a65adcc2e5
Sources: extracted from two upstream archives (skill-repo, skills-main),
merged with the following policy:
- 15 broken symlinks (pointing to /Users/jameslee/.cc-switch/skills or
../../.agents/skills on a foreign machine) discarded
- 3 real name collisions with identical content (ai-pair, ifind-http-api,
zhipu-websearch) kept as one copy
- Functional overlaps deduped keeping the strongest variant:
- docx family: kept docx (official, full toolchain) + docx-cn
(GB/T 9704 Chinese official-document constants),
dropped docx_writer (no scripts, name collided with docx)
- humanizer family: kept humanizer-zh (6 zh reference docs),
dropped humanizer (en, redundant for CN workflow)
- Skills that only ran in a foreign environment removed:
ablemind-ops, app-publish, hlb-design-system, openclaw-adj-skill,
claude-driver
- alphapai excluded from this public repo because its SKILL.md hard-coded
live credentials
Result: 25 skills, 572 files, ~7.5 MB.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
295 lines
9.4 KiB
Python
295 lines
9.4 KiB
Python
"""主题配置 — 配色、字体、间距、结构几何"""
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# ── 新增 tokens 的默认值（供旧主题/自定义主题 fallback） ──────────────
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_LAYOUT_DEFAULTS = {
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# 字号层级
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"cover_title_size": 36,
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"page_title_size": 16,
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"chart_subtitle_size": 11,
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"footer_size": 8,
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# 幻灯片尺寸 (inches)
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"slide_w": 13.333, # 16:9 default
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"slide_h": 7.5,
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# 结构几何 (inches)
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"header_y": 0.25,
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"divider_y": 0.80,
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"divider_h": 0.015,
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"content_y": 1.00,
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"content_h": 5.80,
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"footer_y": 7.10,
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# content_w 由 _theme() 自动计算: slide_w - 2 * margin
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# 表格样式
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"table_header_bg": "1F4E79",
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"table_zebra_even": "F8FAFC",
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"table_zebra_odd": "FFFFFF",
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# 图表默认
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"chart_default_scheme": "default",
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}
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def _theme(base: dict) -> dict:
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"""合并 layout defaults，保证每个 theme 都有完整 tokens"""
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merged = {**_LAYOUT_DEFAULTS, **base}
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# table_header_bg 默认跟随 primary
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if "table_header_bg" not in base:
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merged["table_header_bg"] = base.get("primary", _LAYOUT_DEFAULTS["table_header_bg"])
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# content_w = slide_w - 2 * margin（自动派生）
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if "content_w" not in base:
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merged["content_w"] = merged["slide_w"] - 2 * merged.get("margin", 0.6)
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return merged
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THEMES = {
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"midnight": _theme({
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"name": "Midnight Executive",
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"primary": "1E2761",
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"secondary": "CADCFC",
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"accent": "E8B931",
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"bg_dark": "1E2761",
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"bg_light": "F5F7FA",
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"card_bg": "FFFFFF",
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"text_dark": "1E2761",
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"text_light": "FFFFFF",
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"text_muted": "8896AB",
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"border": "E2E8F0",
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"positive": "10B981",
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"negative": "EF4444",
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"header_font": "微软雅黑",
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"body_font": "微软雅黑",
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"margin": 0.6,
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"title_size": 28,
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"subtitle_size": 18,
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"body_size": 14,
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"caption_size": 10,
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"kpi_size": 44,
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"chart_default_scheme": "midnight",
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}),
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"charcoal": _theme({
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"name": "Charcoal Minimal",
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"primary": "36454F",
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"secondary": "F2F2F2",
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"accent": "E8B931",
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"bg_dark": "36454F",
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"bg_light": "F8F8F8",
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"card_bg": "FFFFFF",
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"text_dark": "212121",
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"text_light": "FFFFFF",
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"text_muted": "808080",
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"border": "E0E0E0",
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"positive": "10B981",
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"negative": "EF4444",
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"header_font": "微软雅黑",
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"body_font": "微软雅黑",
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"margin": 0.6,
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"title_size": 28,
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"subtitle_size": 18,
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"body_size": 14,
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"caption_size": 10,
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"kpi_size": 44,
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"chart_default_scheme": "charcoal",
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}),
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# ── 新主题（设计源自投行级研报分析 + 行业报告研究） ──────────────
