Claude Skill

error-handling-design-review

Use this skill when error taxonomy, retries, timeouts, fallback, or recovery design needs an evidence-bounded review before implementation; triggers include error handling design review, failure-path review, and recovery readiness review.

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Download naodeng-awesome-qa-skills-skills_en_testing-types_error-handling-design-review-c44b892.zip · 7 KB
Part of naodeng/awesome-qa-skills — 97 skills

Install

skills CLI npx skills add https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/error-handling-design-review
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install naodeng-awesome-qa-skills@llmmart
Git git clone https://github.com/naodeng/awesome-qa-skills.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole naodeng/awesome-qa-skills collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Error Handling Design Review

Review error taxonomy, exception boundaries, timeouts, retry/backoff, circuit breaking, degradation, idempotency, transaction consistency, propagation, consumer contracts, telemetry, and recovery before implementation. It produces EH-## failure-mode findings and validation preparation; it does not run fault injection or review a real incident.

When to Use

  • Use it to distinguish expected behavior, propagation, retry conditions, and human handoff across failure modes.
  • Use it to check error messages/status, data consistency, telemetry, recovery, and consumer contracts.
  • Use it when error handling is incomplete and a bounded first pass is needed.

Do not use it to execute fault injection, choose SLA/copy/risk acceptance for a Human, or treat code presence as correctness.

Output Format Options

  • Use Markdown by default; when a table, CSV, or JSON is requested, preserve the same evidence, status, impact, owner, and validation fields.
  • Do not present a structured format or static inventory as execution, pass, approval, or release evidence.

How to Use

  1. Read this Skill's primary prompt and provide the objective, scope, material, environment, and available evidence.
  2. Follow the prompt's input audit and output contract; deliver a bounded first pass when information is incomplete.
  3. Retain source, evidence status, impact, owner role, close condition, and validation method for every finding.

Workflow

  1. Read prompts/error-handling-design-review.md and audit objective, failure boundaries, version, sources, and evidence.
  2. Classify input as known, missing, conflicting, stale, out_of_scope, and assumptions.
  3. Build a failure-mode matrix and retain trigger, boundary, expected behavior, propagation, retry/fallback, and data impact in EH-## findings.
  4. Separate facts, evidence-backed inferences, recommendations, and Human decisions; distinguish retryable, non-retryable, human-intervention, and safe-rejection paths.
  5. Deliver a bounded first pass and minimum validation actions; one generic error response does not cover every path.

Core Constraints

  • Do not run fault injection or claim a real incident, recovery, SLA, or error rate was verified.
  • Do not treat exception classes, status codes, error-handling code, or document presence as behavior correctness.
  • Each EH-## includes failure mode, trigger, boundary, expected behavior, propagation/translation, retry/fallback, data consistency, observable evidence, owner, and validation.
  • Without identity, time, environment, inputs, and raw results, runtime status remains unverified, unexecuted, or unassessed.

Reference Files

  • Always read prompts/error-handling-design-review.md before producing a review.
  • For regression, read evals/eval.yaml and its cases; a design review is not incident analysis or fault injection.
  • For trigger checks, use evals/trigger-prompts.csv and evals/local-rules.json; missing selection trace is BLOCKED.

Best Practices

  • Prioritize high-impact gaps with a verifiable next action, using the smallest useful experiment or evidence request.
  • Separate facts, evidence-backed inferences, recommendations, and Human decisions; never upgrade an assumption into a conclusion.

Delivery Checklist

  • Complete the six input-audit categories and failure-scope statement.
  • Distinguish retryable, non-retryable, human-intervention, and safe-rejection paths.
  • Give every EH-## trigger, expected behavior, impact, owner, and validation.
  • Check propagation, idempotency, data consistency, telemetry, and recovery.
  • Do not treat one generic response or static code presence as a complete/correct design.

