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davidbirchall/squad-skills-ai-engineering

v0.2.0

AI engineering skills for LLM-powered applications and agent systems: LLM integration, prompt engineering, agent design and orchestration, tool and MCP integration, context engineering, retrieval-augmented generation, evaluation, testing, security, and observability.

llm-integration

Integrating LLM providers into a TypeScript/NestJS backend - provider-agnostic abstraction, streaming to clients, structured output with schema validation and repair, timeouts, cancellation, retries with backoff and jitter, rate limit handling, fallbacks across models and providers, token and cost budgets, usage accounting, API key handling, idempotency, and error mapping. Load when adding or changing a model call, wiring a new provider, handling streaming responses, or debugging flaky, slow or expensive LLM requests.

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