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"jp_finance": _theme({
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"name": "IB Finance",
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"primary": "1B3D6E",
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"secondary": "00A5BD",
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"accent": "C9A84C",
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"bg_dark": "0F2340",
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"bg_light": "F4F6FA",
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"card_bg": "FFFFFF",
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"text_dark": "1A2744",
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"text_light": "FFFFFF",
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"text_muted": "6B7C93",
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"border": "C8D6E5",
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"positive": "0B7B3E",
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"negative": "C0392B",
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"header_font": "微软雅黑",
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"body_font": "微软雅黑",
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"margin": 0.6,
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"title_size": 28,
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"subtitle_size": 18,
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"body_size": 12,
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"caption_size": 9,
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"kpi_size": 44,
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# IB-specific overrides
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"table_header_bg": "1B3D6E",
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"chart_default_scheme": "jp_finance",
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}),
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"pension_warm": _theme({
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"name": "Pension Warm",
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"primary": "2E5FA3",
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"secondary": "7BA7BC",
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"accent": "D4903F",
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"bg_dark": "1C3557",
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"bg_light": "F7F4F0",
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"card_bg": "FDFAF7",
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"text_dark": "2C3E50",
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"text_light": "FAFAFA",
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"text_muted": "7F8C8D",
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"border": "D5C9B8",
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"positive": "27AE60",
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"negative": "E74C3C",
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"header_font": "微软雅黑",
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"body_font": "微软雅黑",
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"margin": 0.6,
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"title_size": 28,
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"subtitle_size": 18,
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"body_size": 12,
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"caption_size": 9,
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"kpi_size": 44,
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"chart_default_scheme": "pension_warm",
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}),
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"tech_blue": _theme({
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"name": "Tech Blue",
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"primary": "1565C0",
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"secondary": "29B6F6",
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"accent": "00BFA5",
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"bg_dark": "0A1628",
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"bg_light": "F0F4FF",
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"card_bg": "FFFFFF",
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"text_dark": "102040",
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"text_light": "E8F1FF",
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"text_muted": "7A8CA8",
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"border": "C0D0E8",
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"positive": "00C896",
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"negative": "FF4B5C",
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"header_font": "微软雅黑",
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"body_font": "微软雅黑",
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"margin": 0.6,
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"title_size": 28,
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"subtitle_size": 18,
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"body_size": 12,
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"caption_size": 9,
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"kpi_size": 44,
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"chart_default_scheme": "tech_blue",
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}),
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"state_red": _theme({
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"name": "State Red",
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"primary": "8B0000",
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"secondary": "B8860B",
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"accent": "DAA520",
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"bg_dark": "5C0000",
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"bg_light": "FDF8F0",
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"card_bg": "FFFFFF",
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"text_dark": "2C1810",
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"text_light": "FFF8E7",
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"text_muted": "8C7B6B",
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"border": "E0CFAA",
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"positive": "2E6B30",
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"negative": "8B0000",
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"header_font": "微软雅黑",
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"body_font": "微软雅黑",
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"margin": 0.6,
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"title_size": 28,
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"subtitle_size": 18,
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"body_size": 12,
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"caption_size": 9,
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"kpi_size": 44,
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"chart_default_scheme": "state_red",
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}),
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"esg_green": _theme({
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"name": "ESG Green",
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"primary": "1A5C2A",
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"secondary": "4CAF50",
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"accent": "8BC34A",
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"bg_dark": "0D3318",
