For anyone who has to stand behind a figure in a meeting. Works out what your data can and cannot answer, then hunts for the finding that’s true and misleading.
Packs come in three formats: Claude Skills, Gemini Gems and Custom GPTs, and include full worked examples so you can see the output before committing your to it.
A membership organisation looks at five years of data and finds its renewal rate has dropped from 84 per cent to 76. The calculation is correct. The data is clean. The finding is real.
Three days before it reaches the board as evidence of declining member satisfaction, somebody breaks the number down by category. Every single category is flat or improving. The overall rate fell because a discounted early-career grade had grown from 5 per cent of the membership to nearly 16, and that grade renews at 61 per cent, which is entirely normal for a discounted entry tier.
There was no renewal problem. There was a successful recruitment campaign, and a metric describing the change in mix rather than any change in behaviour.
That’s the failure this skill exists for. It survives review, because the number checks out.
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