Andrew Yim
ba88247496
SKILL.md orchestration (8 gated stages), references (rate_card.yaml, findings_to_service rubric, competitor sets), findings.schema.json contract, and scripts: kg_query.py (generalized KG examination), estimate.py (findings→rate-card 견적 md/xlsx/json), build_deck.py (9-slide branded PPTX), render_pdf.sh (Korean PDF via headless Chrome), plus client_brief.html template. Validated on Sono Hotels & Resorts findings: estimate OD-2026-001 (23-47M KRW) and a 9-slide deck generated cleanly. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
24 lines
1.0 KiB
Markdown
24 lines
1.0 KiB
Markdown
# Default competitor benchmark sets (Korean market)
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Used by Stage 3 (KG/entity) when the user doesn't supply competitors. Pick the set
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matching the prospect's vertical; pass as `--competitors` to `kg_query.py`.
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## hotel_resort (호텔·리조트)
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- 롯데호텔 (Lotte Hotel) — strong KG, LodgingBusiness type, Korean Wikipedia
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- 신라호텔 / 호텔신라 (Shilla)
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- 조선호텔앤리조트 (Josun)
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- 한화리조트 / 한화호텔앤드리조트 (Hanwha)
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- 켄싱턴리조트 (Kensington)
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## city_hotel (시티 호텔)
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- 롯데호텔, 신라호텔, 조선호텔, 글래드호텔, 나인트리
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## condo_membership (콘도·회원권)
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- 한화리조트, 대명(소노), 한솔오크밸리, 금호리조트
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## benchmark_signals
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For each competitor record in `findings.json.entity.competitor_benchmark`:
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`{name, score (KG result_score), type (@type), wikipedia (bool)}`.
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The benchmark table contrasts the prospect's entity strength/type/Wikipedia
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presence against these — the core competitive-gap visual in the deck.
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