# AI commerce readiness auditor Human Guide

## What This Is For
Determines whether AI shopping surfaces are relevant to a product category and audits the product data, merchant information, pricing, availability, offers, and evidence needed for. It gives the agent a clearer input/output frame for paid media: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the AI commerce readiness auditor 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 AI commerce readiness auditor.
- 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 AI commerce readiness auditor 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
- Product category was a stronger Shopping trigger than purchase-intent wording.
- Physical consumer products activated Shopping far more often than software, services, travel, or financial products.
- Commercial intent amplified activation within eligible product categories but was not sufficient by itself.
- Product constraints helped only after the category was plausibly eligible.
- financial or telecom product
- Define target product categories, markets, engines, and commercial prompt families.
- Test whether the shopping surface activates using repeated representative prompts.
- all available merchants or offers
- canonical title and description
- variants, size, color, and compatibility
- price, currency, sale status, and availability
- shipping, returns, and merchant policies

## Decision Points And Nuance
The original skill emphasizes: Research basis, Goal, Eligibility gate, Workflow, Output.

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
- Never fabricate reviews, ratings, availability, discounts, or product claims.

## Copy-And-Paste Prompt
```text
Use the AI commerce readiness auditor 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 Profound skill entry for `ai-commerce-readiness-auditor`.

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

# AI Commerce Readiness Auditor

## Research basis

This skill operationalizes Profound's research, [We reverse-engineered ChatGPT's Shopping trigger](https://www.tryprofound.com/blog/chatgpt-shopping-prediction), published March 17, 2026.

Key takeaways from the study:

- Product category was a stronger Shopping trigger than purchase-intent wording.
- Physical consumer products activated Shopping far more often than software, services, travel, or financial products.
- Commercial intent amplified activation within eligible product categories but was not sufficient by itself.
- Product constraints helped only after the category was plausibly eligible.

The study reports historical observational behavior, not an official OpenAI specification. Verify current merchant and feed requirements using official platform documentation.

## Goal

Decide whether an AI commerce surface deserves investment, then find the data and content gaps that could prevent accurate product inclusion or merchant routing.

## Eligibility gate

Before auditing details, classify the offering:

- shippable consumer product
- consumable or grocery
- large equipment or vehicle
- software or digital product
- service
- travel or hospitality
- financial or telecom product
- local-only offering

If the surface rarely activates for the category, state that clearly and redirect effort to ordinary AI recommendations or another commerce surface.

## Workflow

1. Define target product categories, markets, engines, and commercial prompt families.
2. Test whether the shopping surface activates using repeated representative prompts.
3. Separate three outcomes:
   - product inclusion
   - headline merchant or offer
   - all available merchants or offers
4. Audit product data:
   - canonical title and description
   - brand and identifiers
   - category and attributes
   - variants, size, color, and compatibility
   - images
   - price, currency, sale status, and availability
   - shipping, returns, and merchant policies
   - reviews and current evidence
5. Compare feeds, product pages, structured data, merchant records, and marketplaces for conflicts.
6. Check freshness and update latency.
7. Identify recommendation gaps across use cases, constraints, comparisons, and "best for" framing.
8. Prioritize data correctness before editorial optimization.
9. Retest over multiple runs because cards and merchant offers can vary.

Never fabricate reviews, ratings, availability, discounts, or product claims.

## Output

Return:

1. Surface-fit decision: invest, test narrowly, or deprioritize.
2. Activation evidence by prompt family.
3. Product-data readiness scorecard.
4. Conflict ledger across feeds, pages, and merchants.
5. Priority fixes with source system, owner, and freshness requirement.
6. Recommendation-content opportunities supported by real product evidence.
7. Measurement plan for inclusion consistency, headline-offer share, all-offer share, and downstream outcomes.