Kimi Supervisor - Token-Efficient Multi-Agent Orchestration

Provider: Kimi K2.5 (Moonshot AI) Purpose: Claude orchestrates Kimi (research) + MiniMax (review) for verified exploration with massive token savings Cost Savings: 85-93% on exploration vs direct Claude work

When to Use This Skill

Use Kimi Supervisor when:

• Task requires spawning 2+ exploration/research agents
• Need to synthesize information from multiple sources
• Complex research across multiple domains
• Batch operations (file reading, image analysis, web searches)

Don't use when:

• Single straightforward query
• Code writing (Claude handles this directly)
• Architectural decisions (Claude maintains control)

How It Works

Traditional Approach:
  Claude → [Agent1, Agent2, Agent3] → Claude reads all outputs (~10k tokens)

Kimi Supervisor Approach:
  Claude → Kimi K2.5 → [Agent1, Agent2, Agent3] → Kimi synthesizes → MiniMax reviews → Claude reads summary (~1-2k tokens)

Token Efficiency:

• Kimi exploration: ~$0.30/M tokens
• MiniMax review: ~$0.20/M tokens
• Claude Sonnet: ~$15/M tokens (input)
• Savings: ~50-100x on synthesis work

Architecture Pattern (Claude Orchestrates)

CRITICAL: Only Claude can spawn subagents. Kimi cannot spawn its own agents.

┌─ Claude (Main Agent - Orchestrator) ──────────────────┐
│ 1. Receives task requiring exploration                │
│ 2. Constructs Kimi prompt with supervisor template    │
│ 3. Spawns Kimi K2.5 via Task tool                     │
│                                                        │
│    ┌─ Kimi K2.5 (Research Agent) ──────────────┐      │
│    │ • Uses native Glob/Grep/Read tools        │      │
│    │ • Explores codebase, reads files          │      │
│    │ • Synthesizes findings                    │      │
│    │ • Shows work (quotes, paths, assumptions) │      │
│    │ • Returns compressed summary              │      │
│    └───────────────────────────────────────────┘      │
│                                                        │
│ 4. Claude spawns MiniMax for review                   │
│                                                        │
│    ┌─ MiniMax (Review Agent) ─────────────────┐       │
│    │ • Receives Kimi's synthesis              │       │
│    │ • Checks for hallucinations              │       │
│    │ • Validates quotes and file paths        │       │
│    │ • Returns approval/concerns              │       │
│    └──────────────────────────────────────────┘       │
│                                                        │
│ 5. Claude receives both outputs                       │
│ 6. Claude makes decisions based on verified data      │
└────────────────────────────────────────────────────────┘

Token Efficiency:

• Kimi exploration: ~$0.30/M tokens
• MiniMax review: ~$0.20/M tokens
• Claude Sonnet: ~$15/M tokens (input)
• Claude receives compressed summary (~1-2k tokens vs 10k+ raw)
• Savings: 85-93% on exploration phases

Why This Works (Token Economics)

Claude orchestrates, cheaper models execute:

• Claude makes architectural decisions (what it's best at)
• Kimi does deep research (256K context, $0.30/M tokens)
• MiniMax provides review (fast, cheap, $0.20/M tokens)
• Claude receives compressed summaries (1-2k tokens vs 10k+ raw)

Kimi K2.5 Research Capabilities:

• 256K token context (can read many files)
• Built-in vision (can analyze images)
• Native Glob/Grep/Read tools (fast file exploration)
• Strong synthesis abilities (compresses findings well)

MiniMax Review Strengths:

• Fast turnaround (low latency)
• Good at spotting hallucinations (different model = different blind spots)
• Cheap enough to always use ($0.20/M tokens)
• Cross-model verification (not self-review)

Usage Instructions

For Claude (Main Agent)

This skill loads a step-by-step workflow for you to manually orchestrate Kimi + MiniMax.

Step-by-Step Workflow:

1. Identify exploration task - Recognize when you need to research codebase

2. Spawn Kimi with supervisor prompt:

   ⚠️ IMPORTANT: Kimi requires environment variable setup via scripts/start-kimi.ps1. You CANNOT use model="kimi" as a Task parameter - it will fail with InputValidationError.

