Company research
面向外贸出口场景的 Hermes / Open claw Agent Skills 包,支持海外客户发现、批量名单处理、公司背调、客户评分、决策层线索、开发信、跟进计划和报价单导出。
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Use when a trade agent needs to analyze a prospect company, website, business type, product line, purchasing possibility, red flags, decision-maker clues, or personalization evidence
SKILL.md
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Company Research
Overview
Turn prospect website content into evidence-backed sales intelligence. The core rule is to separate observed facts from inferred opportunities.
When to Use
Use this skill when the user provides a company name, website URL, prospect list row, LinkedIn snippet, trade-show exhibitor page, or distributor page and asks whether the company is worth developing.
Do not use it to fabricate contact names or emails.
Inputs
- Company name
- Website URL or pasted website content
- Tool output from
tools/batch_prospect_pipeline.pyortools/decision_maker_finder.pywhen a URL/list is provided - Product context from
product-loader - Market context from
MARKET.yaml
Outputs
{
"company_summary": "",
"business_type": "",
"main_products": [],
"target_customers": "",
"countries_served": [],
"evidence": [],
"evidence_status": "verified|no_evidence|fetch_failed",
"possible_needs": [],
"personalization_points": [],
"decision_maker_clues": [],
"contact_email": "",
"contact_phone": "",
"email_result": "found|没有",
"phone_result": "found|没有",
"red_flags": [],
"confidence": "low|medium|high"
}
Procedure
- If the user provides a URL or list file and no fetched content, run
tools/batch_prospect_pipeline.pyor the relevant crawler-backed tool first. Required command reference: run tools/batch_prospect_pipeline.py before scoring or email drafting. - Read the provided website content, browsing result, or
research_reports.jsonoutput. - Do not output company facts without fetched evidence. If fetching fails, return
evidence_status: "fetch_failed"and stop at review notes. - Extract observed facts: products, business type, brands, market focus, contact page, about page, and catalog clues.
- If the input is a company name or company link, resolve the official website when possible and run official website contact search across homepage, contact, about, team, catalog, and product pages.
- Record visible email and phone values as
contact_emailandcontact_phone; if either value is not found, writeemail_result: "没有"orphone_result: "没有"instead of guessing. - Record every fact with an evidence URL and short evidence text.
- Identify business type using evidence: importer, distributor, wholesaler, retailer, brand owner, manufacturer, contractor, marketplace seller, or unrelated site.
- Compare observed facts against the product context.
- Create
possible_needsonly when there is visible product, category, application, or channel overlap. - If fetched pages contain no relevant product or channel clue, return
evidence_status: "no_evidence"and keep confidence low. - Create personalization points from concrete website facts, not generic praise.
- Flag red flags: no business relevance, consumer-only content, inactive website, unverifiable contact, unrelated industry, or low-quality scraped directory.
- Return confidence based on evidence quantity and recency.
Verification
- Every personalization point has website evidence.
- Every observed fact includes an evidence URL.
- Official website contact search is run when a company name, website, or company link is available.
- Contact outputs include
contact_email,contact_phone,email_result, andphone_result. - Inferences are labeled as possible needs, not facts.
- No contact name is invented.
- Red flags are present when the website is weak or unrelated.
- Confidence is
lowwhen evidence is thin.
Common Mistakes
| Mistake | Fix |
|---|---|
| Calling every distributor a strong lead | Require product or channel overlap |
| Saying the company imports a product with no proof | Use "may need" and cite the clue |
| Writing generic personalization | Quote a concrete category, market, or service |
| Ignoring weak websites | Mark confidence and red flags clearly |