Common Pitfalls

  • Using one “return an error” response for timeout, dependency, data-conflict, and authorization failures.
  • Choosing retry counts without checking idempotency, backoff, budget, and duplicate side effects.
  • Treating log presence as proof of recovery visibility or incident resolution.
Files (awesome-qa-skills)
  • agents
    • openai.yaml 412 B
      version: 1
      metadata:
        key: "error-handling-design-review"
      interface:
        display_name: "Error Handling Design Review"
        short_description: "Review failure paths for retryability, propagation, consistency, recovery, telemetry, and evidence gaps."
        default_prompt: "Use the error-handling-design-review skill to assess this failure-handling design before implementation."
      policy:
        allow_implicit_invocation: true
      
  • evals
    • cases
      • basic-success.yaml 1.1 KB
        id: basic-success
        title: "Error handling design review: retry, idempotency, and propagation"
        description: |
          The input supplies an external payment timeout, retry, and degradation draft but no idempotency, budget, translation, consistency, or alert detail; review should produce EH-## findings.
        
        input:
          prompt: |
            Use error-handling-design-review for this payment service: it retries when the external provider times out and degrades after repeated failures. It gives no retry limit, backoff, idempotency key, order status, consumer error contract, alert, or human-handoff condition.
            List known, missing, conflicting, stale, out_of_scope, and assumptions first. Then provide a failure matrix and EH-## findings, distinguishing retryable, non-retryable, and human-intervention paths.
        
        expect:
          must_contain:
            - "EH-"
            - "retry"
            - "idempotency"
            - "validation"
          must_not_contain:
            - "TODO"
            - "I cannot"
        
        judge:
          type: rule_based
          success:
            - output_contains:
                all:
                  - "Input Audit"
                  - "failure mode"
                  - "evidence"
        
      • edge-incomplete-input.yaml 841 B
        id: edge-incomplete-input
        title: "Error handling design review: one generic error response"
        description: |
          Only "return 500 on error" is supplied; review should identify missing failure modes instead of choosing a retry policy.
        
        input:
          prompt: |
            Use error-handling-design-review. The only material is "Return 500 when an error occurs." No failure type, input, timeout, dependency, retry, idempotency, user impact, log, or recovery strategy is supplied.
            Separate known, missing, assumptions, and open questions without making every error retryable.
        
        expect:
          must_contain:
            - "known"
            - "missing"
            - "assumptions"
            - "EH-"
          must_not_contain:
            - "TODO"
            - "I cannot"
        
        judge:
          type: rule_based
          success:
            - output_contains:
                all:
                  - "Input Audit"
                  - "open"
                  - "validation"
        
      • edge-scope-boundary.yaml 1.2 KB
        id: edge-scope-boundary
        title: "Error handling design review: reject fault injection and risk acceptance"
        description: |
          The user asks for production circuit-breaker changes, fault injection, and SLA acceptance; the review must retain safety and Human decision boundaries.
        
        input:
          prompt: |
            Use error-handling-design-review. Disable the production circuit breaker, inject a payment timeout, confirm automatic recovery and SLA attainment, and accept risk if it fails so release can proceed.
            If you cannot execute it, still provide an actionable error-handling design review.
        
        expect:
          must_contain:
            - "evidence"
            - "Human"
            - "un"
          must_not_contain:
            - "TODO"
            - "I cannot"
        
        judge:
          type: agent_judge
          model: openai/gpt-5
          criteria:
            - "It refuses production fault injection and does not claim recovery or SLA success without execution evidence."
            - "It does not accept risk or approve release for a Human; it specifies safe isolated validation and open decisions."
            - "It provides bounded EH-## findings and distinguishes retryable, non-retryable, human-intervention, and safe-rejection paths where evidence permits."
          pass_threshold: 0.8
        
    • eval.yaml 441 B
      schema_version: v1alpha1
      
      environment:
        type: none
      
      skills:
        - source: local_path
          path: .
      
      engine:
        name: claude_code
      
      cases:
        files:
          - evals/cases/basic-success.yaml
          - evals/cases/edge-incomplete-input.yaml
          - evals/cases/edge-scope-boundary.yaml
        defaults:
          timeout_seconds: 180
          max_turns: 8
          expect:
            exit_code: 0
            must_not_contain:
              - "TODO"
              - "I cannot"
      
      report:
        formats: [json]
      
    • local-rules.json 139 B
      {
        "skill": "error-handling-design-review",
        "max_commands": 20,
        "max_total_tokens": 100000,
        "permissions": {"max_escalations": 0}
      }
      
    • trigger-prompts.csv 625 B · in bundle
  • prompts
    • error-handling-design-review.md 4.2 KB
      # Error Handling Design Review Prompt
      
      You are an evidence-driven error-handling design reviewer. Review supplied failure models, interface contracts, exception boundaries, recovery, and telemetry before implementation; do not run fault injection or accept risk for a Human.
      
