sourcing-selection

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Sourcing Selection

货源选择判断

Follow shared release-shell rules in:

```
postplus-shared
```
release-shell rules

Use this skill when the user is not just asking for platform data, but for a real sourcing or product-selection judgment.

Typical requests:

这个产品值不值得找货源
适合先上 Amazon 还是 TikTok Shop
1688 上有货，但有没有需求侧证据支撑
帮我把供给侧和需求侧拼起来做判断
给我一个更接近真实决策的找货源结论

This skill is an orchestration and synthesis layer.

It should not replace platform skills. It should decide which evidence to collect, in what order, and how much judgment is justified.

Read first:

```
postplus-shared
```
product-selection preferences

遵循以下共享的发布框架规则：

```
postplus-shared
```
发布框架规则

当用户不仅询问平台数据，而是需要真实的货源采购或产品选品判断时，使用该Skill。

典型请求：

这个产品值不值得找货源
适合先上 Amazon 还是 TikTok Shop
1688 上有货，但有没有需求侧证据支撑
帮我把供给侧和需求侧拼起来做判断
给我一个更接近真实决策的找货源结论

该Skill是一个编排与整合层。

它不应替代平台类Skill。它需要决定收集哪些证据、收集顺序以及合理的判断程度。

请先阅读：

```
postplus-shared
```
产品选品偏好

Design Goal

设计目标

Keep the current version simple, but make the interface extensible.

The skill should think in capability groups, not hard-coded platforms:

```
supply-side source
```
```
search-intent source
```
```
demand-side source
```
```
content-language source
```
```
finance layer
```
```
compliance layer
```

Today, these groups may map to:

```
1688
```
for supply-side
```
Google Trends
```
for search-intent
```
Amazon
```
for search-led demand
```
TikTok Shop
```
for marketplace demand
```
TikTok
```
for content and audience language

In the future, the same groups may map to:

Alibaba

Made-in-China

GlobalSources

Temu supplier-side

, offline vendor lists

Google Trends

Baidu Index

, ad-library, search-console-like,

Shopee

Etsy

Temu

independent-site

Do not write the skill as if

1688 + Amazon + TikTok Shop

are permanent.

保持当前版本简洁，但需确保接口具备可扩展性。

该Skill应基于能力模块思考，而非硬编码平台：

```
supply-side source
```
（供给侧数据源）
```
search-intent source
```
（搜索意图数据源）
```
demand-side source
```
（需求侧数据源）
```
content-language source
```
（内容语言数据源）
```
finance layer
```
（财务层）
```
compliance layer
```
（合规层）

目前，这些模块对应的平台可能为：

供给侧：
```
1688
```
搜索意图：
```
Google Trends
```
搜索导向需求：
```
Amazon
```
marketplace需求：
```
TikTok Shop
```
内容与受众语言：
```
TikTok
```

未来，这些模块可能映射到：

供给侧：
```
Alibaba
```
、
```
Made-in-China
```
、
```
GlobalSources
```
、
```
Temu
```
供应商端、线下供应商清单
搜索意图：
```
Google Trends
```
、
```
Baidu Index
```
、广告库、类似搜索控制台的工具、
```
Shopee
```
、
```
Etsy
```
、
```
Temu
```
、独立站

不要将该Skill设计成仅适配

1688 + Amazon + TikTok Shop

的固定模式。

Core Rule

核心规则

A sourcing judgment is only as strong as its weakest missing layer.

Always separate:

```
Observed from evidence
```
```
Inference
```
```
Missing layer
```

If the user asks for a yes/no decision and the evidence is incomplete, return:

the provisional judgment
what supports it
what is still missing

Do not fake certainty.

货源采购判断的可靠性取决于最薄弱的缺失环节。

始终区分：

```
Observed from evidence
```
（从证据中观察到的事实）
```
Inference
```
（推论）
```
Missing layer
```
（缺失的环节）

如果用户要求做出是/否的决策但证据不完整，需返回：

临时判断结果
支撑该判断的依据
仍缺失的信息

不要伪造确定性结论。

Minimal Decision Model

极简决策模型

Use this order unless the user explicitly asks otherwise:

demand proof
competition shape
channel fit
merchant-model fit
supply-side feasibility
unit-economics pressure
compliance or operational risk

This is intentionally simple. Do not turn it into a giant scorecard unless the user asks.

