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AI Production Failure Reconstructor
Reconstruct production failures from logs, deploy changes, errors, and timelines to identify what happened, why, and how to reproduce it
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See it in action
You say
Our checkout API started returning intermittent 500 errors after yesterday's deployment.
Here is what I have:
Error logs from the affected requests Deployment timestamp Git diff from the last release One successful request One failed request API response traces
Reconstruct what happened, identify the most likely trigger, and tell me how to reproduce the failure.
Your agent does
PRODUCTION FAILURE RECONSTRUCTION
INCIDENT: Intermittent checkout API 500 errors after deployment.
FAILURE TIMELINE:
14:02 — New deployment completed 14:07 — First failed checkout request 14:09 — Successful checkout from another session 14:13 — Repeated failures begin under the same request condition
STRONGEST EVIDENCE: The failing requests contain a missing payment-session field that was not present in successful requests.
LIKELY TRIGGER: A new checkout path introduced in the latest deployment appears to access the payment-session value before it is initialized.
CONFIDENCE: Medium — strong correlation exists, but the causal path requires reproduction.
REPRODUCTION CONDITIONS: • New checkout session • Payment method selected • Specific session state • New application version • Payment-session initialization delayed
NEXT TEST: Replay the failing request sequence against the new build while logging payment-session initialization.
EXPECTED RESULT: If the hypothesis is correct, the 500 error should occur when the payment-session value is accessed before initialization.
REGRESSION TEST: Add a checkout test covering the delayed-session initialization state.`
What you get
About this skill
AI Production Failure Reconstructor is an evidence-first incident analysis skill for developers and engineering teams dealing with confusing production failures.
Instead of immediately guessing a fix, it reconstructs the failure as a timeline and connects symptoms to deployments, code changes, requests, errors, environment conditions, and user actions.
CORE WORKFLOW:
Failure Timeline • Build a chronological sequence of events • Align logs, errors, deployments, requests, and user actions • Identify the first meaningful deviation Trigger Detection • Find conditions that may have triggered the failure • Compare successful vs failed scenarios • Separate correlation from stronger evidence Change Correlation • Compare recent deploys and code changes • Identify suspicious configuration or dependency changes • Map changes to affected behavior Reproduction Model • Convert production evidence into reproducible conditions • Define required inputs, environment, timing, state, and sequence • Handle intermittent and race-condition scenarios Evidence Map • Separate confirmed facts from hypotheses • Rank evidence by confidence • Identify missing evidence needed to confirm the theory Verification Plan • Define the smallest useful test • Create regression scenarios • Specify what evidence should appear if the hypothesis is correct
OUTPUT:
✓ Failure timeline ✓ Trigger hypothesis ✓ Evidence map ✓ Suspected change correlation ✓ Reproduction conditions ✓ Confidence level ✓ Missing-evidence checklist ✓ Verification test plan ✓ Developer-ready incident handoff
PERFECT FOR:
• Production incidents • Intermittent failures • “Works locally” bugs • Post-deployment failures • Authentication failures • API errors • Database inconsistencies • Race conditions • Configuration-related failures • Performance regressions
IMPORTANT:
This skill does not pretend that a root cause is proven when evidence is incomplete. It clearly separates confirmed evidence, strong hypotheses, weak hypotheses, and unknowns.`
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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