Works with the AI tools you already use
AI Bug Root-Cause Investigator
Trace bugs from symptoms to evidence-backed root causes, affected code paths, fixes, and regression tests.
Secure checkout via Stripe
See it in action
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
- 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
- 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
- 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.
- 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.
- 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
- 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.
- 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.
- 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
- 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
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
-
Failure Reconstruction Reconstruct the likely execution path leading to the observed failure.
-
Evidence Correlation Connect stack traces, logs, source code, configuration, and reported behavior.
-
Root-Cause Analysis Separate the actual root cause from symptoms, secondary failures, and misleading errors.
-
Hypothesis Testing Generate multiple plausible causes when evidence is incomplete and rank them by confidence.
-
Change Correlation Identify whether recent code changes plausibly introduced the failure.
-
Impact Analysis Identify affected modules, files, APIs, services, data flows, and user-facing behavior.
-
Minimal-Fix Planning Recommend the smallest evidence-supported correction instead of unnecessary rewrites.
-
Regression Prevention Generate targeted regression tests based on the discovered failure mechanism.
-
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.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill passes our trust checks below.
Security scanned
Passed our 8-point scan before listing
Fresh listing
Recently published to Agensi
30-day refund
Not a fit? Get your money back
Trust & safety
Security scanned
Verified clean today
- 30-day refund guarantee
- One-time purchase, yours forever
- Secure checkout via Stripe
Creator
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.
Frequently Asked Questions
Popular in Testing & QA

AI Software Engineering / Coding Production Agent
Transforms software requirements into planned, implemented, tested, reviewed, and production-ready engineering changes.
Prompt Dataset Builder
Build and curate high-quality prompt datasets for fine-tuning and eval — deduped and labeled.

Systematic Bug Debugger
Stop guessing and start solving with hypothesis-driven debugging for complex, intermittent, and production-only bugs.
test-failure-analyzer
Automatically triage test failures, detect cascading root causes, and get prioritized fix suggestions across 10+ languages.