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Product analytics for instrumenting products, defining metrics, and building retention funnels.
Passed 14 of 23 test cases with the skill, and 7 of 23 without it.
and 7 more in the full report
8 fixed6 passed either way8 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
We compiled our raw weekly active user cohort offset counts into retention.json. Just draft the exact command-line invocation to run our retention analyzer script and generate a markdown summary.
| Checked | Without skill | With skill |
|---|---|---|
| Executes retention_cohort_analyzer.py with --input retention.json. | Missed | Passed |
The instructions your model reads when the skill loads.
A product analytics skill focused on decisions from data, not dashboards. Covers the metric tree, instrumentation patterns, funnel + retention + cohort analysis, and the operational rituals that turn measurement into product changes.
Before designing the metric tree or audit, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
test cases passed, vs 7 / 23 without
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