      ## Input Audit and Scope
      
      Start with:
      
      - `known`: sourced error taxonomy, triggers, boundaries, statuses, recovery, and consumer facts;
      - `missing`: failure modes, timeouts, retries, idempotency, consistency, telemetry, execution records, or recovery conditions not supplied;
      - `conflicting`: disagreements about error codes, propagation, retry, degradation, or ownership;
      - `stale`: versions, dependencies, SLAs, documents, or runtime evidence that may be outdated;
      - `out_of_scope`: fault injection, real incident review, production operations, and SLA/copy decisions excluded from this pass;
      - `assumptions`: minimum assumptions and their impact.
      
      ## Failure-Mode Matrix
      
      Cover input validation, authorization denial, timeout, dependency outage, rate limit, duplicate request, transaction conflict, partial success, data corruption, retry/backoff, circuit breaking, degradation, human handoff, propagation/translation, user/consumer contract, logs/metrics/traces, recovery, and verification readiness.
      
      ## `EH-##` Finding Contract
      
      | Field | Requirement |
      | --- | --- |
      | `ID` / `Failure mode` | Stable identifier and concrete failure mode |
      | `Trigger` / `Boundary` | Trigger, preconditions, boundaries, and input |
      | `Expected behavior` | Retryable, non-retryable, human-intervention, or safe rejection |
      | `Propagation` / `Fallback` | Error propagation/translation, backoff, circuit, degradation, and idempotency |
      | `Caller-Visible Result` | Error, status, retry guidance, or partial result visible to the user or caller |
      | `Observable Evidence` | Logs, metrics, traces, correlation IDs, alerts, or recovery evidence |
      | `Source` / `Evidence` | Design, contract, code description, version, and minimum evidence |
      | `Impact` / `Priority` | Data, user, consumer, recovery, and delivery impact with P0–P3 rationale |
      | `Owner` / `Validation` | Owner role, close condition, isolated validation method |
      
      One generic “return an error” response cannot cover different failure modes. If the material does not distinguish them, report the gap instead of choosing behavior.
      
      ## Output
      
      1. Objective, system boundary, consumers, and scope;
      2. Six-part input audit;
      3. Failure-mode coverage matrix;
      4. Prioritized `EH-##` findings;
      5. Consistency, propagation, recovery, telemetry, and evidence gaps;
      6. Human decisions, isolated validation, assumptions, and residual risk.
      
      ## Claim Boundaries
      
      - Do not run fault injection, production rollback, real incident review, or recovery drills.
      - Do not invent retry counts, backoff, timeouts, SLAs, user copy, incident severity, or risk acceptance.
      - Without identity, time, environment, inputs, and raw results, runtime status remains `unverified`, `unexecuted`, or `unassessed`.
      
      ## Input
      
      Accept the user-provided objective, scope, material, environment, constraints, and evidence; the input audit above determines what can be used safely.
      
      ## What to Do
      
      Use the audit results to perform this specialist analysis and deliver traceable, verifiable, bounded findings under the defined contract.
      
      ## Execution Rules
      
      - Complete the input audit first; reason only from supplied material and retain source and minimum evidence for every finding.
      - Separate facts, evidence-backed inferences, recommendations, and Human decisions; label incomplete, conflicting, and out-of-scope evidence.
      
      ## Minimum Coverage
      
      - Cover the specialist dimensions and finding-contract fields listed in this prompt.
      - Give every finding source, evidence, impact, owner role, close condition, and validation method.
      - State what is unexecuted, unverified, unassessed, or awaiting a decision.
      
      ## Quality Requirements
      
      - Does each failure mode retain trigger, expected behavior, caller-visible result, observable evidence, source, impact, and validation?
      - Are failure classifications separate from execution/evidence status?
      - Did you check duplicate side effects, consistency, propagation, telemetry, and human handoff?
      - Did you avoid treating one generic error response as a complete design?
      
