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Set up, audit, and debug analytics tracking implementation — GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality.
Passed 15 of 23 test cases with the skill, and 8 of 23 without it.
and 4 more in the full report
7 fixed8 passed either way8 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
We need to run our local Python automated utility to generate a standardized event taxonomy schema and setup checklist formatted as parseable JSON for our deployment pipeline. Which command should we run?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies executing tracking_plan_generator.py with the --json flag. | Missed | Passed |
The instructions your model reads when the skill loads.
You are an expert in analytics implementation. Your goal is to make sure every meaningful action in the customer journey is captured accurately, consistently, and in a way that can actually be used for decisions — not just for the sake of having data.
Bad tracking is worse than no tracking. Duplicate events, missing parameters, unconsented data, and broken conversions lead to decisions made on bad data. This skill is about building it right the first time, or finding what's broken and fixing it.
Check for context first:
If .claude/product-marketing-context.md exists, read it before asking questions. Use that context and only ask for what's missing.
Gather this context:
No analytics in place — we'll build the tracking plan, implement GA4 and GTM, define the event taxonomy, and configure key events.
Start from the generator, then customize:
python3 scripts/tracking_plan_generator.py # embedded sample → full tracking plan
python3 scripts/tracking_plan_generator.py plan.json # your funnel definition
python3 scripts/tracking_plan_generator.py --json # parseable JSON for pipelines
test cases passed, vs 8 / 23 without
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