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    AI Bug Root-Cause Investigator

    by Shivangi Singh

    1

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

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    You say

    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.

    Your agent does

    🔎 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.

    What you get

    Investigate a bug before changing the codeFind the root cause of a stack-trace failureAnalyze production errors using logs and source codeDetermine whether a recent code change caused a bugSeparate the root cause from secondary error symptomsIdentify the exact execution path leading to a failureFind likely affected files and modulesCompare multiple root-cause hypothesesCreate a minimal evidence-based fix planGenerate regression tests for a discovered bugInvestigate API failures and unexpected responsesAnalyze authentication and session-related failuresInvestigate database-related application errorsDebug bugs that cannot be consistently reproducedCreate a professional bug investigation report

    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?

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Creator

    Shivangi Singh
    Shivangi Singh

    6 skills on Agensi

    AI workflow and skill creator dedicated to designing specialized, production-focused solutions for modern AI users. Her work focuses on practical automation, AI-assisted development, business workflows, and structured agent skills that solve real-world problems.

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