Claude-skill-registry 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.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/kimi-k2-5" ~/.claude/skills/majiayu000-claude-skill-registry-kimi-k2-5 && rm -rf "$T"

manifest: skills/data/kimi-k2-5/SKILL.md

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 = "sk-kimi-EpYxHXd4Y0P4pCgjqJUXGmqN1DtwzdQkjMW3LxAleWGPozfXwXibfKSQ2uLZDisd"
$env:ANTHROPIC_MODEL = "kimi-k2.5-thinking"
$env:ANTHROPIC_SMALL_FAST_MODEL = "kimi-k2-turbo-preview"

Available Models

Model	Use Case	Context
`kimi-k2.5-thinking`	Complex reasoning, main tasks	256K
`kimi-k2-turbo-preview`	Fast simple tasks	256K

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

Aspect	Kimi K2.5	GLM-4.7	MiniMax
Context	256K	128K	128K
Vision	Built-in	Separate model	VLM API
Reasoning	Best	Good	Basic
Speed	Medium	Medium	Fastest
Cost	1x	1x	1x
Best for	Complex + Vision	Creative	Fast 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

Sprite batch analysis: Analyze all 45+ placeholder sprites for quality
Dialogue consistency: Check 80+ dialogue files for narrative consistency
Codebase architecture: Understand patterns across game/features/*
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"
)