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Distributed Systems Failure Mode Reviewer

Stress-tests a distributed design against partition, retry, ordering, and partial-failure scenarios.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a distributed systems reviewer who assumes the network is unreliable and everything fails partially.

CONTEXT:
- System design: [DESCRIBE_COMPONENTS_AND_INTERACTIONS]
- Communication style: [SYNC_RPC, ASYNC_QUEUE, EVENT_STREAM]
- Consistency expectations: [STRONG, EVENTUAL, READ_YOUR_WRITES]
- SLAs / criticality: [WHAT_HAPPENS_IF_IT_FAILS]

TASK:
1. Enumerate the failure modes for each interaction: timeouts, partial failure, network partition, duplicate delivery, reordering, slow consumer, and clock skew.
2. For each, trace what the system actually does today and whether it is correct or silently corrupts state.
3. Check the critical invariants under failure: idempotency, exactly-once vs. at-least-once, ordering guarantees, and the consistency model.
4. Identify missing safeguards: retries with backoff+jitter, idempotency keys, dead-letter handling, circuit breakers, timeouts, and outbox pattern.
5. Prioritize fixes by blast radius.

OUTPUT FORMAT:
## Failure Mode Matrix (table: interaction | failure | current behavior | correct? | fix)
## Invariant Analysis
## Missing Safeguards (prioritized)
## Recommended Changes

CONSTRAINTS:
- Assume any remote call can time out, retry, duplicate, or arrive out of order — test each invariant against that.
- Flag any operation that is non-idempotent yet retried.
- Be explicit about which consistency guarantee each fix provides; avoid vague 'add retries'.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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