Last Resort
Complex reasoning Kimi K2.5 Most intelligent, 256K context GLM-4.7 MiniMax Claude direct
Image/vision (batch) Kimi K2.5 Built-in vision capability GLM-4.6v — Claude direct
Creative/brainstorming GLM-4.7 Strong creative problem-solving Kimi MiniMax Claude direct
Web research MiniMax Fast, reliable, cheap GLM Kimi Claude direct
Simple file exploration MiniMax Quick turnaround GLM Kimi Claude direct
Batch operations GLM Good parallelism MiniMax Kimi Claude direct
Code review MiniMax Fast blind-spot check Kimi GLM Claude direct

Automatic Fallback Protocol

When a Task() call fails, follow this chain (NEVER fallback to Haiku/Sonnet/Opus):

Primary Failed Try Next Then Try Last Resort
Kimi K2.5 GLM-4.7 MiniMax Claude does work directly
GLM-4.7 MiniMax Kimi K2.5 Claude does work directly
MiniMax GLM-4.7 Kimi K2.5 Claude does work directly

Example:

python
# ❌ WRONG - Kimi failed, fell back to Haiku
Task(model="kimi", ...) → Error
Task(model="haiku", ...) → VIOLATION

# ✅ CORRECT - Kimi failed, fell back to GLM
Task(model="kimi", ...) → Error
Task(model="minimax", ...) → Success

If all cheap models fail:

  1. Do the work yourself (Claude)
  2. Document why all subagents failed
  3. Flag for manual investigation with user
  4. NEVER use Haiku/Sonnet/Opus as fallback

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]

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

majiayu000/claude-skill-registry

agent-ops-spec

Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-state

Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-spec

Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-testing

Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-testing

Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-state

Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.

163 31
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results