Hermes repairs Shopify schema after backing it up
Stewart reports that Hermes diagnosed and repaired a client’s Shopify schema after backups; subsequent Google Shopping bot activity was observed as correlation.
stewartmedia.com.au
Suggested prompt
Suggested prompt written by Agents In Use, not the source author. Replace bracketed inputs and review tool permissions first.
Investigate [observed product-snippet decline] using [authorised site data]. Propose a schema correction, create a restore point before changes and report the measurement source. Separate observed correlation from a claimed causal result.
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Sourced account- Source author
- Jim Stewart
- Original account
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- Published
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- Reported result
- Stewart reports that Hermes diagnosed and repaired a client’s Shopify schema after backups; subsequent Google Shopping bot activity was observed as correlation.
- Human involvement
- Stewart supplies client goals and builds the specialists and skills. Backups precede site changes; the post does not specify an explicit approval click for this repair.
A linked account supports attribution. It does not mean we independently reproduced its result.
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How the agent did it
- Use SKAW observations to identify a drop in product snippets.
- Have a specialist diagnose the Shopify schema issue and propose a fix.
- Back up the client site before applying changes.
Jim Stewart describes a Hermes setup with specialists for Shopify engineering, search engineering and client-meeting preparation. Each has client context and its own skills. The agency's SKAW tool surfaces information from client sites, giving the agents a concrete starting point for investigating changes rather than a generic optimisation request.
After a client's product snippets and related metrics dropped, the agents diagnosed a schema problem, proposed a repair and applied it. Stewart says a backup procedure runs before any client-site change, with those backups also pushed to GitHub daily. His account identifies the sequence as diagnosis, proposal, backup and fix, although it does not specify a separate human approval click for this particular repair.
Afterwards, SKAW showed the Google Shopping bot returning. Stewart explicitly calls this correlation and says he needs observations from another two or three sites to confirm the relationship. He also notes that the change was not reflected in Google Search Console's crawl-bot traffic. The reported outcome therefore supports a completed, recoverable schema repair and an observed crawl signal. It does not establish improved sales or prove that the repair caused the bot's return. The useful operational pattern is to preserve a restore point and identify which measurement system actually supplies the result.
Prompt
Suggested prompt (written by Agents In Use, not the author's)
Source: Blog · Jim Stewart
Frequently asked questions
What safeguards preceded the change?
Stewart says the agents back up the site before applying a fix and push backups to GitHub daily.
Did the repair demonstrably increase sales?
No. The reported follow-up is Google Shopping bot activity observed through SKAW; no sales increase is established.
Why is the crawl result qualified?
Stewart calls it correlation needing confirmation on more sites, and says the activity was not reflected in Search Console crawl-bot traffic.
More details
Remaining steps
- Apply the repair and preserve daily backups in GitHub.
- Observe bot activity while separating correlation from confirmed causation.
About this account
Hermes specialists diagnose a site problem, propose and apply a schema change, and preserve a backup; SKAW supplies the subsequent crawl observation.
Firsthand account by Jim Stewart; original publication date verified against Primary article byline Writing · 12 May 2026. Outcomes are the author’s reports, not independently reproduced. Suggested prompt written by Agents In Use; not the author’s original prompt.
Credit: Jim Stewart
Source published
Added to Agents In Use
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Tools and topics
Hermes Agent · SKAW · Shopify · GitHub backups
Schema repair · Crawl monitoring · Client-site backups
Editorial completeness: 100/100. Measures documented fields, not agent performance or verified outcomes.