OpenClaw Cross-Border Product Research

Background

Cross-border product research often suffers from:

• Inconsistent scoring across researchers
• No clear exclusion rules for risky or non-compliant items
• Sources mixed without structure
• Outputs that are hard to compare over time

This use case builds a scan → score → filter pipeline with fixed dimensions.

Key steps

1. Define dimensions: market size, compliance, logistics cost, competition, margin potential.
2. Scan sources: product listings, reviews, regulatory feeds, competitor sites.
3. Apply scoring rules: standardized rubric per dimension.
4. Exclude risks: compliance, IP, supply-chain flags.
5. Output: ranked list plus reasons, with human review queue.

Workflow sketch

Input query → Fetch sources (Tavily) → Extract facts → Score by dimensions → Apply exclusion rules → Ranked output + risk flags

Skill recommendations

openclaw skills install @clawhub/tavily
openclaw skills install @clawhub/summarize

Expected results

• Products ranked by fixed dimensions
• Risk items filtered and flagged
• Structured output suitable for spreadsheets or reports
• Clear audit trail for each score

Common pitfalls

• Changing dimensions mid-project, making comparisons invalid
• Skipping exclusion rules for compliance-sensitive categories
• Over-relying on model output without source citations

Related pages

• Research report system

• Data cleaning pipeline

• Get started

• Official docs → docs.openclaw.ai

• Issues → GitHub Issues