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kimi-k2.5

Kimi K2.5 setup and usage patterns. Most capable subagent with 256K context and built-in vision. Use for complex reasoning and batch image analysis.

SKILL.md

Full skill instructions

Kimi K2.5 Integration

Overview

Kimi K2.5 is the most capable subagent available in this project. Use it when tasks require:

  • Complex multi-step reasoning
  • Batch image/​vision analysis (10+ images)
  • Very long context understanding (256K tokens)
  • Thinking mode for difficult problems

Quick Setup

Launcher Script

.\scripts\start-kimi.ps1

Manual Environment Setup

$env:ANTHROPIC_BASE_URL = "https://api.moonshot.cn/​anthropic/"
$env:ANTHROPIC_API_KEY = "<KIMI_API_KEY>"
$env:ANTHROPIC_MODEL = "kimi-k2.5-thinking"
$env:ANTHROPIC_SMALL_FAST_MODEL = "kimi-k2-turbo-preview"

Available Models

ModelUse CaseContext
kimi-k2.5-thinkingComplex reasoning, main tasks256K
kimi-k2-turbo-previewFast simple tasks256K

Tip: The -thinking variant uses chain-of-thought reasoning, making it better for complex problems but slower for simple queries.


When to Use Kimi K2.5

✅ Use Kimi For:

  • Complex reasoning requiring multiple steps
  • Batch vision (10+ images to analyze)
  • Long documents (large codebase exploration)
  • Difficult problems where GLM/​MiniMax failed
  • Cross-file analysis requiring broad context

❌ Don't Use Kimi For:

  • Simple web searches (use MiniMax)
  • Quick file lookups (use MiniMax)
  • Creative brainstorming (use GLM-4.7)
  • Tasks where speed matters more than quality

Vision Capabilities

Kimi K2.5 has built-in vision. No separate model needed.

Single Image Analysis

Prompt: "Analyze this sprite for pixel art quality issues"
Image: [attached or URL]

Batch Image Analysis

For 10+ images, spawn Kimi as subagent:

Task(
  prompt="Analyze each of these 20 sprites for: transparency, outline quality, shading consistency. Return a table.",
  subagent_type="general-purpose"
)

Delegation Patterns

Pattern 1: Fallback After GLM Fails

1. Claude tries GLM for creative task
2. GLM output is inadequate
3. Claude retries with Kimi K2.5
4. Kimi provides deeper analysis

Pattern 2: Long-Context Research

1. Claude needs to understand 50+ files
2. Claude spawns Kimi: "Read these files and summarize patterns"
3. Kimi processes with 256K context
4. Claude receives condensed findings

Pattern 3: Multi-Step Reasoning

1. Claude faces complex architectural decision
2. Claude spawns Kimi: "Analyze trade-offs between approaches A, B, C"
3. Kimi provides detailed reasoning chain
4. Claude makes final decision

API Configuration

Base URL

https://api.moonshot.cn/​anthropic/

Headers (Anthropic-compatible)

Authorization: Bearer <KIMI_API_KEY>
Content-Type: application/​json

Example Request

curl -s -X POST "https://api.moonshot.cn/​anthropic/​v1/​messages" \
  -H "Authorization: Bearer sk-kimi-..." \
  -H "Content-Type: application/​json" \
  -d '{
    "model": "kimi-k2.5-thinking",
    "max_tokens": 4096,
    "messages": [{"role": "user", "content": "Your prompt here"}]
  }'

Comparison with Other Providers

AspectKimi K2.5GLM-4.7MiniMax
Context256K128K128K
VisionBuilt-inSeparate modelVLM API
ReasoningBestGoodBasic
SpeedMediumMediumFastest
Cost1x1x1x
Best forComplex + VisionCreativeFast tasks

Troubleshooting

"Connection refused"

Check API key is correctly set:

echo $env:ANTHROPIC_API_KEY

"Model not found"

Use exact model names:

  • ✅ kimi-k2.5-thinking
  • ✅ kimi-k2-turbo-preview
  • ❌ kimi-k2.5 (incomplete)

"Rate limited"

Kimi has generous rate limits but may throttle during peak usage. Wait 30 seconds and retry.

"Vision not working"

Ensure image is:

  • PNG, JPEG, or WebP format
  • Under 20MB
  • Accessible (local path or public URL)

Integration with Project

Circe's Garden Use Cases

  1. Sprite batch analysis: Analyze all 45+ placeholder sprites for quality
  2. Dialogue consistency: Check 80+ dialogue files for narrative consistency
  3. Codebase architecture: Understand patterns across game/​features/​*
  4. Visual target comparison: Compare screenshots against Harvest Moon reference

Example: Sprite Quality Audit

Task(
  prompt="""
  Analyze these sprite files for:
  1. Transparency issues (blocky backgrounds)
  2. Outline consistency (1-2px dark outline expected)
  3. Shading quality (SNES Harvest Moon style)
  4. Color palette compliance (see docs/​reference/​concept_art/​HERAS_GARDEN_PALETTE.md)

  Return a table: filename | issues | severity (1-5) | recommendation
  """,
  subagent_type="general-purpose"
)

See Also

  • /​skill delegation - Provider selection matrix
  • /​skill token-efficient-delegation - Cost optimization patterns
  • /​skill image-analysis - GLM-4.6v alternative for vision

[Opus 4.5 - 2026-01-29]

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