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    AI Bug Root-Cause Investigator

    1

    Trace bugs from symptoms to evidence-backed root causes, affected code paths, fixes, and regression tests.

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    AI Bug Root-Cause Investigator

    AI Bug Root-Cause Investigator

    Example session with this skill installed

    Investigate this production bug before suggesting a fix.

    Bug: Users occasionally receive a 500 error when submitting the checkout form.

    Observed error

    TypeError: Cannot read properties of undefined (reading 'id')

    Stack trace

    checkoutService.ts:184

    orderController.ts:92

    checkout.tsx:317

    Recent change: We changed the payment response handling yesterday.

    Expected behavior: A successful payment should create an order and return the order ID.

    Actual behavior: Some payments succeed but the application returns HTTP 500.

    Analyze the available repository evidence and determine:

    The likely failure path
    Root-cause candidates
    Which evidence supports each candidate
    The most likely root cause
    Files/functions that should be inspected
    The smallest safe fix
    Regression risks
    Tests that should be added
    How the fix should be verified

    Do not modify any files and do not claim that the bug is fixed.

    • Read your context and instructions
    • Compiled the ai bug root-cause

    🔎 BUG ROOT-CAUSE INVESTIGATION

    Incident: Checkout intermittently returns HTTP 500
    Investigation Mode: Read-only
    Confidence: MEDIUM

    1. SYMPTOM

    Some checkout requests return HTTP 500 after payment processing succeeds.

    The observed exception is

    Cannot read properties of undefined (reading 'id')

    The stack trace points to

    checkoutService.ts:184

    1. FAILURE PATH

    Based on the supplied evidence, the likely execution path is:

    Checkout UI

    ↓

    Order Controller

    ↓

    Checkout Service

    ↓

    Payment Response

    ↓

    Order Creation / Order ID Access

    ↓

    Undefined value

    ↓

    HTTP 500

    1. ROOT-CAUSE CANDIDATES
      Candidate A — Payment response shape changed

    Confidence: HIGH

    The payment response handling was changed immediately before the incident.

    If the new response structure no longer contains the property expected by checkoutService.ts, an undefined value could reach the order-ID access.

    Candidate B — Different payment-success response variants

    Confidence: MEDIUM

    The intermittent nature suggests that only some payment responses may follow the expected structure.

    Candidate C — Order creation failure

    Confidence: LOW

    This is possible, but the available evidence does not yet establish that order creation itself is failing.

    1. MOST LIKELY ROOT CAUSE

    The strongest current hypothesis is a mismatch between the payment response structure introduced by the recent change and the structure expected by the checkout service.

    This is not yet a confirmed root cause because the relevant implementation and response payloads must be inspected.

    1. FILES TO INSPECT

    HIGH PRIORITY

    checkoutService.ts

    Inspect the value accessed at line 184 and trace where it originates.

    paymentService.ts

    Inspect the new response transformation.

    orderController.ts

    Confirm what data is passed into the checkout service.

    MEDIUM PRIORITY

    Payment integration tests
    Checkout integration tests
    Order creation tests

    1. MINIMAL FIX STRATEGY

    First verify the actual payment response shape.

    Then make the smallest compatibility correction at the response boundary rather than adding defensive checks throughout unrelated modules.

    If multiple valid response shapes exist, normalize them at the payment-service boundary.

    1. REGRESSION RISKS
      Successful payments could fail during order creation.
      Some payment providers may return different response structures.
      A defensive fallback could hide a genuine payment failure.
      Existing tests may cover only the previous response format.
    2. REGRESSION TESTS

    Add tests for

    Test 1: Standard successful payment response
    Test 2: Alternate successful response structure
    Test 3: Missing order/payment identifier
    Test 4: Payment success with order-creation failure
    Test 5: Invalid payment response

    1. VERIFICATION PLAN
      Capture the actual payment response for a failing request.
      Compare it with the structure expected by the checkout service.
      Confirm the value accessed at line 184.
      Apply the smallest boundary-level correction.
      Run the new regression tests.
      Verify successful payment, order creation, and error handling independently.
      FINAL ASSESSMENT

    Likely Root Cause: Payment response contract mismatch

    Confidence: MEDIUM

    Fix Status: NOT VERIFIED

    Files Modified: None

    Next Action: Inspect the payment response transformation and the value consumed at checkoutService.ts:184.