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"bg_light": "F1F8F2",
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"card_bg": "FFFFFF",
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"text_dark": "1B2B1C",
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"text_light": "E8F5E9",
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"text_muted": "5C7A5E",
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"border": "B8D8BA",
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"positive": "2E7D32",
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"negative": "C62828",
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"header_font": "微软雅黑",
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"body_font": "微软雅黑",
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"margin": 0.6,
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"title_size": 28,
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"subtitle_size": 18,
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"body_size": 12,
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"caption_size": 9,
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"kpi_size": 44,
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"chart_default_scheme": "esg_green",
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}),
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# ── 研究级主题（Morningstar / 宏观研究风格） ──────────────────────
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"morningstar": _theme({
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"name": "Morningstar Research",
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"primary": "1D2B3A", # 深石板灰
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"secondary": "5A9BD5", # 中蓝
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"accent": "E67E22", # 暖橙
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"bg_dark": "1A1A2E", "bg_light": "FAFBFC", "card_bg": "FFFFFF",
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"text_dark": "2C3E50", "text_light": "F8F9FA", "text_muted": "7B8794",
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"border": "DEE2E6", "positive": "28A745", "negative": "DC3545",
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"header_font": "微软雅黑", "body_font": "微软雅黑",
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"margin": 0.6, "title_size": 28, "subtitle_size": 18,
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"body_size": 11, "caption_size": 9, "kpi_size": 36,
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"chart_default_scheme": "morningstar",
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}),
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"macro_research": _theme({
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"name": "Macro Research",
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"primary": "2C3E50", # 深藏蓝灰
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"secondary": "3498DB", # 净蓝
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"accent": "95A5A6", # 中性灰（低调）
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"bg_dark": "1A252F", "bg_light": "F7F9FB", "card_bg": "FFFFFF",
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"text_dark": "2C3E50", "text_light": "ECF0F1", "text_muted": "95A5A6",
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"border": "D5D8DC", "positive": "27AE60", "negative": "E74C3C",
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"header_font": "微软雅黑", "body_font": "微软雅黑",
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"margin": 0.6, "title_size": 28, "subtitle_size": 18,
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"body_size": 11, "caption_size": 9, "kpi_size": 36,
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"chart_default_scheme": "macro_research",
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}),
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"dark_pro": _theme({
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"name": "Dark Pro",
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"primary": "00BFFF",
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"secondary": "7B68EE",
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"accent": "FFD700",
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"bg_dark": "080808",
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"bg_light": "141820",
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"card_bg": "1E2430",
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"text_dark": "E0E8F0",
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"text_light": "FFFFFF",
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"text_muted": "6880A0",
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"border": "2A3848",
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"positive": "00E676",
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"negative": "FF1744",
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"header_font": "微软雅黑",
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"body_font": "微软雅黑",
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"margin": 0.6,
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"title_size": 30,
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"subtitle_size": 20,
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"body_size": 13,
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"caption_size": 10,
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"kpi_size": 48,
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"chart_default_scheme": "dark_pro",
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}),
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}
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DEFAULT_THEME = THEMES["midnight"]
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# ── 比例预设 ──────────────────────────────────────────────────────────
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ASPECT_4_3 = {"slide_w": 10.0, "slide_h": 7.5}
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ASPECT_16_9 = {"slide_w": 13.333, "slide_h": 7.5}
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def resolve_theme(theme, aspect_ratio=None) -> dict:
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"""解析主题参数，确保所有 tokens 都有值
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Args:
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theme: 主题名称(str) 或主题字典(dict) 或 None
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aspect_ratio: 可选比例 "4:3" 或 "16:9"
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Returns:
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完整的主题字典（包含所有 layout tokens）
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"""
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if isinstance(theme, str):
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result = dict(THEMES.get(theme, DEFAULT_THEME))
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elif isinstance(theme, dict):
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result = {**DEFAULT_THEME, **theme}
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else:
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result = dict(DEFAULT_THEME)
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if aspect_ratio == "4:3":
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result.update(ASPECT_4_3)
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result["content_w"] = result["slide_w"] - 2 * result.get("margin", 0.6)
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return result
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