sourcing-selection

name	sourcing-selection
description	Synthesize supply-side and demand-side evidence into a practical sourcing judgment for a product, niche, or shortlist without collapsing platform data collection and business judgment into one step.
metadata	{"postplus":{"familyId":"marketplace-sourcing","familyName":"Marketplace, Sourcing, and Growth"}}

Sourcing Selection

Use When

• The user wants a sourcing or product-selection judgment, not just raw platform data.
• Supply-side evidence and demand-side evidence need to be combined.
• A product idea, supplier signal, channel opportunity, or shortlist needs a cautious go/no-go or next-step memo.

Do Not Use When

• Do not replace platform collection skills.
• Do not claim hosted supplier collection from this released skill surface.
• Do not turn incomplete evidence into a confident sourcing recommendation.

Core Rule

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

Always separate:

• observed evidence
• inference
• missing layer

If evidence is incomplete, return a provisional judgment, supporting signals, and the missing layer instead of fake certainty.

Minimal Decision Model

Use this order unless the user asks otherwise:

1. demand proof
2. competition shape
3. channel fit
4. merchant-model fit
5. supply-side feasibility
6. unit-economics pressure
7. compliance or operational risk

Input Shapes

• Product idea validation: demand proof, supply feasibility, then synthesis.
• Supply-led opportunity check: supplier or cheap-supply signal first, then matching demand proof and channel fit.
• Demand-led sourcing check: marketplace, search, or content demand first, then supply feasibility.
• Shortlist comparison: normalize candidates into one decision frame, compare strongest evidence and biggest gaps, then rank cautiously.

Capability Routing

• Supply-side source: user-provided supplier sheets, quotations, or approved marketplace data. Do not imply a released hosted 1688 collector.
• Search-intent source: use google-trends-research for early momentum and rising-query signals.
• Search-led demand: use google-trends-research for rising-query signals and user-provided marketplace exports for listings, reviews, price bands, and channel-native competition.
• Content-language fit: use tiktok-research when demos, hooks, audience language, or content-led selling matter.
• Instagram/Meta social proof: use social-media-extractor before choosing the platform-specific collector.

Output Shape

The artifact packages the decision memo as JSON with product or niche, target channel, decision, demand signals, supply signals, rationale, missing layers, and recommended next step when provided.

Stop Conditions

• Stop when required user intent, source evidence, or owned input artifacts are missing and guessing would change the result.
• If an owned CLI or script command fails, report the exact error and stop. Do not bypass the failure with metadata-only answers, readiness probing, local payload rewrites, fallback providers, or unpublished tools.

Handoff

• Evidence missing -> route to the right collection skill first.
• Memo complete -> hand decision.json to research expansion, supplier outreach, channel decision, or brief creation.

Public Command Boundary

• Choose the smallest matching command or workflow from the user input and run it directly.
• If an owned CLI or script command fails, report the exact error and stop. Do not bypass the failure with metadata-only answers, readiness probing, local payload rewrites, fallback providers, or unpublished tools.
• This public skill is instruction-driven. Produce the artifact described by the workflow directly from the available evidence.
• Do not call private provider/runtime paths or unpublished local tools.
• If the CLI returns a quote-confirmation challenge, run postplus quote confirm --json --challenge-file <challenge.json> and retry with the returned token.