   Correct approach: The Kimi Supervisor workflow requires launching Kimi in a separate terminal:

   powershellCopy

   # In separate terminal/session
   .\scripts\start-kimi.ps1
   

   Then delegate work to that Kimi session (not via Task tool model parameter).

3. Receive Kimi's synthesis - Contains quotes, file paths, assumptions, confidence scores

4. Spawn MiniMax for review:

   Copy

   Task(
     subagent_type="general-purpose",
     model="minimax",
     prompt="Review the following Kimi synthesis for hallucinations, errors, and missing context:\n\n[Kimi's output]",
     description="MiniMax reviews Kimi findings"
   )
   

5. Receive MiniMax's verification - APPROVED / NEEDS_REVISION / concerns

6. Make decisions - You now have verified, compressed findings to act on

Anti-Hallucination Protocol

Show Work Requirements

Kimi MUST include in every response:

1. Direct Quotes:

   markdownCopy

   > "Exact text from file.py:42"
   

   • Shows actual code/content found
   • Reduces paraphrasing errors

2. File Paths with Line Numbers:

   Copy

   Found in src/utils.py:156-172
   

   • Verifiable locations
   • Claude can spot-check if suspicious

3. Assumptions List:

   markdownCopy

   ## Assumptions Made
   - Assumed X based on Y
   - Inferred Z from pattern in files A, B, C
   

   • Makes inferences explicit
   • Easier to catch logical leaps

4. New Files Created:

   markdownCopy

   ## Files Created
   - /path/to/new/file.py (42 lines)
   - /path/to/config.json (12 lines)
   

   • Claude knows what to verify
   • No silent file creation

5. Confidence Scores:

   markdownCopy

   Confidence: High (95%) - verified in 3 files
   Confidence: Medium (70%) - inferred from naming patterns
   Confidence: Low (40%) - no direct evidence found
   

MiniMax Review Checklist

MiniMax verifies:

• All code quotes are exact (not paraphrased)
• File paths are complete and valid
• Line numbers match actual file content
• Assumptions are explicitly stated
• No logical leaps without evidence
• New files are documented
• Confidence scores are justified

If MiniMax flags issues:

• Kimi revises output
• Second review pass
• Maximum 2 revision rounds (prevents infinite loops)

Token Optimization Patterns

1. AgentDiet Pattern

• Remove redundant/expired info from trajectories
• 40-60% token savings

2. Observation Masking

• Sliding window for older observations
• 50% cost reduction vs raw agent

3. Parallel Execution

• K2.5 swarm reduces critical-path latency 3-4.5x
• Same tokens, faster completion

4. Escape Hatch

• If Kimi can't verify information:
  Copy

  [UNVERIFIED]: Claim about X - no source found
  Confidence: Low (30%) - proceed with caution
  

Example Usage

Task: Find Dialogue Presentation Patterns in Quest Files

Step 1: Claude spawns Kimi with supervisor prompt

⚠️ NOTE: This example is conceptual. In practice, Kimi must be launched via .\scripts\start-kimi.ps1 in a separate session, NOT via Task tool model parameter.

Conceptual workflow (requires separate Kimi session):

Kimi Prompt Template:
---
You are Kimi K2.5 researching dialogue patterns for Claude.

Task: Find all dialogue presentation patterns in quest files

Deliverable Format:
- Files containing dialogue
- Common patterns (RichTextLabel, dialogue boxes, etc.)
- Direct code quotes with line numbers
- File paths with line numbers

CRITICAL - Show Your Work:
1. Include EXACT code quotes (not paraphrased)
2. File paths with line numbers (path/file.gd:42-56)
3. List all assumptions explicitly
4. Confidence scores with justification

Minimum Confidence: 80%
---

Actual implementation requires:

1. Launch Kimi in separate terminal: .\scripts\start-kimi.ps1
2. Provide prompt to Kimi session manually or via automation
3. Kimi returns synthesis
4. Claude spawns MiniMax to review (via Task tool with model="minimax")
5. Claude receives both outputs and decides

Step 2: Kimi returns synthesis

## Dialogue Patterns Found

### Pattern 1: RichTextLabel with BBCode
> "dialogue_label.text = \"[wave amp=50]\" + npc_text"
Source: game/quests/quest_1.gd:42