除非用户明确要求，否则请按以下顺序进行判断：

需求验证
竞争格局
渠道适配性
商家模式适配性
供给侧可行性
单位经济压力
合规或运营风险

该模型故意设计得简洁。除非用户要求，否则不要将其扩展为复杂的评分卡。

Input Shapes

请求类型分类

Classify the request first:

首先对用户请求进行分类：

1. Product Idea Validation

1. 产品创意验证

Use when the user asks:

这个产品值不值得做
这个方向能不能找货源来卖

Default route:

collect demand-side proof
collect supply-side feasibility
synthesize

适用于用户提出以下请求时：

这个产品值不值得做
这个方向能不能找货源来卖

默认流程：

收集需求侧验证证据
收集供给侧可行性证据
整合分析

2. Supply-Led Opportunity Check

2. 供给导向机会核查

Use when the user already has a supply-side signal:

1688 上看到很多货
某工厂或类目看起来很便宜
已经有一批供应商候选

Default route:

inspect supply-side evidence
collect matching demand-side proof
test channel fit
synthesize

适用于用户已掌握供给侧信号时：

1688 上看到很多货
某工厂或类目看起来很便宜
已经有一批供应商候选

默认流程：

核查供给侧证据
收集匹配的需求侧验证证据
测试渠道适配性
整合分析

3. Demand-Led Sourcing Check

3. 需求导向货源核查

Use when the user already has a demand-side signal:

Amazon 上卖得不错
TikTok Shop 上很多人在卖
某类内容在 TikTok 很火

Default route:

inspect demand-side proof
collect supply-side feasibility
synthesize

适用于用户已掌握需求侧信号时：

Amazon 上卖得不错
TikTok Shop 上很多人在卖
某类内容在 TikTok 很火

默认流程：

核查需求侧验证证据
收集供给侧可行性证据
整合分析

4. Shortlist Comparison

4. 候选清单对比

Use when the user has:

several products
several niches
several supplier options

Default route:

normalize each candidate into the same decision frame
compare strongest evidence and biggest gaps
rank cautiously

适用于用户拥有以下内容时：

多款产品
多个细分领域
多个供应商选项

默认流程：

将每个候选对象标准化为同一决策框架
对比最有力的证据和最大的差距
谨慎排序

Capability Routing

能力路由

Choose sources by role, not by habit.

根据角色选择数据源，而非依赖习惯。

Supply-Side Source

供给侧数据源

Use for:

factory options
supplier variety
MOQ
tiered pricing
customization
location

Current preferred route:

```
skills/20-research/1688-research
```

用于：

工厂选项
供应商多样性
MOQ（最小起订量）
分级定价
定制化服务
地理位置

当前首选路径：

```
skills/20-research/1688-research
```

Demand-Side Source

需求侧数据源

Use for:

listings
pricing
reviews
order or ranking proof
bestseller shape
channel-native competition

Current preferred routes:

Amazon search-led demand ->
```
skills/20-research/amazon-research
```
TikTok Shop marketplace demand ->
```
skills/20-research/tiktok-shop-research
```

用于：

商品列表
定价
用户评价
订单或排名数据
畅销品格局
渠道原生竞争情况

当前首选路径：

Amazon搜索导向需求 ->
```
skills/20-research/amazon-research
```
TikTok Shop marketplace需求 ->
```
skills/20-research/tiktok-shop-research
```

Search-Intent Source

搜索意图数据源

Use for:

early demand signals
topic or keyword momentum
geo search interest
rising-query discovery

Current preferred route:

Google search-intent ->

skills/20-research/google-trends-research

Treat this as an early signal layer. Do not confuse it with transaction demand or channel-native competition proof.

用于：

早期需求信号
话题或关键词趋势
地域搜索兴趣
新兴查询发现

当前首选路径：

Google搜索意图 ->

skills/20-research/google-trends-research

将其视为早期信号层。不要将其与交易需求或渠道原生竞争证据混淆。

Content-Language Source

内容语言数据源

Use when content-led selling matters:

what hooks are working
what user language repeats
what visual demo style fits the product

Current preferred route:

```
skills/20-research/tiktok-research
```

If the request is broader than one named platform and the goal is to compare social proof or audience language across networks, route first through:

skills/10-routing/social-media-extractor

Use this layer only when it changes the decision. Do not force it into every sourcing task.

When social proof is cross-platform, do not let one familiar network stand in for the whole market. Use the extractor to decide which platform-specific research skill should collect first.

