attribution-ads-helper_skill

This skill helps diagnose attribution across channels, compare window effects, and output practical, confidence-labeled allocation guidance for optimization.

Python

2.6k

GitHub Stars

3

Bundled Files

2 months ago

Catalog Refreshed

3 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill attribution-ads-helper

_meta.json308 B
metadata.json79 B
SKILL.md3.7 KB

Overview

This skill builds cross-channel attribution analysis and decision guidance for Meta, Google Ads, TikTok, YouTube, Amazon, Shopify Ads, and DSP/programmatic campaigns. It diagnoses attribution discrepancies, quantifies window sensitivity, and produces allocation recommendations with confidence labels and validation plans. The goal is actionable, attribution-aligned budget decisions that respect tracking limits and incremental evidence.

How this skill works

The skill ingests channel metrics broken out by attribution window, conversion event definitions, and a decision context. It normalizes events, compares performance across windows, and produces an Attribution Mismatch Map and Window Sensitivity Analysis. It then quantifies decision deltas, proposes guarded budget moves or blends when variance is high, and outputs validation experiments for unresolved gaps.

When to use it

When channels report conflicting ROAS/CAC or diverging contribution shares
Before reallocating budget when attribution window assumptions differ
When you need a decision-safe KPI view for cross-platform optimization
To design validation experiments or interpret holdout/incrementality results
If offline conversions or time-lagged sales may change channel credit

Best practices

Normalize conversion definitions before comparing channels; do not mix inconsistent events in a single conclusion
Show explicit per-channel window comparisons (e.g., 1d-click vs 7d-click) and percent deltas
Label recommendations with confidence and cap budget shifts when attribution variance is high
Prioritize incremental/holdout evidence over platform-reported proxies when available
Design small, time-boxed validation experiments for high-stakes reallocations

Example use cases

1d vs 7d dispute: produce sensitivity table, decision-safe policy, and validation plan
Cross-channel reallocation: diagnose Meta vs Google ROAS mismatch and propose guarded shifts
Incrementality integration: reconcile holdout test data with platform reports and update allocations
Time-lagged sales: flag blind spots where offline conversion pipelines are missing and recommend conservative caps

FAQ

Required inputs are channel_metrics_by_window, attribution_windows, conversion_event_definitions, and decision_context. Optional inputs include offline conversion data and holdout/incrementality data.

How does the skill handle high attribution variance?

It applies a blended guardrail plan, caps budget shifts (e.g., max_shift_pct), and recommends validation tranches until variance is reduced by testing or incremental evidence.

Can it prioritize incremental test results over platform reports?

Yes. Incrementality or holdout evidence is prioritized and will update confidence labels and allocation recommendations accordingly.