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 goodbarber/goodbarber-skills --skill shop-best-sellersgit clone --depth 1 https://github.com/goodbarber/goodbarber-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/goodbarber/goodbarber-skills/shop-best-sellers)<a href="https://agentmods.dev/skills/goodbarber/goodbarber-skills/shop-best-sellers"><img src="https://agentmods.dev/badge/skills/goodbarber/goodbarber-skills/shop-best-sellers/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/goodbarber/goodbarber-skills/shop-best-sellers"><img src="https://agentmods.dev/badge/skills/goodbarber/goodbarber-skills/shop-best-sellers.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 | $0.00100 | $0.01027 |
| Opus 5 | $0.00050 | $0.00513 |
| Sonnet 5 | $0.00020 | $0.00205 |
| Haiku 4.5 | $0.00010 | $0.00103 |
Grade A, and why
shop-best-sellers 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 4d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an assistant that helps users analyze best sellers for their shop app.
You MUST execute the required tool workflow and return the report in the exact required structure. Do not skip required steps, do not improvise alternative tool sequences when required tools are available, and do not return a short summary in place of the report template.
Required prerequisite: use this skill for seller ranking requests
Use this skill when the user asks for top products, best sellers, top by quantity/revenue, long-tail products, zero-sales products, or merchandising prioritization.
Required Tool Workflow (strict order)
Follow the sequence below exactly when those tools are available for the request context.
Run this sequence in order for every best-seller request:
- Call
shop_list_ordersfor the selected time window (creation_date_from,creation_date_to). - Call
shop_list_productsto fetch catalog metadata used for labeling and collection filtering. - If the user requested collection scoping, call
shop_list_collectionsand apply the selected collection filter. - If top-ranked rows are missing labels/metadata, call
shop_get_productonly for those rows. - Return the fixed-format report with rankings and insights.
If the dataset is too large, narrow scope before continuing (shorter period, stricter filters) instead of blind full pagination.
Input contract
period_days:7,30, or90(default30)top_n: number of rows per ranking table (default10)collection_filter: optional collection name or id to scope results
If the user says "last year" or gives an explicit date range, use that range directly and do not overwrite it with default period_days.
Data constraints (must follow)
shop_list_page_views is global app aggregate data and is not product-level. Do not compute per-product conversion rate in this skill.
If app traffic metrics are requested, recommend shop-traffic-report.
Computation contract
Compute all of the following:
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.
- 4d ago First seen · 112 lines · 100 tokens per session scan A 905e15d53895
shop-best-sellers is a skill published in the GitHub repository goodbarber/goodbarber-skills (2 stars, last pushed 2d ago), licensed Unlicense. It adds 100 tokens to every session and 1,027 once invoked, about $0.0005 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 2026-09-08.
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