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    About this skill

    AI Bug Root-Cause Investigator™ is an evidence-first debugging skill for developers and AI coding agents who need to understand why a bug happened before changing the code.

    Instead of immediately generating a speculative patch, the skill investigates the failure path and builds a structured root-cause analysis from the available evidence.

    It can analyze:

    Bug reports Error messages Stack traces Logs Relevant source files Recent code changes API responses Configuration Database interactions Authentication/session flows Test failures Reproduction steps

    The skill produces a professional investigation report covering:

    SYMPTOM → EVIDENCE → FAILURE PATH → ROOT-CAUSE CANDIDATES → ROOT CAUSE → AFFECTED FILES → MINIMAL FIX → REGRESSION RISK → TEST PLAN

    Core Capabilities

    1. Failure Reconstruction Reconstruct the likely execution path leading to the observed failure.

    2. Evidence Correlation Connect stack traces, logs, source code, configuration, and reported behavior.

    3. Root-Cause Analysis Separate the actual root cause from symptoms, secondary failures, and misleading errors.

    4. Hypothesis Testing Generate multiple plausible causes when evidence is incomplete and rank them by confidence.

    5. Change Correlation Identify whether recent code changes plausibly introduced the failure.

    6. Impact Analysis Identify affected modules, files, APIs, services, data flows, and user-facing behavior.

    7. Minimal-Fix Planning Recommend the smallest evidence-supported correction instead of unnecessary rewrites.

    8. Regression Prevention Generate targeted regression tests based on the discovered failure mechanism.

    9. Verification Plan Specify what evidence should confirm that the fix actually resolves the original problem.

    Investigation Principles

    The skill follows an evidence-first approach.

    It must not:

    Invent stack traces Invent logs Claim to have reproduced a bug when it has not Assume a root cause without evidence Recommend unrelated rewrites Treat a symptom as the root cause Claim a fix was verified without verification evidence

    When evidence is insufficient, the skill explicitly states what is known, what is uncertain, and what additional evidence is required.

    Typical Questions It Can Answer Why is this error happening? Where does the failure actually originate? Which file/function is most likely responsible? Is this the root cause or only a symptom? Did a recent change likely introduce the bug? What execution path leads to the failure? Which components are affected? What is the smallest safe fix? What regression test should be added? How do I verify the fix?

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Testing & Debugging.

    Root-cause confidence depends on the quality and completeness of the supplied evidence. Bugs that require unavailable production state, external services, timing conditions, hardware-specific behavior, private infrastructure, or missing logs may not be conclusively diagnosed. The skill does not claim to reproduce a failure unless reproduction evidence is actually available. Root-cause candidates may remain hypotheses when evidence is insufficient. Recommended fixes and regression tests require developer validation before production deployment.

    How to use

    1. 1

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    3. 3

      Ask for what you need

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    Permissions

    Read Files

    File Scopes

    src/**
    tests/**
    *.log
    *.json
    *.yaml
    *.yml
    *.md

    Read access is used to inspect source code, stack traces, logs, tests, configuration, dependency manifests, and other evidence supplied for the investigation. The default workflow is read-only and does not require shell execution, network access, browser access, environment variables, or permission to modify project files.

    Listed3 months ago
    Updated17 days ago

    What's inside

    Frequently Asked Questions

    No. Its primary purpose is evidence-based root-cause investigation. It can recommend a minimal fix and verification plan without pretending that the bug has been fixed.

    The skill uses an investigation-first workflow. It traces evidence, evaluates competing root-cause hypotheses, identifies affected code paths, and creates regression tests before recommending a fix.

    Yes. Stack traces can be correlated with available source code, logs, dependencies, and execution paths.

    Yes, when relevant logs, errors, source code, configuration, and other evidence are available. It cannot inspect unavailable production systems or claim reproduction without evidence.

    It can identify likely responsible files and functions when supported by evidence. It clearly labels uncertain conclusions rather than presenting guesses as facts.

    Yes. It can compare possible execution conditions and generate ranked hypotheses, evidence requirements, and reproduction strategies. Intermittent bugs may remain unresolved when critical runtime evidence is unavailable.

    No. The default workflow is read-only.

    Yes. It generates targeted regression-test scenarios based on the suspected failure mechanism and identified risk areas.

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