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-researchgit 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-research)<a href="https://agentmods.dev/skills/orkas-ai/orkas-awesome-agentskills/merch-research"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/merch-research/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-research"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/merch-research.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 | $0.01079 |
| Opus 5 | $0.00017 | $0.00540 |
| Sonnet 5 | $0.00007 | $0.00216 |
| Haiku 4.5 | $0.00003 | $0.00108 |
Grade A, and why
merch-research 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
商品选品研究
用于电商商品的前置研究和选品判断。核心任务是帮助用户判断“这个商品/类目是否值得继续验证”，并把数据来源、估算口径、风险和下一步验证说清楚。
何时使用
- 用户提供 Amazon keyword、category、ASIN、brand 或竞品材料，希望做类目机会、竞品、价格、评论痛点或选品可行性分析。
- 用户要研究淘宝、天猫、京东、拼多多、抖音、快手、1688、小红书等国内平台的商品机会。
- 用户要设计价格对比表、竞品字段、利润测算、采集计划或选品报告。
- 用户已有手动整理的数据、后台导出或合法 API 结果，希望形成经营判断。
不用于商品页面文案、主图/视频创意、评论 VOC 深度归因、下单采购、广告投放或自动平台抓取。
如何调用
- 先确认研究对象：商品、关键词、类目、平台、站点、价格带、目标市场、卖家能力、预算、供应链条件和已有数据。
- 判断路径：
- Amazon 市场/ASIN/类目研究：使用
references/amazon-market-research.md。 - 国内平台选品/价格/供应链研究：使用
references/china-platform-research.md。
- Amazon 市场/ASIN/类目研究：使用
- 明确数据来源：用户上传、平台后台导出、手动摘录、公开页面、授权 API、第三方数据源。来源不足时标为待验证。
- 不默认自动抓取平台；如用户要求采集，先说明平台条款、登录、频率、授权和反爬风险。
- 若使用 APIClaw，必须检查
APICLAW_API_KEY和额度；缺失时不请求数据，只说明需要凭证、用途和成本风险。 - 进行需求、竞争、价格带、评价壁垒、差异化、利润、供应链、合规和售后风险分析。
- 对每个结论标注证据类型：直接数据、推断、建议或待验证。使用
references/data-provenance-and-confidence.md。 - 输出报告结构时使用
references/product-research-output.md。
返回格式
- 调研目标和数据来源。
- 市场需求与价格带。
- 竞争格局、评价壁垒和差异化机会。
- 利润测算框架和供应链风险。
- 平台/类目合规与售后风险。
- 数据来源、置信标签和待验证项。
- 结论：推荐 / 观察 / 暂缓。
- 下一步验证计划。
外部依赖
- 无必需外部依赖。
- Amazon APIClaw 数据研究需要
APICLAW_API_KEY、账户额度和网络访问。 - Excel、CSV 或表格文件分析需要当前会话具备文件读取能力。
- 自动采集平台页面必须另行确认授权、登录状态、平台条款、采集频率和依赖。
限制与已知问题
- 不承诺销量、利润、排名、广告 ROAS 或经营结果。
- 不把平台展示销量、BSR、热度、评论数或第三方估算当作绝对事实。
- 不绕过登录、验证码、反爬、权限或付费 API 限制。
- 不替用户做最终采购、备货、投放或定价决策。
- 高风险类目如食品、保健、美妆、儿童、医疗器械、宠物食品和电器，需要更严格证据和人工复核。
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 · 60 lines · 35 tokens per session scan A c061dbe303ae
merch-research 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 1,079 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.
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