Cloud API Integration Skill

File Organization: Split structure. Main SKILL.md for core patterns. See references/ for complete implementations.

1. Overview

Risk Level: HIGH - Handles API credentials, processes untrusted prompts, network exposure, data privacy concerns

You are an expert in cloud AI API integration with deep expertise in Anthropic Claude, OpenAI GPT-4, and Google Gemini APIs. Your mastery spans secure credential management, prompt security, rate limiting, error handling, and protection against LLM-specific vulnerabilities.

You excel at:

• Secure API key management and rotation
• Prompt injection prevention for cloud LLMs
• Rate limiting and cost optimization
• Multi-provider fallback strategies
• Output sanitization and data privacy

Primary Use Cases:

• JARVIS cloud AI integration for complex tasks
• Fallback when local models insufficient
• Multi-modal processing (vision, code)
• Enterprise-grade reliability with security

---

2. Core Principles

1. TDD First - Write tests before implementation. Mock all external API calls.
2. Performance Aware - Optimize for latency, cost, and reliability with caching and connection reuse.
3. Security First - Never hardcode keys, sanitize all inputs, filter all outputs.
4. Cost Conscious - Track usage, set limits, cache repeated queries.
5. Reliability Focused - Multi-provider fallback with circuit breakers.

---

3. Implementation Workflow (TDD)

Step 1: Write Failing Test First

# tests/test_cloud_api.py
import pytest
from unittest.mock import AsyncMock, patch, MagicMock
from src.cloud_api import SecureClaudeClient, CloudAPIConfig

class TestSecureClaudeClient:
    """Test cloud API client with mocked external calls."""

    @pytest.fixture
    def mock_config(self):
        return CloudAPIConfig(
            anthropic_key="test-key-12345",
            timeout=30.0
        )

    @pytest.fixture
    def mock_anthropic_response(self):
        """Mock Anthropic API response."""
        mock_response = MagicMock()
        mock_response.content = [MagicMock(text="Test response")]
        mock_response.usage.input_tokens = 10
        mock_response.usage.output_tokens = 20
        return mock_response

    @pytest.mark.asyncio
    async def test_generate_sanitizes_input(self, mock_config, mock_anthropic_response):
        """Test that prompts are sanitized before sending."""
        with patch('anthropic.Anthropic') as mock_client:
            mock_client.return_value.messages.create.return_value = mock_anthropic_response

            client = SecureClaudeClient(mock_config)
            result = await client.generate("Test <script>alert('xss')</script>")

            # Verify sanitization was applied
            call_args = mock_client.return_value.messages.create.call_args
            assert "<script>" not in str(call_args)
            assert result == "Test response"

    @pytest.mark.asyncio
    async def test_rate_limiter_blocks_excess_requests(self):
        """Test rate limiting blocks requests over threshold."""
        from src.cloud_api import RateLimiter

        limiter = RateLimiter(rpm=2, daily_cost=100)

        await limiter.acquire(100)
        await limiter.acquire(100)

        with pytest.raises(Exception):  # RateLimitError
            await limiter.acquire(100)

    @pytest.mark.asyncio
    async def test_multi_provider_fallback(self, mock_config):
        """Test fallback to secondary provider on failure."""
        from src.cloud_api import MultiProviderClient

        with patch('src.cloud_api.SecureClaudeClient') as mock_claude:
            with patch('src.cloud_api.SecureOpenAIClient') as mock_openai:
                mock_claude.return_value.generate = AsyncMock(
                    side_effect=Exception("Rate limited")
                )
                mock_openai.return_value.generate = AsyncMock(
                    return_value="OpenAI response"
                )

                client = MultiProviderClient(mock_config)
                result = await client.generate("test prompt")

                assert result == "OpenAI response"
                mock_openai.return_value.generate.assert_called_once()

Step 2: Implement Minimum to Pass

# src/cloud_api.py
class SecureClaudeClient:
    def __init__(self, config: CloudAPIConfig):
        self.client = Anthropic(api_key=config.anthropic_key.get_secret_value())
        self.sanitizer = PromptSanitizer()

    async def generate(self, prompt: str) -> str:
        sanitized = self.sanitizer.sanitize(prompt)
        response = self.client.messages.create(
            model="claude-sonnet-4-20250514",
            messages=[{"role": "user", "content": sanitized}]
        )
        return self._filter_output(response.content[0].text)

Step 3: Refactor with Patterns

Apply caching, connection pooling, and retry logic from Performance Patterns.

Step 4: Run Full Verification

# Run all test

...