当内容驱动型销售至关重要时使用：

哪些钩子策略有效
用户重复使用的语言
适合产品的视觉演示风格

当前首选路径：

```
skills/20-research/tiktok-research
```

如果请求范围超出单个指定平台，且目标是跨网络对比社交证据或受众语言，请先通过以下路径路由：

skills/10-routing/social-media-extractor

仅当该层会影响决策时才使用。不要将其强制应用于所有货源采购任务。

当社交证据跨平台时，不要用一个熟悉的网络代表整个市场。使用提取器来决定应优先调用哪个平台专属的研究Skill进行数据收集。

Extensibility Rule

扩展性规则

When a new platform appears, do not rewrite the decision model.

Instead, map it into one of these roles:

```
supply-side
```
```
search-intent
```
```
demand-side
```
```
content-language
```
```
finance
```
```
compliance
```

Then state:

what role the source covers
what role is still missing

This keeps the skill stable while letting the source set expand.

当出现新平台时，不要重写决策模型。

相反，将其映射到以下角色之一：

```
supply-side
```
（供给侧）
```
search-intent
```
（搜索意图）
```
demand-side
```
（需求侧）
```
content-language
```
（内容语言）
```
finance
```
（财务）
```
compliance
```
（合规）

然后说明：

该数据源覆盖的角色
仍缺失的角色

这样可以保持Skill的稳定性，同时允许数据源集合扩展。

Good Output

优质输出

Return a compact decision memo with:

product or niche
target merchant model
target channel
observed evidence
provisional judgment
biggest risks
missing layer
recommended next step

Good recommendation shapes:

```
promising, but demand proof still thin
```

good Amazon search fit, weak TikTok demo fit

cheap supply exists, but competition is commodity-price-led

strong demand and workable sourcing, but returns or compliance may kill margin

If the result is going to move into execution, keep the handoff explicit:

sourcing judgment -> merchant or channel decision
merchant or channel decision -> research expansion, supplier outreach, or brief creation

Do not blur evidence collection, business judgment, and execution prep into one opaque step.

返回一份简洁的决策备忘录，包含：

产品或细分领域
目标商家模式
目标渠道
观察到的证据
临时判断结果
最大风险
缺失环节
建议下一步动作

优质建议示例：

前景可观，但需求验证证据仍不足

适配Amazon搜索场景，TikTok演示适配性较弱

存在低价货源，但竞争已陷入价格战

需求强劲且货源可行，但退货或合规问题可能吞噬利润

如果结果将进入执行阶段，需明确交接流程：

货源采购判断 -> 商家或渠道决策
商家或渠道决策 -> 研究扩展、供应商接洽或任务简报制定

不要将证据收集、商业判断和执行准备模糊为一个不透明的步骤。

Failure Modes To Avoid

需避免的错误模式

Do not:

treat one platform's popularity as universal demand proof
treat cheap 1688 supply as a recommendation by itself
jump from TikTok content heat to Amazon launch logic without search proof
jump from Amazon demand to TikTok Shop without content-demo fit
hide missing finance or compliance layers

请勿：

将单一平台的热度视为通用需求验证证据
将1688上的低价货源直接作为推荐依据
在没有搜索验证的情况下，从TikTok内容热度直接跳到Amazon上线逻辑
在没有内容演示适配性的情况下，从Amazon需求直接跳到TikTok Shop
隐瞒缺失的财务或合规环节

Current Workspace Default

当前工作区默认设置

At the moment, this skill should usually compose existing skills rather than create a brand-new collection workflow.

Current building blocks:

supply-side:
```
skills/20-research/1688-research
```

search-intent:

skills/20-research/google-trends-research

search-led demand:
```
skills/20-research/amazon-research
```
marketplace demand:
```
skills/20-research/tiktok-shop-research
```
content-language fit:
```
skills/20-research/tiktok-research
```

Future sources should be slotted into the same roles.

目前，该Skill通常应组合现有Skill，而非创建全新的数据收集流程。

当前构建模块：

供给侧：
```
skills/20-research/1688-research
```

搜索意图：

skills/20-research/google-trends-research

搜索导向需求：
```
skills/20-research/amazon-research
```
marketplace需求：
```
skills/20-research/tiktok-shop-research
```
内容语言适配性：
```
skills/20-research/tiktok-research
```

未来的数据源应归入相同的角色模块。