Full upload: MCNVideo AI project with .workbuddy
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#!/usr/bin/env python3
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"""
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合并6个Agent的调研结果，生成Phase 1.5调研Review检查点的摘要表格。
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扫描 references/research/ 目录下的01-06 md文件，统计每个维度的来源数量、
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一手/二手占比、关键发现。
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用法:
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python3 merge_research.py <skill目录路径>
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示例:
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python3 merge_research.py .claude/skills/elon-musk-perspective
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输出: 打印markdown格式的摘要表格到stdout
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"""
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import sys
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import re
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from pathlib import Path
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AGENTS = {
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'01-writings': '著作',
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'02-conversations': '对话',
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'03-expression-dna': '表达',
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'04-external-views': '他者',
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'05-decisions': '决策',
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'06-timeline': '时间线',
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}
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def count_sources(content: str) -> dict:
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"""统计来源数量和一手/二手占比"""
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# 计算URL数量作为来源数
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urls = re.findall(r'https?://[^\s\)]+', content)
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# 检测一手/二手标记
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primary_markers = len(re.findall(r'一手|primary|本人|原文|原始|直接引用', content, re.IGNORECASE))
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secondary_markers = len(re.findall(r'二手|secondary|转述|总结|评论|分析', content, re.IGNORECASE))
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return {
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'url_count': len(urls),
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'unique_urls': len(set(urls)),
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'primary_markers': primary_markers,
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'secondary_markers': secondary_markers,
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}
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def extract_key_findings(content: str, max_items: int = 3) -> list[str]:
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"""提取关键发现（取前几个二级标题或加粗项）"""
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# 尝试提取##标题
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headings = re.findall(r'^##\s+(.+)$', content, re.MULTILINE)
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if headings:
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return headings[:max_items]
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# fallback: 提取加粗项
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bolds = re.findall(r'\*\*(.+?)\*\*', content)
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if bolds:
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return bolds[:max_items]
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# fallback: 取前3个非空行
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lines = [l.strip() for l in content.split('\n') if l.strip() and not l.startswith('#')]
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return [l[:50] + '...' if len(l) > 50 else l for l in lines[:max_items]]
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def find_contradictions(files: dict[str, str]) -> list[str]:
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"""简单检测跨文件矛盾（同一关键词出现不同判断）"""
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contradictions = []
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# 检测「但是」「然而」「相反」「矛盾」等矛盾标记
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for name, content in files.items():
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matches = re.findall(r'(?:矛盾|相反|但实际上|然而.*?不同|争议).{0,100}', content)
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for m in matches:
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contradictions.append(f"{AGENTS.get(name, name)}: {m[:80]}")
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return contradictions[:5] # 最多5条
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def main():
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if len(sys.argv) < 2:
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print("用法: python3 merge_research.py <skill目录路径>")
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sys.exit(1)
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skill_dir = Path(sys.argv[1])
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research_dir = skill_dir / 'references' / 'research'
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if not research_dir.exists():
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print(f"❌ 目录不存在: {research_dir}")
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sys.exit(1)
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files = {}
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rows = []
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total_sources = 0
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total_primary = 0
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total_secondary = 0
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missing = []
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for key, label in AGENTS.items():
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md_file = research_dir / f"{key}.md"
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if not md_file.exists():
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missing.append(label)
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rows.append(f"│ {label:<12} │ {'❌ 缺失':<8} │ {'—':<24} │")
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continue
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content = md_file.read_text(encoding='utf-8')
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files[key] = content
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stats = count_sources(content)
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findings = extract_key_findings(content)
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total_sources += stats['unique_urls']
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total_primary += stats['primary_markers']
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total_secondary += stats['secondary_markers']
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findings_str = ', '.join(findings) if findings else '—'
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if len(findings_str) > 40:
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findings_str = findings_str[:37] + '...'
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rows.append(f"│ {label:<12} │ {stats['unique_urls']:<8} │ {findings_str:<24} │")
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# 矛盾检测
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contradictions = find_contradictions(files)
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# 输出
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print("┌──────────────┬──────────┬──────────────────────────┐")
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print("│ Agent │ 来源数量 │ 关键发现 │")
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print("├──────────────┼──────────┼──────────────────────────┤")
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for row in rows:
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print(row)
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print("├──────────────┼──────────┼──────────────────────────┤")
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primary_ratio = f"{total_primary}/{total_primary + total_secondary}" if (total_primary + total_secondary) > 0 else "未标记"
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print(f"│ 总来源数 │ {total_sources:<8} │ 一手占比: {primary_ratio:<15} │")
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if contradictions:
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print(f"│ 矛盾点 │ {len(contradictions)}处 │ {contradictions[0][:24]:<24} │")
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else:
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print(f"│ 矛盾点 │ 0处 │ {'—':<24} │")
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if missing:
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print(f"│ 信息不足维度 │ {len(missing)}个 │ {', '.join(missing):<24} │")
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else:
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print(f"│ 信息不足维度 │ 无 │ {'—':<24} │")
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print("└──────────────┴──────────┴──────────────────────────┘")
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# 总结
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if total_sources < 10:
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print("\n⚠️ 总来源数 <10，建议降低期望或补充调研")
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if missing:
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print(f"\n⚠️ 缺失维度: {', '.join(missing)}，建议补充或在诚实边界中标注")
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if __name__ == '__main__':
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main()
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