Game Design KPI Coverage Audit

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name: game-design-kpi-coverage-audit description: Audit a game feature, roadmap candidate, UX improvement, support system, connective-tissue feature, or quality-of-life change for KPI coverage bias and measurement blind spots. Use when a team is overvaluing only directly attributable metrics, neglecting foundational work that is hard to measure, or struggling to justify features whose value is real but not cleanly tied to one headline KPI.

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Game Design KPI Coverage Audit

Check whether the evaluation framework is seeing the whole value of the feature, or only the parts that are easy to measure.

Use this skill when a feature is being judged mainly through directly attributable KPIs and you suspect that measurement logic is biasing the team toward flashy, self-contained systems while undervaluing connective tissue, UX, quality-of-life, enabling systems, or long-term structural work.

Read references/value-types.md when identifying what kind of value the feature creates. Read references/blind-spot-patterns.md when diagnosing common KPI-coverage failures. Read references/recommendation-patterns.md when deciding how to justify or evaluate hard-to-measure work.

What to produce

Produce: 1. Feature read - what the proposal is and what role it plays 2. Current KPI framing - what the team is measuring or expecting to measure 3. Coverage diagnosis - what value is covered versus ignored by those KPIs 4. Blind spots - what may be neglected because it is hard to measure directly 5. Risk of mis-prioritization - what bad decisions may result from the current framing 6. Evaluation recommendation - how the feature should be justified, monitored, or compared more fairly

Process

1. Identify the role of the feature

Clarify whether the proposal is mainly:

a standalone engagement feature

a monetization feature

a progression layer

a UX improvement

connective tissue between systems

quality-of-life work

support infrastructure for future features

a clarity, pacing, or usability improvement

2. Identify the current KPI story

Ask:

what metric is the team using to justify this feature?

is it tied to revenue, retention, engagement, conversion, economy balance, sentiment, or something else?

is the KPI direct, indirect, speculative, or absent?

3. Audit KPI coverage

Check whether the metric framing captures the actual value of the work. Look for value types such as:

direct monetization

direct engagement lift

retention support

reduced friction

improved comprehension

stronger connective tissue between systems

future feature enablement

long-term sustainability

reduced support burden or balancing burden

4. Identify blind spots

Common signs:

the feature is dismissed because it cannot move one headline KPI on its own

direct-revenue features are always favored over structural health

UX work is treated as optional because it lacks clean attribution

foundational work is postponed until crisis

enabling systems are undervalued because they mostly improve the performance of other features

5. Judge prioritization risk

Ask:

what happens if this feature is judged only by direct KPI lift?

is the team likely to underinvest in maintenance, UX, clarity, infrastructure, or connective tissue?

could the current framework systematically reward short-term visible wins over long-term health?

6. Recommend better evaluation

Possible moves:

use a mixed scorecard instead of one KPI

classify the feature as enabling or connective work rather than forcing a fake direct KPI

evaluate via downstream support of other systems

allocate protected capacity for high-value, hard-to-measure work

compare opportunity cost honestly rather than pretending everything must tie to one direct metric

Response structure

Feature Read

...

Current KPI Framing

...

Coverage Diagnosis

...

Blind Spots

...

Risk of Mis-Prioritization

...

Recommendation

...

Fast mode

What is this feature actually for?

What KPI is being used to justify it?

What important value is not being captured by that KPI?

What bad prioritization decision could this cause?

How should the team evaluate it more fairly?

Style rules

Do not dismiss KPIs; diagnose their limits.

Do not invent fake measurable certainty for support work.

Distinguish direct value from enabling value.

Prefer fairer framing over anti-metrics rhetoric.

Be specific about how blind spots distort roadmap decisions.

Working principle

Teams often prioritize what they can measure cleanly, not what matters most. Use this skill to expose where KPI logic is too narrow for the actual design value on the table.