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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.

agent-orchestration

Orchestrating multiple LLM agents and steps in a TypeScript/Node/NestJS backend - sequential, parallel, router, orchestrator-workers, evaluator-optimiser, and handoff patterns; shared state; typed message contracts between agents; concurrency limits; durable and resumable execution; timeouts and cancellation; cost control; preventing runaway loops; and preferring deterministic code over LLM routing. Provider-agnostic. Load when coordinating more than one agent or model call, designing handoffs, or making multi-step runs durable and bounded.

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