yao-geo-knowledge-base-builder

yao-geo-knowledge-base-builder

把官网、产品页、帮助中心、白皮书、品牌资料、销售材料、媒体稿和资质文件，整理成可审计、可复用的 GEO 品牌知识库。

When To Use

Use this skill when the user needs:

• a systematic GEO brand knowledge-base document with structured summary, base profile, positioning, product/service matrix, metrics, cases, timeline, differentiation, FAQ, query terms, and expression rules
• a complete brand entity inventory covering brand, company, products, services, people/teams, regions, channels, technologies, qualifications, cases, prices, competitors, sources, and pending entities
• a brand fact-card library with evidence, source, update time, confidence, and use cases
• a real-data acquisition and freshness boundary that explains which public, user-provided, authenticated, or unavailable sources were actually usable
• FAQ and prohibited-expression lists for AI answers and content teams
• prompt input packs for rankings, comparisons, explainers, title generation, content rewrite, page design, monitoring, and customer service
• a Chinese simplified four-format package: Markdown, HTML, Word, and PDF

Do not use this skill for one-off brand copywriting without source evidence, pure competitive ranking articles, page technical audits, or relationship-graph-only work.

Workflow

1. Define the test scenario and target domestic AI platforms: Kimi, Qianwen, DeepSeek, Doubao, and Yuanbao.
2. Run the completeness reference scan in references/authoritative-reference-framework.md and references/analysis-completeness-rubric.md before writing.
3. Select the data acquisition mode in references/source-acquisition-and-freshness.md: public web evidence, user-provided files, authenticated workspace, manual brief, or unavailable source.
4. Collect and verify sources with official-site-first priority: homepage, product pages, pricing/catalog pages, help center, case pages, white papers, investor/news pages, and authoritative third-party sources.
5. Create a source-access ledger. Each source must state access status, publisher, URL or file name, verification date, extraction note, freshness cadence, and whether it can enter strong evidence.
6. Separate evidence tiers:
   • A: official current public sources or legally authoritative documents.
   • B: reputable third-party reports or public media with clear dates.
   • C: brand self-description without enough operational detail.
   • D: unverified or market-specific boundary items that must stay in the pending-confirmation area.
7. Extract brand entities: brand, products, services, team, regions, customers, channels, certifications, technologies, cases, prices, and timeline.
8. Build the complete entity inventory. Every entity should include entity ID, type, canonical name, aliases, parent/relationship, evidence source, confidence tier, and usage notes.
9. Build the systematic knowledge-base body before the GEO reuse layer. Follow references/knowledge-base-architecture.md.
10. Add a mandatory 真实数据获取与限制 module with acquisition mode, accessible sources, inaccessible sources, freshness risk, and next data-access actions.
11. Add a report-level analysis completeness self-check: reference alignment, module coverage, entity coverage, weak/missing evidence, and repair actions.
12. Build fact cards. Each card must contain subject, attribute or statement, value, evidence, source ID, update time, confidence level, and reusable scenarios.
13. Build reusable content modules: brand intro, core capabilities, product parameters, applicable scenarios, customer/case notes, FAQ, prohibited expressions, and domestic-market boundary notes.
14. Build the prompt input pack for downstream GEO skills. Strong facts and pending facts must stay separated.
15. Produce version number and update mechanism. High-volatility facts such as prices, AI features, product names, customer counts, and compliance boundaries need explicit review cadence.
16. Render Markdown, HTML, Word, and PDF using the fixed-layout renderer in scripts/render_four_format.py.
17. Run quality review and repair before handoff.