Observed evidence
Public signals captured by ECIndex or another reproducible inspection. Include the source and observation date.
Practical, evidence-led guidance for building storefronts that people, search engines and AI systems can understand and trust.
A practical operating guide for owners, managers, developers and advisers. It is not a guarantee of ECIndex ranking, search visibility, AI citation or commercial results.
A useful AI workflow tells the reader what was observed, what the merchant supplied, what the system inferred, and what still requires verification.
Public signals captured by ECIndex or another reproducible inspection. Include the source and observation date.
Policies, operations or commercial details supplied by an authorized representative. Label their provenance.
Proposed actions based on available evidence. Treat them as hypotheses until a qualified person reviews them.
Checks completed after implementation, including what changed, who approved it and whether the intended outcome occurred.
Start with a narrow operational problem, give the system reliable context, and require a measurable acceptance check.
Standardize names, attributes, variants, units, identifiers and descriptions without changing factual product claims.
Product schema lesson →Create page-specific metadata, structured data and useful category language grounded in visible storefront content.
Metadata guidance →Draft answers from approved policies, identify uncertainty, and route exceptions or sensitive cases to a person.
Find missing attributes, inconsistent imagery and weak comparisons while preserving the merchant’s voice.
Help teams classify delays, returns and stock discrepancies without exposing unnecessary customer data.
Translate measured speed and usability findings into prioritized tickets with owners and acceptance checks.
Run SpeedAudit ↗Create campaign variants from verified offers and brand rules. Never invent scarcity, testimonials or performance claims.
Keep prompts, sources, outputs, approvals and material changes available for review.
State the business problem, affected page or process, and the measure that would show improvement.
Use the current storefront, ECIndex profile, policies and approved product data. Avoid private customer or payment information.
Require the AI to identify gaps, uncertainty and priorities before it drafts a solution.
Check factual accuracy, legal or policy implications, platform constraints and brand fit.
Release a controlled change, test the live result, and preserve a rollback path.
Compare the new observation with the baseline and record what worked, failed or remains uncertain.
This prompt is deliberately structured to prevent an AI assistant from presenting assumptions as verified findings.
Review this storefront using the ECIndex evidence model. Store URL: [URL] Primary goal: [GOAL] Approved context: [FACTS OR LINKS] Separate your response into: 1. Observed public evidence 2. Merchant-supplied facts 3. Uncertainty and missing evidence 4. Prioritized recommendations 5. Human review required 6. Validation checks after publishing Do not invent reviews, ratings, prices, availability, certifications, policies, performance data or customer claims.
No invented ratings, testimonials, certificates, prices, stock, delivery promises or product properties.
Do not place customer, order, payment or credential information into general-purpose AI tools.
Use clear internal records and public disclosure where AI materially shapes content or decisions.
A named person owns approval, publishing, monitoring and rollback.
Validate recommendations against current provider, advertising and search requirements.
Improvements may strengthen observable readiness; they do not purchase or guarantee an ECIndex position.
The ECIndex Store Improvement Skill packages the evidence model, safety boundaries and output structure for compatible AI assistants. It does not connect to private store systems or authorize changes.