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Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization.
Passed 21 of 22 test cases with the skill, and 6 of 22 without it.
and 16 more in the full report
16 fixed6 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
I want to evaluate touchpoints in `quarterly_journeys.json` where every interaction receives equal weight across the journey. Which CLI invocation should be used?
| Checked | Without skill | With skill |
|---|---|---|
| recommends running `python scripts/attribution_analyzer.py quarterly_journeys.json --model linear` | Missed | Passed |
The instructions your model reads when the skill loads.
Production-grade campaign performance analysis with multi-touch attribution modeling, funnel conversion analysis, and ROI calculation. Three Python CLI tools provide deterministic, repeatable analytics using standard library only -- no external dependencies, no API calls, no ML models.
All scripts accept a JSON file as positional input argument. See assets/sample_campaign_data.json for complete examples.
{
"journeys": [
{
"journey_id": "j1",
"touchpoints": [
{"channel": "organic_search", "timestamp": "2025-10-01T10:00:00", "interaction": "click"},
{"channel": "email", "timestamp": "2025-10-05T14:30:00", "interaction": "open"},
{"channel": "paid_search", "timestamp": "2025-10-08T09:15:00", "interaction": "click"}
],
"converted": true,
"revenue": 500.00
}
]
}
{
"funnel": {
"stages": ["Awareness", "Interest", "Consideration", "Intent", "Purchase"],
"counts": [10000, 5200, 2800, 1400, 420]
}
}
test cases passed, vs 6 / 22 without
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