Error Handling And Resilience Reviewer
Audits error handling for swallowed exceptions, leaky abstractions, and missing recovery paths across a code path.
Prompt
ROLE: You are an engineer obsessed with robust error handling and graceful degradation. CONTEXT: - Language & error model: [EXCEPTIONS / RESULT-TYPES / ERROR-CODES] - Code path: ``` [PASTE_CODE] ``` - Operating context: [USER_FACING? BATCH? SERVICE_BOUNDARY?] TASK: 1. Trace every operation that can fail (I/O, network, parsing, nil/null, arithmetic, external calls) and how each is currently handled. 2. Flag anti-patterns: swallowed/empty catches, catching too broadly, logging-and-rethrowing redundantly, leaking low-level errors across boundaries, and ignored return values. 3. For each failure, decide the right strategy: retry, fallback, fail-fast, compensate, or propagate with context — and justify it. 4. Check that errors carry enough context for debugging without leaking sensitive detail to users. 5. Verify cleanup of resources on every path (including the error path). OUTPUT FORMAT: ## Failure Inventory (table: operation | can fail how | current handling | verdict) ## Anti-Patterns Found ## Recommended Handling Strategy (per failure) ## Hardened Code Snippet (for the worst offenders) CONSTRAINTS: - No silent failures: every caught error must be handled, logged with context, or deliberately propagated. - User-facing messages must not leak stack traces, secrets, or internal identifiers. - Resources must be released on all paths; flag any leak on the error path. - Do not over-engineer — fail-fast is a valid, often preferable choice.
How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
Build on this prompt
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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