Kimi K2.5 Cursor Integration: AI-Powered Coding in Your IDE

// Example values for an OpenAI-compatible provider
{
  "provider": "openai-compatible",
  "apiKey": "YOUR_MOONSHOT_API_KEY",
  "baseUrl": "https://api.moonshot.cn/v1",
  "model": "kimi-k2.5"
}

{
  "provider": "openai-compatible",
  "apiKey": "YOUR_OPENROUTER_API_KEY",
  "baseUrl": "https://openrouter.ai/api/v1",
  "model": "moonshotai/kimi-k2.5"
}

# Type:
def calculate_fibonacci(n):
    # Cursor + Kimi K2.5 suggests:
    """
    Calculate the nth Fibonacci number.

    Args:
        n: Position in Fibonacci sequence (0-indexed)

    Returns:
        The nth Fibonacci number

    Raises:
        ValueError: If n is negative
    """
    if n < 0:
        raise ValueError("n must be non-negative")
    if n <= 1:
        return n

    a, b = 0, 1
    for _ in range(2, n + 1):
        a, b = b, a + b
    return b

User: "Add error handling and logging to this function"
Kimi K2.5: [Generates improved code with try-except blocks and logging]

User: "Explain how this React component works and suggest optimizations"
Kimi K2.5: [Provides detailed explanation and 3 optimization suggestions]

Prompt: "Find all uses of the deprecated API in @src and suggest
modern replacements with implementation examples"

# Select your function
def divide_numbers(a, b):
    return a / b

# Prompt: "Generate comprehensive unit tests including edge cases"
# Kimi K2.5 generates:
import pytest

def test_divide_numbers_normal():
    assert divide_numbers(10, 2) == 5

def test_divide_numbers_float():
    assert divide_numbers(7, 2) == 3.5

def test_divide_numbers_negative():
    assert divide_numbers(-10, 2) == -5

def test_divide_by_zero():
    with pytest.raises(ZeroDivisionError):
        divide_numbers(10, 0)

# Select undocumented code
class DataProcessor:
    def __init__(self, config):
        self.config = config
        self.cache = {}

    def process(self, data):
        if data.id in self.cache:
            return self.cache[data.id]
        result = self._transform(data)
        self.cache[data.id] = result
        return result

# Prompt: "Generate comprehensive docstrings"
# Kimi K2.5 outputs fully documented code

{
  "cursor.customCommands": [
    {
      "name": "Review Security",
      "prompt": "Review this code for security vulnerabilities including SQL injection, XSS, and insecure data handling. Provide specific fixes."
    },
    {
      "name": "Optimize Performance",
      "prompt": "Analyze this code for performance bottlenecks. Suggest optimizations with Big O analysis."
    },
    {
      "name": "Add Type Hints",
      "prompt": "Add comprehensive Python type hints to this code, including generics and overloads where appropriate."
    }
  ]
}

Prompt: "Generate a commit message for the changes in @git diff"

Kimi K2.5: "feat(auth): implement JWT token refresh mechanism

- Add automatic token refresh 5 minutes before expiry
- Store refresh tokens in httpOnly cookies
- Add retry logic for failed authenticated requests
- Update tests for new auth flow"

# Paste an error traceback
Prompt: "Help me debug this error: [paste stack trace]"

Kimi K2.5 analyzes:
1. Root cause identification
2. File and line number highlighting
3. Suggested fixes with code examples
4. Prevention strategies

# Type hint inference
# Prompt: "Add type hints"
def process_data(data, options):
    # Kimi K2.5 suggests:
    from typing import Dict, Any, Optional

    def process_data(
        data: Dict[str, Any],
        options: Optional[Dict[str, bool]] = None
    ) -> Dict[str, Any]:

// React component generation
// Prompt: "Create a React hook for data fetching with caching"

// Kimi K2.5 generates:
import { useCallback, useEffect, useState } from 'react';

interface UseFetchOptions {
  cacheTime?: number;
  retryCount?: number;
}

export function useFetch<T>(url: string, options: UseFetchOptions = {}) {
  // Implementation with proper TypeScript types
}

// Lifetime annotation assistance
// Prompt: "Fix the lifetime issues in this code"

// Kimi K2.5 provides corrected version with:
// - Proper lifetime parameters
// - Ownership clarification
// - Borrowing optimization

# Good: Specific and contextual
"@database.py Review the connection pooling implementation
for thread safety issues"

# Less effective: Vague
"Check this code for bugs"

# Verify your API key
curl https://api.moonshot.ai/v1/models \
  -H "Authorization: Bearer YOUR_KEY"

// Verify configuration
{
  "provider": "openai-compatible",
  "apiKey": "sk-...",
  "baseUrl": "https://api.moonshot.cn/v1",
  "model": "kimi-k2.5"
}

"Create a FastAPI endpoint that accepts JSON,
validates with Pydantic, and stores in PostgreSQL"

"Convert this Python 2 script to Python 3,
following modern best practices and adding type hints"

"@src Explain the architecture of this project
and how data flows from frontend to database"