cloud-api-integration_skill

Overview

This skill provides expert guidance and reusable patterns for securely integrating cloud AI APIs such as Anthropic Claude, OpenAI GPT-4, and Google Gemini. It focuses on protecting credentials, preventing prompt injection, controlling costs, and hardening integrations against data exfiltration. The content emphasizes test-driven development, performance patterns, and multi-provider reliability.

How this skill works

The skill inspects integration points and codifies secure defaults: secret-backed configuration, input sanitization, output filtering, rate limiting, caching, and retry/backoff strategies. It validates patterns through tests that mock external APIs and outlines implementation workflows, from minimal passing code to production-ready refactors. It also maps threats (prompt injection, key exposure, data leaks) to concrete mitigations and operational checks.

When to use it

• Building production cloud LLM connectors that handle user-supplied prompts
• Adding multi-provider fallback and circuit breakers for higher reliability
• Implementing cost controls, rate limits, and usage monitoring for paid models
• Integrating vision or code-model endpoints that require secure I/O and sanitization
• Auditing an existing integration for prompt injection or data exfiltration risks

Best practices

• Never hardcode keys — use environment variables or secret managers and rotate regularly
• Treat all prompts as untrusted: sanitize inputs and enforce output filtering before use
• Implement connection pooling, async clients, and response caching to reduce latency and cost
• Use retry with exponential backoff and circuit breakers; mock all external calls in tests
• Enforce rate limits, spending thresholds, and logging policies that avoid sensitive data in logs

Example use cases

• JARVIS-style assistant that falls back between Claude, GPT-4, and Gemini on failure
• Enterprise chatbot that must strip PII before forwarding prompts to cloud APIs
• Batch processing pipeline that caches repeated requests and batches API calls for efficiency
• Cost-aware service that throttles requests when daily spend thresholds are reached
• Integration test suite that mocks LLM responses to validate sanitization and rate limiting