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Post-signup user onboarding optimization covering activation metrics, time-to-value reduction, onboarding flow design, empty state optimization, multi-channel coordination, and stalled user recovery.
Passed 17 of 22 test cases with the skill, and 7 of 22 without it.
and 12 more in the full report
10 fixed7 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
We have raw step-by-step onboarding drop-off counts stored in `funnel_data.json`. We need to run a command line script to analyze drop-offs and output formatted JSON results. What command line script and flag should we execute?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies running `python scripts/activation_funnel_analyzer.py funnel_data.json`. | Missed | Passed |
| Includes the `--json` flag to request JSON output. | Passed | Passed |
The instructions your model reads when the skill loads.
Production-grade user onboarding optimization framework covering activation definition, time-to-value engineering, flow architecture, empty state design, multi-channel coordination, stalled user recovery, and experiment design. Focused on the critical window between signup and habitual product usage.
Before designing the onboarding, 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 deliverable.
test cases passed, vs 7 / 22 without
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