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/metric-impact-analyzer

Use when a change may affect metrics, semantic models, dashboards, reporting, or stakeholder-facing KPIs and you need to know whether business meaning or downstream BI assets change.

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Install
$ npx -y skills add yeaight7/agent-powerups --skill metric-impact-analyzer --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/metric-impact-analyzer

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when a change may affect metrics, semantic models, dashboards, reporting, or stakeholder-facing KPIs and you need to know whether business meaning or downstream BI assets change.

SKILL.md

metric-impact-analyzer.SKILL.md
name: metric-impact-analyzer
description: Use when a change may affect metrics, semantic models, dashboards, reporting, or stakeholder-facing KPIs and you need to know whether business meaning or downstream BI assets change.

Metric Impact Analyzer

Use this skill when a change may affect metrics, semantic models, dashboards, reporting, or stakeholder-facing KPIs.

Goals

  • Detect whether a technical change also changes business meaning.
  • Identify likely downstream breakage in BI tooling and dashboards.
  • Separate harmless refactors from definition-level changes.

What to Inspect

  • Metric definitions and calculation logic.
  • Semantic model changes (dimensions, measures, relationships).
  • Renamed or removed fields referenced by saved queries or dashboards.
  • Changed filters, time windows, or default values.
  • Changed joins, granularity, or fanout.
  • Contract-breaking field type changes (e.g., INT → FLOAT, nullable → NOT NULL).

Change Classification

| Change type | Risk level | Action required | |---|---|---| | Column rename with alias preserved | Low | Validate alias propagation | | Description or label change | Low | Confirm no tooling parses descriptions | | New optional field added | Low | No action unless it changes defaults | | Filter condition tightened | Medium | Validate that the new filter matches intent | | Metric formula changed | High | Stakeholder review required | | Grain changed (e.g., day → week) | High | All downstream rollups must be revalidated | | Dimension removed | Breaking | Cannot ship without migration plan | | Join path changed | Breaking | All dependent metrics may be affected |

Analysis Steps

1. Summarize what changed technically (field names, types, logic, joins). 2. Classify each change using the table above. 3. Infer what may have changed semantically — ask: "will this metric return a different number for the same time period?" 4. Identify who or what downstream may break: dashboards, saved queries, alerts, exports, downstream dbt models, BI tool semantic layers. 5. Flag whether stakeholder sign-off is required before the change is promoted.

Output Format

1. **Technical change summary** — what exactly changed, at the field/model level. 2. **Business meaning impact** — does this change what the number means to a business user? 3. **Likely reporting or dashboard breakage** — specific assets at risk. 4. **Required validation** — what must be confirmed before shipping. 5. **Stakeholder confirmation needed?** yes / no / depends — with reason.

Validation / Done Criteria

  • Technical change summary covers all modified fields, models, and logic.
  • Every change is classified by risk level using the table above.
  • Downstream breakage is identified or explicitly noted as "unknown — downstream consumers not traced."
  • Stakeholder confirmation requirement is stated as yes / no / depends, with a reason.

Rules

  • Be explicit about uncertainty when you cannot trace all downstream consumers.
  • Use plain business language in the final summary — avoid dbt/SQL jargon in the stakeholder section.
  • Do not edit code or models unless explicitly asked.
Read more
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