Trade Customer Intel

Overview

Use this skill to turn sparse lead data into a structured public-web due-diligence report for sales development. This is the core judgment layer of the whole outreach chain: customer search finds candidates, lead screening cleans them, but this Skill decides what is actually worth saying before the email Skill drafts anything.

角色定位：

• 客户情报分析员
• 负责基于公开网页证据完成客户背调与风险判断
• 负责识别近期客户动态、市场变化、合规/关税/贸易环境信号
• 负责给开发信 Skill 提供清晰、可引用、可复核的销售切入点
• 不负责批量搜客户，也不负责替代人工直接外发邮件
• 必须明确是否允许进入开发信阶段

上下游关系：

• 上游：线索整理skill/ 输出的 customer_intel_input，或人工提供的稀疏 lead 信息
• 下游：开发信skill/

当前最稳的是公司级背调主线。联系人、职位和个性化信号只作辅助。

Workflow

1. Normalize input into the standard shape:

{
  "company_name": "",
  "person_name": "",
  "email": "",
  "company_website": "",
  "country_or_market": "",
  "product_or_offer": "",
  "industry_lens": "auto",
  "seller_context": {
    "company_name": "",
    "product_or_offer": "",
    "product_categories": [],
    "target_customer_types": [],
    "target_industries": [],
    "value_propositions": [],
    "proof_points": [],
    "authorized_materials": [],
    "forbidden_claims": []
  },
  "notes": ""
}

2. If only an email is present, extract the email domain and treat it as the first company clue.
3. Search in this order:
   • Official website and domain evidence
   • LinkedIn company page and personal profile
   • Facebook and Instagram
   • X/Twitter and YouTube
   • General web search and news
4. Extract recent and market signals:
   • Recent customer signals: LinkedIn updates, social posts, news, hiring, funding, expansion, new warehouse, channel changes, product launches.
   • Market signals: target-market regulation, certification, tariff, trade agreement, compliance, import/export rule changes.
   • Every signal must keep source, time or period when visible, freshness, confidence, and product relevance.
5. Resolve identities conservatively:
   • Prefer direct identifiers over inference.
   • Do not merge ambiguous people or companies unless multiple signals line up.
   • Mark weak conclusions as inference, not fact.
6. Convert raw sources into an evidence_ledger; every item receives an EV-* ID and source-quality label.
7. Convert facts, inferences, and hypotheses into a claim_ledger; every claim receives a CL-* ID and references its supporting evidence.
8. Apply the selected industry lens. Industrial accounts emphasize BOM and component clues; food accounts emphasize SKU, channel, certification, packaging, and cold-chain clues.
9. Run identity, evidence, seller-offer, product-fit, and risk decision gates.
10. Assemble the SIEGER v2 report using report-template.md.
11. Score only when at least three verdict dimensions have evidence-backed ratings; otherwise return score = null and customer_grade = 未分级.
12. Generate sales angles as proposed; this Skill must never approve its own angle.

Main Script

Use build_customer_intel_report.py as the default entrypoint.

Contracts:

• customer-intel-input.schema.json
• customer-intel-report.schema.json

Example run

python3 ./scripts/build_customer_intel_report.py --input-json /path/to/input.json --markdown-out /tmp/customer-intel.md --json-out /tmp/customer-intel.json

Or pipe JSON directly:

cat <<'EOF' | python3 ./scripts/build_customer_intel_report.py
{
  "company_name": "Acme Industrial",
  "person_name": "Jane Smith",
  "email": "jane@acme-industrial.com",
  "country_or_market": "United States"
}
EOF

Script behavior

• If tvly is installed, the script uses it first for web search.
• If tvly is not installed, the script falls back to DuckDuckGo HTML search.
• If evidence is sparse, the script still produces a report and explicitly flags low confidence.
• If one or more providers fail, preserve the errors in collection_errors; do not convert a collection outage into a successful intel decision.
• Reject malformed identity or nested context types instead of coercing them to strings.
• Create missing parent directories for requested JSON and Markdown outputs.

Output Requirements

• Keep the report structured and CRM-friendly.
• Keep core analysis in Chinese.
• Include English wording in the Executive Summary and Sales Angles sections.
• Attach source URLs to every material claim when possible.
• Use Low, Medium, or High risk ratings only.
• Include IntelDecision with evidence sufficiency and next action.
• Include decision_brief, evidence_ledger, claim_ledger, and decision_gates.
• Every factual claim must reference at least one evidence_id.
• Preserve seller_context and the resolved industry_lens in the report.
• Include the SIEGER Verdict Card, but never fabricate a score to make the report look complete.
• Include recent_signals and market_signals where evidence supports them.
• Include sales angles with angle_id, claim/evidence references, authorized-material references, and approval_status = proposed.
• Apply BOM, motor/drive, ASRS, or engineering-fit language only when the resolved industry_lens is industrial and the selected claims plus seller offer support the concept; never leak that copy into food, consumer, or general reports.
• If the person match is weak, say so explicitly instead of inventing a firm personal profile.
• Keep outreach personalization conservative. Do not invent private preferences or present weak inferences as facts.
• Do not replace 客户搜索skill/ or 线索整理skill/ as the lead-entry stage.
• Do not imply the final outreach draft can skip human review.
• Do not imply stable support for precise personal email discovery.

Openclaw Integration Notes

• Missing fields are allowed; the report header must list what was missing.
• Only use public internet sources. Do not imply private-data access.

References

• Read report-template.md before changing the output shape.
• Read source-playbook.md before changing search order, confidence rules, or risk scoring.

Defaults

• Standard depth, not exhaustive crawling.
• Public web only.
• Conservative entity matching.
• Conservative risk scoring.
• This is the flagship judgment layer in the outreach chain; email drafting should depend on this output instead of inventing recent events.
• The email bridge may only consume an angle after a human or authorized workflow changes its status to approved.
• Default intel-stage action: ready_for_email_draft | hold_for_manual_review

Table and Rule Capture

• 本 Skill 输出客户背调和风险判断能力，不要求企业使用固定背调表模板。
• 写入企业表格或知识库时，先沿用已有表头和文档归口，再映射主体可信度、证据充分度、近期信号、市场信号、风险评级、推荐销售角度、推荐下一步、报告引用等标准字段。
• 用户没有可用表格时，龙虾可以按企业产品、市场和背调流程新建够用表。
• 当用户确认新的背调规则、风险分级、近期信号判断、市场信号来源、证据来源权重、行业习惯或表头映射后，必须追问：是否更新到对应 Skill 以便下次自动复用。真实写入必须得到用户授权。

Enhancement Entry

如需数据留存、统一编排、多代理协作或飞书落地，请查看飞书文档：

• https://evenbetter.feishu.cn/wiki/W6GnwTZGFiUdJ0kXZv6cV4PSnpf