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 Orkas-AI/Orkas-Awesome-AgentSkills --skill merch-listinggit clone --depth 1 https://github.com/Orkas-AI/Orkas-Awesome-AgentSkillsWrote 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/orkas-ai/orkas-awesome-agentskills/merch-listing)<a href="https://agentmods.dev/skills/orkas-ai/orkas-awesome-agentskills/merch-listing"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/merch-listing/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/orkas-ai/orkas-awesome-agentskills/merch-listing"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/merch-listing.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 | $0.00035 | $0.00958 |
| Opus 5 | $0.00017 | $0.00479 |
| Sonnet 5 | $0.00007 | $0.00192 |
| Haiku 4.5 | $0.00003 | $0.00096 |
Grade A, and why
merch-listing 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.
What it actually says
商品页面优化
用于商品上架文案和页面结构优化。核心任务是把商品信息、证据、平台限制和用户购买阻力组织成可发布前复核的页面方案。
何时使用
- 用户需要生成或优化商品标题、卖点、bullet、详情描述、SEO 关键词、FAQ、短视频口播草稿或套装文案。
- 用户要适配淘宝、拼多多、Amazon、Shopify、Shopee、AliExpress 等平台页面。
- 用户提供 Amazon listing、竞品 ASIN、页面材料、评价摘要、图片素材或品牌定位，希望做 listing/A+ 优化。
- 用户要诊断现有页面：关键词缺失、卖点不清、结构混乱、合规风险或竞品机会。
不用于选品市场研究、评论数据归因、商品图 prompt、视频广告分镜、图片生成或广告投放。
如何调用
- 收集产品名称、类目、目标平台、目标市场、目标人群、价格/SKU、核心卖点、品牌调性、竞品信息、证据材料和合规限制。
- 判断路径：
- 跨平台标题、卖点、描述、FAQ：使用
references/cross-platform-listing.md。 - Amazon listing、A+、图片顺序、竞品页面：使用
references/amazon-listing-and-a-plus.md。
- 跨平台标题、卖点、描述、FAQ：使用
- 所有销量、排名、认证、功效、优惠、评价、对比结论都要标注来源；没有来源就写“需证据支持”或改为中性表达。
- 输出前做合规检查：极限词、虚假稀缺、医疗功效、侵权品牌词、平台禁词、未证实数据、误导性对比。使用
references/listing-compliance.md。 - 如果材料不足，先给可用草稿，并列出缺失信息和需要人工确认的证据。
- 返回结构使用
references/listing-output-templates.md。
返回格式
- 输入摘要和缺失信息。
- 目标平台适配说明。
- 标题方案和关键词布局。
- 卖点 / bullet / 描述 / FAQ。
- Amazon A+ 或图片顺序建议，如适用。
- 竞品页面机会和待验证数据。
- 合规风险清单和人工确认项。
外部依赖
- 无必需外部依赖。
- 若要使用真实搜索量、排名、竞品销量、广告词数据，需要用户提供平台后台、合法导出或第三方数据结果。
- 若用户只提供 ASIN 或页面链接且无法联网访问，需要用户上传截图、文案或页面材料。
限制与已知问题
- 不保证曝光、排名、转化、销量或平台审核通过。
- 不编造“月销 10 万+”“全网第一”“医生推荐”等声明。
- 不生成侵权品牌词、竞品商标堆砌或误导性比较。
- 跨境文案需要用户确认目标国家、语言、单位、合规限制和禁售规则。
- 高风险类目必须保守表达并提示人工复核。
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 56 lines · 35 tokens per session scan A 516d57b5a22d
merch-listing is a skill published in the GitHub repository Orkas-AI/Orkas-Awesome-AgentSkills (13 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 958 once invoked, about $0.0002 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.
Other skills, from other repositories
viral-tech-reel-editor
End-to-end viral tech reel production for Instagram Reels and TikTok using 2026 trend grammar — retention-first pacing, punch-ins, 3D cinematic AI-generated shots, motion design graphics, proof b-roll, trending SFX, karaoke captions (Georgian/English), safe-zone layout, and QA-gated 1080x1920 export. This skill should…
billing-automation
Build automated billing systems for recurring payments, invoicing, subscription lifecycle, and dunning management. Use when implementing subscription billing, automating invoicing, or managing recurring payment systems.
hunt-business-logic
Hunting skill for business logic vulnerabilities. Built from 7 public bug bounty reports. Use when hunting business logic on any target.
should-i-buy
Helps the user decide on a purchase by taking the product links they're considering, asking a couple of sharp clarifying questions about their needs and circumstances, then opening each link in real Chrome via the Claude-in-Chrome extension to extract price, specs, ratings, reviews, return policy, and shipping.…
shop-research
Researches products across Amazon, Google Shopping, and relevant specialty sites using the Claude-in-Chrome browser extension. Finds candidates matching user-specified criteria (gift, gadget, gear, home goods, etc.), captures screenshots and key data per candidate, then generates a modern 2026 HTML report with…
MarketplaceStrategist
Complete two-sided marketplace intelligence — cold start strategy, liquidity engineering, take rate optimization, trust and safety, supply/demand balance, and scaling from 100 to 10M transactions.