# Cross-platform attribution health audit Human Guide

## What This Is For
Cross-platform attribution health audit covering AdAttributionKit (iOS view-through 24h post-impression, WWDC 2025 configurable windows), GA4 attribution models (data-driven vs. It gives the agent a clearer input/output frame for conversion optimization: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Cross-platform attribution health audit agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for cross-platform attribution health audit.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Cross-platform attribution health audit guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- Read the main `ads` contract and normalized account snapshots.
- Declare the business conversion, value, data window, timezone, currency, and
- Inventory every browser, server, platform, analytics, MMP, offline, and app
- Reconcile comparable events and explain differences caused by eligibility,
- Separate measurement quality from platform-reported performance.
- Return findings, contradictions, confidence, missing evidence, and a measurement

## Decision Points And Nuance
The original skill emphasizes: Comparability gate.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- decision the attribution analysis must support.
- Do not assume one platform is ground truth, add incompatible reports together, or
- definitions; do not compute a total.

## Copy-And-Paste Prompt
```text
Use the Cross-platform attribution health audit human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the agricidaniel/claude-ads skill entry for `ads-attribution`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# Attribution Audit

1. Read the main `ads` contract and normalized account snapshots.
2. Declare the business conversion, value, data window, timezone, currency, and
   decision the attribution analysis must support.
3. Inventory every browser, server, platform, analytics, MMP, offline, and app
   attribution source with its identity, counting, deduplication, and privacy rules.
4. Reconcile comparable events and explain differences caused by eligibility,
   view-through rules, consent, modeled data, conversion lag, thresholds, or scope.
5. Separate measurement quality from platform-reported performance.
6. Return findings, contradictions, confidence, missing evidence, and a measurement
   improvement plan through the common JSON contract.

Do not assume one platform is ground truth, add incompatible reports together, or
recommend an attribution model without the operator's decision context.

## Comparability gate

Reject aggregation until the sources share, or are explicitly normalized to, the
same conversion event and value definition, attribution window, click/view scope,
counting method, deduplication identity, timezone, currency, attribution model,
and modeled-data treatment. Until then, report the values side by side with their
definitions; do not compute a total.

Example: Meta seven-day conversions and Google thirty-day conversions are
incompatible. Refuse to add them, reconcile windows and definitions first, and
only aggregate a newly comparable dataset.