Does every system need to be distributed?

No. It seeks the simplest architecture that satisfies real correctness, scale, reliability and organizational requirements.

Does a timeout mean the operation failed?

No. It models uncertain remote effects and designs idempotency or reconciliation rather than blind retries.

Can it prove a design meets our SLO?

It can define validation and failure scenarios, but actual targets need representative measurement and operational exercises.

Distributed Systems Architect — Design for Scale

Example session with this skill installed

Illustrative scenario: Review an order-processing proposal: API writes an order, then calls a shipping service; on timeout it retries the whole request. No idempotency key or durable workflow state exists. Shipping behavior and transaction semantics are unknown. Provide a failure-model review without implementing or claiming exactly-once processing.

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Compiled the distributed systems architect

Distributed failure review — illustrative proposal

Business invariant to confirm: one intended order should create at most the allowed shipment effect, while preserving a recoverable processing state. Actual cancellation, duplicate and shipping semantics remain unknown.

Failure scenario: shipping call times out after the remote service may have accepted it. Timeout does not prove failure. Retrying the whole request may create another order or shipment and amplify load.

Design proposal: define stable business request identity and idempotent order creation; store durable workflow state; use a verified dispatch/reconciliation pattern suited to the chosen datastore and shipping API. Remote idempotency support must be checked, not assumed.

Effect handling: if shipping lacks reliable duplicate protection or status lookup, record ambiguity and route to controlled reconciliation rather than blindly resending. Exactly-once business behavior requires end-to-end semantics and tests, not a label.

Reliability: bounded retries with appropriate backoff, timeouts, backpressure and stop/escalation states; avoid unbounded queues and retry storms. Distinguish unavailable shipping from lost processing state and define safe user-visible status.

Validation scenarios: remote accepted then timeout; local failure before/after durable state; duplicate request; delayed response; retry exhaustion; crash recovery and reconciliation. Verify ownership and observability of each state.

Open inputs: actual throughput, latency/SLO, durability, datastore transactions, shipping contract, recovery objectives and operators. No multi-region architecture justified from this packet alone.

Execution notes: no code inspected, service called, failure test run or guarantee demonstrated.

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About this skill

Distributed Systems Architect helps teams design or review systems whose correctness spans processes, machines, networks, services or regions. It starts with actual scale, latency, consistency, durability, recovery, security, team and budget requirements before introducing complexity.

The workflow models state ownership, trust and failure boundaries, partial failure, concurrency, retries, replication, queues, caches and overload. Timeouts mean uncertainty; retries can duplicate effects and increase load. Eventual consistency needs explicit convergence and user semantics, and queues do not create capacity.

Use authorized architecture, sanitized telemetry and requirements to build failure models, tradeoffs, rollout and validation plans. No live access, benchmark, test result or infrastructure guarantee is invented. Capacity and recovery targets require representative measurement, and no architecture guarantees unlimited scale or zero incidents.

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Distributed Systems Architect — Design for Scale