The SKAW Glossary
Essential terms for understanding the agentic age.
Agent Engagement
One of the five SKAW scoring pillars. Measures whether AI agents are actually visiting your store, which agents are crawling, and which pages they're reading. High Agent Engagement means the bots are paying attention — low engagement means you may be invisible to them entirely.
Agent Readiness
How well-prepared a Shopify store is to be found, read, and recommended by AI shopping agents such as ChatGPT, Perplexity, and Google AI. SKAW measures Agent Readiness as a score from 0–100 across five pillars: Data Quality, Structure, Policies, Schema, and Agent Engagement.
Agentic Commerce
The shift in e-commerce where AI agents — rather than humans — perform the research, comparison, and recommendation of products. Instead of a shopper Googling "best waterproof jacket under $200", they ask ChatGPT, and the agent picks the store.
Data Quality
One of the five SKAW scoring pillars. Measures whether your product descriptions are complete, specific, and fact-rich. AI agents rely heavily on description text when comparing products — vague or thin descriptions reduce your chances of being recommended.
Policies
One of the five SKAW scoring pillars. Measures whether an AI agent can quickly locate and understand your returns, shipping, and privacy policies. Agents often check policies before recommending a store — if they can't find yours, your store may be passed over.
Query Simulator
A SKAW Pro feature that lets merchants test how an AI agent would respond to real product queries. Enter a question (e.g. "best running shoes under $150 that ship to Canada") and see how your products would rank in an agent's response — before any real shopper asks.
Quick Win
A prioritised fix identified by SKAW during an audit. Quick Wins are ranked by impact — the issues at the top of the list will improve your SKAW Score the most for the least effort. Each Quick Win comes with plain-English instructions and can be exported to Shopify Sidekick for automated fixes.
Rollout
A SKAW Pro feature that creates a phased fix plan for your store. Rather than presenting all issues at once, Rollout sequences improvements by priority so merchants can work through them systematically without being overwhelmed.
Schema (Schema.org markup)
One of the five SKAW scoring pillars. Measures whether your store's structured data markup (Schema.org) is valid and complete. Schema markup is the machine-readable layer that helps AI agents understand what your products are, what they cost, and whether they're in stock — without having to interpret your page visually.
Sidekick Brief
A formatted export generated by SKAW that can be pasted directly into Shopify Sidekick, Shopify's built-in AI assistant. The brief gives Sidekick a prioritised list of fixes so it can apply optimisations to your store without you having to manually translate the audit results.
SKAW Alerts
Notifications triggered when SKAW detects a significant issue with your store's AI readiness — such as missing product data, Schema markup errors, or unreadable policy pages. Alerts are prioritised so you know which issues to address first.
SKAW Score
A single number from 0 to 100 representing your store's overall Agent Readiness. Calculated across the five scoring pillars: Data Quality, Structure, Policies, Schema, and Agent Engagement. A score of 80 or above is the recommended target for strong agent visibility. Scores are colour-coded: Agent Ready (green, 80+), Partially Ready (amber, 50–79), and Not Agent Ready (red, below 50).
SKAWboard
The central dashboard where merchants monitor their store's Agent Readiness. The SKAWboard displays your overall SKAW Score, a breakdown across all five scoring pillars, your AI Activity feed, and a prioritised list of Quick Wins. Available on all plans — SKAW Free includes a full score snapshot; SKAW Pro adds complete catalogue depth across all pillars.
Structure
One of the five SKAW scoring pillars. Measures whether your product titles, attributes, and variants are organised in the way AI agents expect. Well-structured products are easier for agents to parse, compare, and recommend. Poorly structured ones — inconsistent titles, missing attributes, jumbled variants — are frequently skipped.
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