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 commerce-agentic/agentic-commerce-skills --skill agentic-commerce-query-shopping-agentsgit clone --depth 1 https://github.com/commerce-agentic/agentic-commerce-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/commerce-agentic/agentic-commerce-skills/agentic-commerce-query-shopping-agents)<a href="https://agentmods.dev/skills/commerce-agentic/agentic-commerce-skills/agentic-commerce-query-shopping-agents"><img src="https://agentmods.dev/badge/skills/commerce-agentic/agentic-commerce-skills/agentic-commerce-query-shopping-agents/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/commerce-agentic/agentic-commerce-skills/agentic-commerce-query-shopping-agents"><img src="https://agentmods.dev/badge/skills/commerce-agentic/agentic-commerce-skills/agentic-commerce-query-shopping-agents.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00051 | $0.01231 |
| Opus 5 | $0.00026 | $0.00616 |
| Sonnet 5 | $0.00010 | $0.00246 |
| Haiku 4.5 | $0.00005 | $0.00123 |
Grade A, and why
agentic-commerce-query-shopping-agents 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
The signature skill of the agentic-commerce-skills library. Sends a single shopping query to 5 AI shopping agents in parallel (ChatGPT, Gemini, Claude, Mistral, DeepSeek) and returns each agent's ranked product recommendations.
This is what makes AI catalog visibility measurable. Without cross-agent ground truth, "AI visibility" is a vibe; with it, it is a metric you can A/B test fixes against (see agentic-commerce-visibility-report).
Together with the Agentic Commerce Benchmarks dataset, this skill lets you measure where your products rank vs. competitors across the 6 most important AI shopping agents.
Prerequisites
API keys for the agents you want to query (each is optional; missing keys = that agent skipped):
ANTHROPIC_API_KEY— for ClaudeOPENAI_API_KEY— for ChatGPTGEMINI_API_KEY— for Gemini (free tier available, https://aistudio.google.com)MISTRAL_API_KEY— for Mistral Le ChatDEEPSEEK_API_KEY— for DeepSeek
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| query | string | yes | — | Shopping query in natural language. Examples: "best winter jacket under $200 for hiking", "sustainable bamboo socks for sensitive skin" |
| agents | array | no | all-configured | Subset of agents to query: claude, chatgpt, gemini, mistral, deepseek. Skip with missing API keys. |
| vertical | string | no | — | Hint to the agents about category: apparel, beauty, home, electronics, fitness, food, pets, baby, outdoor, gifts |
| price_ceiling | float | no | — | Max price in USD (passed to agents in system prompt) |
| format | string | no | human | Output format: human or json |
| timeout_seconds | integer | no | 60 | Per-agent timeout |
Workflow Steps
- Build the shared shopping prompt:
You are a shopping recommendation assistant. Given the query below, provide a numbered list of 5-10 specific product recommendations. For each: brand + product name, approximate price, why you recommend it (1-2 sentences), and where to buy it (merchant/website). Format each recommendation as: 1. **Brand ProductName** - $XX.XX Reason for recommendation. Available at: https://merchant.com/product-link Query: {query} {vertical and price ceiling hints if provided}
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 · 96 lines · 51 tokens per session scan A 931564546897
agentic-commerce-query-shopping-agents is a skill published in the GitHub repository commerce-agentic/agentic-commerce-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 51 tokens to every session and 1,231 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 2026-08-31.
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