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Company research

Skill Xuxchloris/export_skills/skills/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。

Install
npx -y skills add Xuxchloris/export_skills --skill company-research

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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.py or tools/decision_maker_finder.py when 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

  1. If the user provides a URL or list file and no fetched content, run tools/batch_prospect_pipeline.py or the relevant crawler-backed tool first. Required command reference: run tools/batch_prospect_pipeline.py before scoring or email drafting.
  2. Read the provided website content, browsing result, or research_reports.json output.
  3. Do not output company facts without fetched evidence. If fetching fails, return evidence_status: "fetch_failed" and stop at review notes.
  4. Extract observed facts: products, business type, brands, market focus, contact page, about page, and catalog clues.
  5. 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.
  6. Record visible email and phone values as contact_email and contact_phone; if either value is not found, write email_result: "没有" or phone_result: "没有" instead of guessing.
  7. Record every fact with an evidence URL and short evidence text.
  8. Identify business type using evidence: importer, distributor, wholesaler, retailer, brand owner, manufacturer, contractor, marketplace seller, or unrelated site.
  9. Compare observed facts against the product context.
  10. Create possible_needs only when there is visible product, category, application, or channel overlap.
  11. If fetched pages contain no relevant product or channel clue, return evidence_status: "no_evidence" and keep confidence low.
  12. Create personalization points from concrete website facts, not generic praise.
  13. Flag red flags: no business relevance, consumer-only content, inactive website, unverifiable contact, unrelated industry, or low-quality scraped directory.
  14. 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, and phone_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 low when evidence is thin.

Common Mistakes

MistakeFix
Calling every distributor a strong leadRequire product or channel overlap
Saying the company imports a product with no proofUse "may need" and cite the clue
Writing generic personalizationQuote a concrete category, market, or service
Ignoring weak websitesMark confidence and red flags clearly

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.