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Data · Version 1.2.0 · Reviewed 2026-08-02

Slowly Changing Dimension Advisor

Make data systems more correct and operable for strategy selection and effective dating with evidence, explicit trade-offs, and a verification plan.

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

Chooses and implements SCD strategies so historical analysis stays correct as attributes change.

₹149 one-time

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What this skill helps you do

  • Strategy selection
  • Effective dating
  • Historical restatement

How Slowly Changing Dimension Advisor works

You provide

Current signals, alert rules, and recent incidents

It inspects

Symptom-versus-cause coverage for strategy selection

It decides

A effective dating design with bounded label cardinality

You verify

Each page has a documented action and a real trigger

What it checks first

Slowly Changing Dimension Advisor chooses and implements SCD strategies so historical analysis stays correct as attributes change. Use it when the work involves Strategy selection, Effective dating, Historical restatement.

  1. Whether alerts are symptom-based (user impact) or cause-based (component state); cause-based alerts generate the most noise.
  2. Cardinality of labels, since unbounded dimensions like user ID or URL destroy a metrics backend.
  3. Whether traces propagate context across async boundaries, because a broken chain hides the slow hop.
  4. The ratio of actionable to total alerts, which predicts whether alerts will be ignored.
  5. Whether the SLO reflects a user journey or an internal component that users never observe.

Failure modes it recognizes

  • Alert fatigue where a noisy alert trains responders to ignore the channel that later carries a real outage.
  • Metric cardinality explosion from a label containing a request ID, causing ingestion cost and query failure.
  • Sampled traces dropping exactly the slow requests that needed investigation.
  • Logs without correlation IDs, making a multi-service request impossible to reconstruct.
  • A dashboard averaging latency, which hides the tail where user pain actually lives.
  • An alert on a threshold that only fires after the error budget is already exhausted.

Answers it will reject

  • Alerting on CPU utilization, which is a resource state rather than user impact and fires without consequence.
  • Adding a dashboard instead of an alert, which requires a human to be watching to be useful.
  • Logging at debug level in production to "have the data", which costs more than the incidents it solves.
  • Reporting availability as a mean, which allows a total regional outage to disappear into the average.

Decision rules it applies

  • Alert on symptoms that users feel; use cause metrics for diagnosis, not for paging.
  • Every page must have a documented action; if the action is "look at it", it is not a page.
  • Measure latency with percentiles and always include p99, since averages hide the tail.
  • Keep label cardinality bounded and known; treat an unbounded dimension as a defect.

Evidence it asks for

  • Define an SLI as a ratio of good events to valid events, with both terms explicitly specified.
  • Use tail-based sampling so slow and failed traces are retained preferentially.
  • Propagate a correlation ID from edge to database and include it in every log line.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to strategy selection.
  2. Trace concrete failure or abuse paths for effective dating; do not report checklist items without a mechanism.
  3. Prioritize historical restatement findings by impact, likelihood, confidence, and cost of correction.
  4. Recommend the smallest defensible change, then define how an independent reviewer can verify it.

Deliverables

  • Strategy selection assessment
  • Effective dating decision and action plan
  • Historical restatement verification checklist

Evidence requirements

  • Schema, access patterns, query plans, or event contracts
  • Volume, cardinality, retention, and freshness
  • Consistency, latency, and migration 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

When a customer changes segment, all their historical orders move to the new segment and last year totals change.

Expected output

That is type-1 overwrite behavior applied to an attribute that needs history. Use effective-dated dimension rows and join facts on the key valid at transaction time, so past periods stay fixed while current reporting reflects the new segment...

Boundaries and compatibility

Ideal for

  • Strategy selection: produce a decision or artifact grounded in supplied evidence.
  • Effective dating: produce a decision or artifact grounded in supplied evidence.
  • Historical restatement: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Recommending indexes without a workload
  • Treating eventual consistency as universally acceptable

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

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.