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name: metric-gaslighting-detector description: "Find out how a dashboard, KPI report, or metrics slide is lying to you — before you repeat its story in a bigger room. Use when numbers feel too tidy, a narrative rests on one chart, or you inherited metrics you didn't define. Produces a deception audit: every metric graded for the eleven classic distortions (denominator games, survivorship, y-axis crimes, cherry-picked windows…), the story the data would tell under honest framing, and the three questions to ask the metric's owner."

Metric Gaslighting Detector

Dashboards rarely contain false numbers. They contain true numbers arranged to create false beliefs. This skill audits the arrangement — the eleven standard distortions through which honest data becomes dishonest narrative.

Required Inputs

• The metrics artifact — the dashboard description, KPI table, chart, or the numbers with their labels exactly as presented. Include axis ranges, time windows, and any annotations; the lie usually lives there.
• The claim being made with it (if any) — "churn is under control", "the launch worked". The audit tests the claim-data connection, not the data alone.

The Eleven Distortions

1. Denominator games — the base changed ("of active users" quietly became "of weekly active")
2. Survivorship framing — measuring only what remained (retention of cohorts that didn't churn early)
3. Y-axis crimes — truncated baselines, dual axes, log scales without labels
4. The cherry window — the date range that starts at the trough or ends before the drop
5. Mix-shift laundering — the aggregate improved because composition changed, not performance
6. Ratio without magnitude — "+40%!" concealing 5→7
7. The vanity proxy — measuring what moves instead of what matters (signups for activation)
8. Goodhart's ghost — the metric improved because it became a target, and the gamed behaviour is visible elsewhere
9. Smoothing to silence — rolling averages wide enough to bury the event being asked about
10. The missing counterfactual — "up 20% since launch" with no baseline trend (it was up 25% before)
11. Significance theatre — differences within noise presented as movement ("ticked up to 4.6 from 4.5, n=41")

Output Format

1. The audit table — metric | distortion(s) detected | severity (🔴 changes the conclusion / 🟡 shades it / 🟢 clean) | the honest version of that number's sentence.
2. The honest retelling (≤150 words) — what this data says under fair framing. Sometimes the story survives; say so — the detector earns trust by clearing metrics too.
3. Three questions for the owner — specific, answerable, non-accusatory ("what was the trend in the 8 weeks before launch?"), ordered by how much the answer would change the conclusion.
4. The one chart to request — the single re-cut (full window, fixed denominator, split by segment) that would settle the biggest 🔴.

Quality Checks

• Every 🔴 names the specific mechanism and what the conclusion becomes without it — "misleading" alone is not a finding
• At least one metric is graded 🟢 or the audit admits the artifact gave nothing to clear — all-guilty audits read as motivated
• The honest retelling uses only the numbers present — the detector doesn't smuggle in its own speculation
• Questions are answerable from data the owner plausibly has, and none contain an accusation
• Distortion names from the list are used consistently so repeated audits build a shared vocabulary

Anti-Patterns

• Do not accuse people of lying — the framing is "what belief does this arrangement create vs what the data supports"; most gaslighting dashboards are self-deception forwarded
• Do not grade a metric 🔴 for a distortion that doesn't change the decision at hand — severity is about consequences, not purity
• Do not demand data that doesn't exist as a gotcha — the three questions must be realistically answerable
• Do not rewrite the numbers — the honest retelling reframes; it never adjusts figures
• Do not skip auditing metrics that support conclusions you like — run the eleven on the favourable ones first

Example Trigger Phrases

• "These numbers feel too tidy."
• "Is this dashboard lying to me?"
• "Audit this KPI report before I present it."
• "I inherited these metrics: can I trust them?"