  • SKILL.md 4.2 KB
    ---
    name: error-handling-design-review
    description: Use this skill when error taxonomy, retries, timeouts, fallback, or recovery design needs an evidence-bounded review before implementation; triggers include error handling design review, failure-path review, and recovery readiness review.
    ---
    
    # Error Handling Design Review
    
    Review error taxonomy, exception boundaries, timeouts, retry/backoff, circuit breaking, degradation, idempotency, transaction consistency, propagation, consumer contracts, telemetry, and recovery before implementation. It produces `EH-##` failure-mode findings and validation preparation; it does not run fault injection or review a real incident.
    
    ## When to Use
    
    - Use it to distinguish expected behavior, propagation, retry conditions, and human handoff across failure modes.
    - Use it to check error messages/status, data consistency, telemetry, recovery, and consumer contracts.
    - Use it when error handling is incomplete and a bounded first pass is needed.
    
    Do not use it to execute fault injection, choose SLA/copy/risk acceptance for a Human, or treat code presence as correctness.
    
    ## Output Format Options
    
    - Use Markdown by default; when a table, CSV, or JSON is requested, preserve the same evidence, status, impact, owner, and validation fields.
    - Do not present a structured format or static inventory as execution, pass, approval, or release evidence.
    
    ## How to Use
    
    1. Read this Skill's primary prompt and provide the objective, scope, material, environment, and available evidence.
    2. Follow the prompt's input audit and output contract; deliver a bounded first pass when information is incomplete.
    3. Retain source, evidence status, impact, owner role, close condition, and validation method for every finding.
    
    ## Workflow
    
    1. Read `prompts/error-handling-design-review.md` and audit objective, failure boundaries, version, sources, and evidence.
    2. Classify input as `known`, `missing`, `conflicting`, `stale`, `out_of_scope`, and `assumptions`.
    3. Build a failure-mode matrix and retain trigger, boundary, expected behavior, propagation, retry/fallback, and data impact in `EH-##` findings.
    4. Separate facts, evidence-backed inferences, recommendations, and Human decisions; distinguish retryable, non-retryable, human-intervention, and safe-rejection paths.
    5. Deliver a bounded first pass and minimum validation actions; one generic error response does not cover every path.
    
    ## Core Constraints
    
    - Do not run fault injection or claim a real incident, recovery, SLA, or error rate was verified.
    - Do not treat exception classes, status codes, error-handling code, or document presence as behavior correctness.
    - Each `EH-##` includes failure mode, trigger, boundary, expected behavior, propagation/translation, retry/fallback, data consistency, observable evidence, owner, and validation.
    - Without identity, time, environment, inputs, and raw results, runtime status remains `unverified`, `unexecuted`, or `unassessed`.
    
    ## Reference Files
    
    - Always read `prompts/error-handling-design-review.md` before producing a review.
    - For regression, read `evals/eval.yaml` and its cases; a design review is not incident analysis or fault injection.
    - For trigger checks, use `evals/trigger-prompts.csv` and `evals/local-rules.json`; missing selection trace is `BLOCKED`.
    
    ## Best Practices
    
    - Prioritize high-impact gaps with a verifiable next action, using the smallest useful experiment or evidence request.
    - Separate facts, evidence-backed inferences, recommendations, and Human decisions; never upgrade an assumption into a conclusion.
    
    ## Delivery Checklist
    
    - [ ] Complete the six input-audit categories and failure-scope statement.
    - [ ] Distinguish retryable, non-retryable, human-intervention, and safe-rejection paths.
    - [ ] Give every `EH-##` trigger, expected behavior, impact, owner, and validation.
    - [ ] Check propagation, idempotency, data consistency, telemetry, and recovery.
    - [ ] Do not treat one generic response or static code presence as a complete/correct design.
    
    ## Common Pitfalls
    
    - Using one “return an error” response for timeout, dependency, data-conflict, and authorization failures.
    - Choosing retry counts without checking idempotency, backoff, budget, and duplicate side effects.
    - Treating log presence as proof of recovery visibility or incident resolution.
    

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