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Reliability · Version 1.2.0 · Reviewed 2026-08-02

Historical Risk Pattern Miner

Reduce production risk in historical defect correlation and recurring component risk analysis with evidence, explicit trade-offs, and a verification plan.

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

Finds recurring risk patterns across prior reviews, incidents, reverts, and changes to the same system surface. It grounds the decision in past changes, review findings, incidents, reverts, ownership, affected components, and final resolutions and explicitly prevents matching on author or filename alone and presenting correlation as a causal prediction.

₹199 one-time

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What this skill helps you do

  • Historical defect correlation
  • Recurring component risk analysis
  • Review pattern evidence

How Historical Risk Pattern Miner works

You provide

Impact window, telemetry, and dependency state

It inspects

Saturation and blast radius behind historical defect correlation

It decides

A recurring component risk analysis plan that stabilizes before diagnosing

You verify

Detect, mitigate, and resolve times recorded separately

What it checks first

Historical Risk Pattern Miner finds recurring risk patterns across prior reviews, incidents, reverts, and changes to the same system surface. It grounds the decision in past changes, review findings, incidents, reverts, ownership, affected components, and final resolutions and explicitly prevents matching on author or filename alone and presenting correlation as a causal prediction. Use it when the work involves Historical defect correlation, Recurring component risk analysis, Review pattern evidence.

  1. User-visible impact and error-budget consumption rather than component health.
  2. Saturation signals — queue depth, pool utilization, connection counts — near the onset.
  3. Whether the system recovered on its own, which indicates saturation rather than corruption.
  4. The blast radius and what boundary should have contained it.

Failure modes it recognizes

  • Retry amplification turning a partial failure into a total outage.
  • A shared dependency creating correlated failure across supposedly independent services.
  • Slow resource exhaustion invisible until a hard limit is crossed.
  • A rollback blocked by an incompatible migration.

Answers it will reject

  • Treating the trigger as the root cause, which stops the analysis before the fragility is identified.
  • Adding a runbook step where a boundary would remove the failure mode.
  • Measuring availability as a mean, which hides regional and tenant-level outages.

Decision rules it applies

  • Stabilize user impact before completing diagnosis.
  • Bound every retry with a budget, jitter, and a circuit breaker.
  • Prefer removing a failure mode over detecting it faster.

Evidence it asks for

  • Record time-to-detect, time-to-mitigate, and time-to-resolve separately.
  • Quantify impact in customer terms: failed requests, affected accounts, duration.
  • Verify recovery with the same signal that detected the failure.

The method inside

  1. Establish what is actually true about historical defect correlation from the supplied evidence, and mark what is missing.
  2. Identify the mechanism behind recurring component risk analysis rather than restating the symptom.
  3. Choose the smallest defensible change for review pattern evidence, weighing impact, confidence, effort, and reversibility.
  4. Define measurable ownership and verification

Deliverables

  • Historical defect correlation assessment
  • Recurring component risk analysis decision and action plan
  • Review pattern evidence verification checklist

Evidence requirements

  • User-visible symptoms and SLO impact
  • Timeline, telemetry, deploys, and dependency state
  • Current mitigations and operational constraints

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 historical risk pattern miner to our current historical defect correlation work. We need a concrete decision, bounded changes, and evidence that the result is correct.

Expected output

Start with past changes, review findings, incidents, reverts, ownership, affected components, and final resolutions. The highest-risk failure is matching on author or filename alone and presenting correlation as a causal prediction. Require a shared mechanism or invariant before calling history relevant to the current change. Verify the result by sampling positive and negative historical matches and measuring whether the pattern predicts later failure.

Boundaries and compatibility

Ideal for

  • Historical defect correlation: produce a decision or artifact grounded in supplied evidence.
  • Recurring component risk analysis: produce a decision or artifact grounded in supplied evidence.
  • Review pattern evidence: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Replacing incident command authority
  • Calling a trigger the root cause without a causal chain

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.