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Debugging · Version 1.1.0 · Reviewed 2026-08-02

Terraform Production Debug Specialist

Diagnose terraform production incident triage and terraform root-cause isolation with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Diagnoses production failures from runtime evidence instead of symptom matching in Terraform using configuration, state, provider locks, modules, and saved plans and plan JSON, replacement actions, state drift, and provider diagnostics, with explicit attention to address or immutable-attribute change replacing stateful infrastructure unexpectedly.

₹199 one-time

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What this skill helps you do

  • Terraform production incident triage
  • Terraform root-cause isolation
  • Terraform fix verification

How Terraform Production Debug Specialist works

You provide

Symptoms, timestamps, recent changes, and logs

It inspects

Correlated subset and first failing component for terraform production incident triage

It decides

A ranked cause for terraform root-cause isolation that explains recovery too

You verify

The cheapest discriminating test, run before the fix

What it checks first

Terraform Production Debug Specialist diagnoses production failures from runtime evidence instead of symptom matching in Terraform using configuration, state, provider locks, modules, and saved plans and plan JSON, replacement actions, state drift, and provider diagnostics, with explicit attention to address or immutable-attribute change replacing stateful infrastructure unexpectedly. Use it when the work involves Terraform production incident triage, Terraform root-cause isolation, Terraform fix verification.

  1. Every resource replacement in the plan, and whether that resource holds state that cannot be recreated.
  2. Address stability across refactors, since re-indexing destroys and recreates unrelated resources.
  3. Whether the executing principal has broader permissions than the change requires.
  4. Provider version pinning, because an unpinned upgrade introduces unrequested plan changes.
  5. Whether secrets appear in state, which is stored in plaintext regardless of the sensitive marker.

Failure modes it recognizes

  • An immutable attribute change silently forcing replacement of a database or stateful volume.
  • Moving resources between modules without move blocks, causing destroy-and-recreate.
  • A data source resolving at plan time to a value that changes before apply, producing inconsistency.
  • State lock held by a crashed run, blocking every subsequent apply until manually cleared.
  • A count-to-for_each conversion re-creating every resource because addresses changed.
  • Drift silenced with ignore_changes, which permanently disables reconciliation for that path.

Answers it will reject

  • Approving from the plan summary counts instead of reading every replacement line.
  • Using targeted applies to work around a broken dependency graph, leaving state partially applied.
  • Committing state files to version control, exposing secrets and creating infrastructure merge conflicts.
  • Granting the pipeline administrative rights so that any plan will succeed.

Decision rules it applies

  • Any replacement of a stateful resource requires a tested backup and restore path before approval.
  • Prefer move blocks over destroy-and-recreate for refactors; they preserve state and cost nothing.
  • Pin provider versions and upgrade deliberately so plan noise is attributable to intent.
  • If the plan cannot be explained line by line, it has not been reviewed.

Evidence it asks for

  • Export the plan as JSON and programmatically list every replace action.
  • Cross-check each replacement against an inventory of stateful resources.
  • Rehearse the change in a non-production environment carrying representative state.

The method inside

  1. Reconstruct the symptom timeline and define what healthy behavior would look like for terraform production incident triage.
  2. Rank hypotheses for terraform root-cause isolation by evidence, blast radius, and ability to explain every observed symptom.
  3. Run the cheapest discriminating check for terraform fix verification; update confidence only when evidence changes.
  4. Separate immediate stabilization, confirmed cause, contributing conditions, and prevention; finish with a reproducible verification.

Deliverables

  • Terraform production incident triage assessment
  • Terraform root-cause isolation decision and action plan
  • Terraform fix verification verification checklist

Evidence requirements

  • Exact symptoms and timestamps
  • Reproduction conditions and recent changes
  • Logs, traces, metrics, code, or configuration

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Apply the production debug specialist to our Terraform system before the next production change. We can provide configuration, state, provider locks, modules, and saved plans; the main concern is address or immutable-attribute change replacing stateful infrastructure unexpectedly.

Expected output

Start with plan JSON, replacement actions, state drift, and provider diagnostics and split the affected population before changing configuration. The leading hypothesis is address or immutable-attribute change replacing stateful infrastructure unexpectedly. Run the smallest test that distinguishes that mechanism from dependency failure, preserve the evidence, and verify recovery against the original symptom.

Boundaries and compatibility

Ideal for

  • Terraform production incident triage: produce a decision or artifact grounded in supplied evidence.
  • Terraform root-cause isolation: produce a decision or artifact grounded in supplied evidence.
  • Terraform fix verification: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Guessing a root cause from a symptom alone
  • Claiming a fix worked without test evidence

Agent compatibility

  • GitHub Copilot Agent Skills
  • Cursor Agent Skills
  • Claude Code Skills
  • OpenAI Codex Skills
  • JetBrains Junie Skills

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.