**Locations:**
- game/quests/quest_1.gd:42
- game/quests/quest_2.gd:67
- game/ui/dialogue_box.gd:28

**Confidence:** High (90%) - verified in 3 files

### Assumptions Made
- Assumed dialogue_label is RichTextLabel (based on BBCode usage)
- Inferred [wave] is animation effect (not verified in docs)

### Confidence: Overall 85%

Step 3: Claude spawns MiniMax for review

✅ This step works correctly - MiniMax can be spawned via Task tool:

Task(
  subagent_type="general-purpose",
  model="minimax",  # ✅ Valid - MiniMax supports model parameter
  prompt="""Review the following Kimi synthesis for hallucinations and errors:

[Kimi's output above]

Verify:
- Code quotes are exact (not paraphrased)
- File paths are complete with line numbers
- Assumptions are stated explicitly
- No logical leaps without evidence

Return: APPROVED / NEEDS_REVISION with specific concerns
""",
  description="MiniMax reviews Kimi findings"
)

Step 4: MiniMax returns verification

### MiniMax Review

✅ Code quotes appear exact (BBCode syntax present)
✅ File paths include line numbers
✅ Assumptions are explicitly stated
⚠️  Recommend Claude verify game/quests/quest_1.gd:42 line number

**Status:** APPROVED (spot-check recommended)
**Flagged for verification:** quest_1.gd:42

Step 5: Claude makes decisions

Claude now has:

• Compressed findings (2k tokens vs 10k+ if Claude explored directly)
• Cross-model verification (MiniMax reviewed)
• Specific items to spot-check if needed
• High confidence to proceed with implementation

API Configuration

Kimi K2.5 Setup:

# Use existing launcher
.\scripts\start-kimi.ps1

# Or configure environment
$env:ANTHROPIC_BASE_URL = "https://api.moonshot.cn/anthropic/"
$env:ANTHROPIC_API_KEY = "sk-kimi-YOUR_KEY"
$env:ANTHROPIC_MODEL = "kimi-k2.5-thinking"

MiniMax Setup:

# Use existing launcher
.\scripts\start-claude-minimax.ps1

# Or configure environment
$env:ANTHROPIC_BASE_URL = "https://api.minimax.io/anthropic"
$env:ANTHROPIC_API_KEY = "sk-cp-YOUR_KEY"
$env:ANTHROPIC_MODEL = "minimax:m2.1"

Limitations & Caveats

Don't use Kimi Supervisor for:

• ❌ Code writing (Claude is better)
• ❌ Architectural decisions (Claude maintains control)
• ❌ Single-file reads (too much overhead)
• ❌ Simple queries (no coordination needed)

When Kimi may struggle:

• Complex code synthesis (not trained for code generation)
• Subtle semantic understanding (Claude is better)
• Domain-specific reasoning requiring deep context

Fallback strategy:

• If Kimi confidence < threshold: escalate to Claude
• If MiniMax rejects 2x: escalate to Claude
• If task takes >5 min: report progress and continue

Success Metrics

Track these to measure effectiveness:

• Token reduction: Target 70-90% savings on exploration tasks
• Accuracy: MiniMax approval rate should be >80%
• Speed: Parallel execution should be 3-4x faster
• Claude iterations: Fewer fixes needed post-handoff

Quality indicators:

• All code quotes are exact
• File paths are verifiable
• Assumptions are explicit
• Confidence scores are accurate

Integration with Existing Skills

Complements:

• /skill delegation - Kimi supervisor is one delegation pattern
• /review - MiniMax review mirrors the /review workflow
• /longplan - Use Kimi for research phases in long plans
• /ralph - Kimi can coordinate ralph subagents

Replaces:

• Manual spawning of 3+ Explore agents in parallel
• Direct Claude reading of multiple agent outputs
• Self-review (now cross-model MiniMax review)

Next Steps

After using this skill:

1. Verify Kimi's work (spot-check high-value claims)
2. Make architectural decisions (Claude's role)
3. Write implementation (Claude writes code)
4. Consider updating token budget (you saved tokens!)

Future enhancements:

• Auto-select Kimi vs GLM based on task type
• Track token savings per session
• Build confidence score history
• Create Kimi swarm templates for common patterns

Updated: 2026-01-29 Model: Kimi K2.5 (kimi-k2.5-thinking) Review Protocol: MiniMax M2.1 Cost: ~$0.30-0.50/M tokens (vs $15/M for Claude)