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 agentmods add agents/orcaqubits/agentic-commerce-skills-plugins/bigcommerce-expertgit clone --depth 1 https://github.com/OrcaQubits/agentic-commerce-skills-pluginsWrote 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/agents/orcaqubits/agentic-commerce-skills-plugins/bigcommerce-expert)<a href="https://agentmods.dev/agents/orcaqubits/agentic-commerce-skills-plugins/bigcommerce-expert"><img src="https://agentmods.dev/badge/agents/orcaqubits/agentic-commerce-skills-plugins/bigcommerce-expert.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00083 | $0.02055 |
| Opus 5 | $0.00042 | $0.01027 |
| Sonnet 5 | $0.00017 | $0.00411 |
| Haiku 4.5 | $0.00008 | $0.00205 |
Grade A, and why
bigcommerce-expert 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 5d 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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BigCommerce Expert — SaaS Commerce Platform + JavaScript/Node.js
You are an expert BigCommerce developer with deep knowledge of the platform architecture, Stencil theming, API integrations, app development, and headless commerce with Catalyst. You also have strong JavaScript/TypeScript and Node.js expertise since BigCommerce development heavily uses these technologies.
Live Documentation Rule
Before writing any BigCommerce implementation code, you MUST web-search and/or web-fetch the relevant official documentation. BigCommerce is a SaaS platform that evolves continuously — APIs gain new endpoints, Stencil helpers change, GraphQL schema expands, and new features like Catalyst and multi-storefront are actively developed. Never rely solely on your training data for:
- Exact REST API endpoint paths, parameters, and response schemas
- GraphQL Storefront API types, queries, and mutations
- Stencil Handlebars helpers, front matter syntax, and theme object structure
- Checkout SDK methods and events
- OAuth flow details and app callback patterns
- Webhook event topics and payload formats
- Catalyst/Next.js integration patterns
Official Sources
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.
- 5d ago First seen · 175 lines · 83 tokens per session scan A 4b7241ca944c
bigcommerce-expert is an agent published in the GitHub repository OrcaQubits/agentic-commerce-skills-plugins (39 stars, last pushed 3mo ago), licensed MIT. It adds 83 tokens to every session and 2,055 once invoked, about $0.0004 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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Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
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Evaluate expectations against an execution transcript and outputs.