Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add 40RTY-ai/shopify-admin-skills --skill shopify-admin-agentic-product-jsonld-backfillgit clone --depth 1 https://github.com/40RTY-ai/shopify-admin-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/40rty-ai/shopify-admin-skills/shopify-admin-agentic-product-jsonld-backfill)<a href="https://agentmods.dev/skills/40rty-ai/shopify-admin-skills/shopify-admin-agentic-product-jsonld-backfill"><img src="https://agentmods.dev/badge/skills/40rty-ai/shopify-admin-skills/shopify-admin-agentic-product-jsonld-backfill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/40rty-ai/shopify-admin-skills/shopify-admin-agentic-product-jsonld-backfill"><img src="https://agentmods.dev/badge/skills/40rty-ai/shopify-admin-skills/shopify-admin-agentic-product-jsonld-backfill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.01825 |
| Opus 5 | $0.00027 | $0.00912 |
| Sonnet 5 | $0.00011 | $0.00365 |
| Haiku 4.5 | $0.00005 | $0.00183 |
Grade A, and why
shopify-admin-agentic-product-jsonld-backfill scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
AI shopping agents read a product's structured data (the fields Shopify themes emit as schema.org/Product JSON-LD) to confirm price, availability, and identity. Missing barcodes (GTIN), SKUs, vendor, or product type leave the listing ambiguous — so the agent skips it or recommends a competitor whose data is complete. This skill finds products/variants with those gaps and backfills them: vendor and product type at the product level, barcode/SKU at the variant level. Fixes the agentiq.report findings product-schema-jsonld, gtin-sku-pdp, and variant-metadata.
Prerequisites
- Authenticated Shopify CLI session (
shopify auth login --store <domain>) - Required API scopes:
read_products,write_products
Parameters
All skills accept these universal parameters:
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| store | string | yes | — | Store domain (e.g., mystore.myshopify.com) |
| format | string | no | human | Output format: human (default) or json |
| dry_run | bool | no | false | Preview mutations without executing |
Skill-specific parameters:
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| collection_id | string | no | — | Limit to a collection GID (else whole catalog) |
| tag | string | no | — | Limit to a product tag |
| set_vendor | string | no | — | Vendor to apply where missing (else only reports) |
| set_product_type | string | no | — | Product type to apply where missing |
| barcodes_csv | string | no | — | Path to a CSV of sku,barcode to map GTINs onto matching variants |
| fields | string | no | all | Comma list of fields to backfill: vendor,product_type,barcode,sku |
Safety
⚠️ Step 3 (
productUpdate) and Step 4 (productVariantsBulkUpdate) write live product/variant data. Barcodes and SKUs are matched from yourbarcodes_csv; a wrong mapping mislabels a product's identity to every agent. Always rundry_run: truefirst and verify the change set CSV. This skill never overwrites a field that already has a value — it only fills blanks.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 183 lines · 54 tokens per session scan A 69788e767bb3
shopify-admin-agentic-product-jsonld-backfill is a skill published in the GitHub repository 40RTY-ai/shopify-admin-skills (185 stars, last pushed 28d ago), licensed MIT. It adds 54 tokens to every session and 1,825 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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