Full upload: MCNVideo AI project with .workbuddy

.workbuddy/skills/nuwa-skill-main/scripts/quality_check.py

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+#!/usr/bin/env python3
+"""
+自动检查生成的SKILL.md是否通过Phase 4质量标准。
+对照通过标准表格逐项检查，输出通过/不通过和具体原因。
+
+用法:
+    python3 quality_check.py <SKILL.md路径>
+
+示例:
+    python3 quality_check.py .claude/skills/elon-musk-perspective/SKILL.md
+"""
+
+import sys
+import re
+from pathlib import Path
+
+
+def check_mental_models(content: str) -> tuple[bool, str]:
+    """检查心智模型数量（3-7个）"""
+    # 匹配 ### 模型N: 或 ### N. 等模式
+    models = re.findall(r'^###\s+(?:模型|Model|心智模型)\s*\d', content, re.MULTILINE)
+    if not models:
+        # fallback: 数「### 」开头的行在心智模型section中
+        in_section = False
+        count = 0
+        for line in content.split('\n'):
+            if re.match(r'^##\s+.*心智模型|Mental Model', line, re.IGNORECASE):
+                in_section = True
+                continue
+            if in_section and re.match(r'^##\s+', line) and '心智模型' not in line:
+                break
+            if in_section and re.match(r'^###\s+', line):
+                count += 1
+        if count > 0:
+            passed = 3 <= count <= 7
+            return passed, f"{count}个心智模型 {'✅' if passed else '❌ (应为3-7个)'}"
+
+    count = len(models)
+    if count == 0:
+        return False, "未检测到心智模型section"
+    passed = 3 <= count <= 7
+    return passed, f"{count}个心智模型 {'✅' if passed else '❌ (应为3-7个)'}"
+
+
+def check_limitations(content: str) -> tuple[bool, str]:
+    """检查每个模型是否有局限性"""
+    has_limitation = bool(re.search(r'局限|失效|不适用|盲区|limitation|blind spot', content, re.IGNORECASE))
+    return has_limitation, "有局限性标注 ✅" if has_limitation else "❌ 未找到局限性描述"
+
+
+def check_expression_dna(content: str) -> tuple[bool, str]:
+    """检查表达DNA辨识度"""
+    dna_section = bool(re.search(r'表达DNA|Expression DNA|表达风格', content, re.IGNORECASE))
+    if not dna_section:
+        return False, "❌ 未找到表达DNA section"
+
+    # 检查是否有具体的风格描述（句式、词汇等）
+    style_markers = len(re.findall(r'句式|词汇|语气|幽默|节奏|确定性|引用|口头禅', content))
+    passed = style_markers >= 3
+    return passed, f"表达DNA特征: {style_markers}项 {'✅' if passed else '❌ (应≥3项)'}"
+
+
+def check_honest_boundary(content: str) -> tuple[bool, str]:
+    """检查诚实边界（至少3条）"""
+    # 找诚实边界section
+    boundary_match = re.search(r'(?:##\s+.*诚实边界|## Honest Boundary)(.*?)(?=\n##\s|\Z)', content, re.DOTALL | re.IGNORECASE)
+    if not boundary_match:
+        return False, "❌ 未找到诚实边界section"
+
+    boundary_text = boundary_match.group(1)
+    # 计算列表项
+    items = re.findall(r'^[-*]\s+', boundary_text, re.MULTILINE)
+    count = len(items)
+    passed = count >= 3
+    return passed, f"诚实边界: {count}条 {'✅' if passed else '❌ (应≥3条)'}"
+
+
+def check_tensions(content: str) -> tuple[bool, str]:
+    """检查内在张力（至少2对）"""
+    tension_markers = len(re.findall(r'张力|矛盾|tension|paradox|一方面.*另一方面|既.*又', content, re.IGNORECASE))
+    passed = tension_markers >= 2
+    return passed, f"内在张力: {tension_markers}处 {'✅' if passed else '❌ (应≥2处)'}"
+
+
+def check_primary_sources(content: str) -> tuple[bool, str]:
+    """检查一手来源占比"""
+    # 找调研来源section
+    source_section = re.search(r'(?:##\s+.*来源|## Source|## Reference)(.*?)(?=\n##\s|\Z)', content, re.DOTALL | re.IGNORECASE)
+    if not source_section:
+        return True, "未找到来源section（跳过检查）"
+
+    source_text = source_section.group(1)
+    primary = len(re.findall(r'一手|primary|本人著作|原始', source_text, re.IGNORECASE))
+    secondary = len(re.findall(r'二手|secondary|转述|评论', source_text, re.IGNORECASE))
+    total = primary + secondary
+    if total == 0:
+        return True, "未标记来源类型（跳过检查）"
+
+    ratio = primary / total
+    passed = ratio > 0.5
+    return passed, f"一手来源占比: {primary}/{total} ({ratio:.0%}) {'✅' if passed else '❌ (应>50%)'}"
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("用法: python3 quality_check.py <SKILL.md路径>")
+        sys.exit(1)
+
+    skill_path = Path(sys.argv[1])
+    if not skill_path.exists():
+        print(f"❌ 文件不存在: {skill_path}")
+        sys.exit(1)
+
+    content = skill_path.read_text(encoding='utf-8')
+
+    checks = [
+        ("心智模型数量", check_mental_models),
+        ("模型局限性", check_limitations),
+        ("表达DNA辨识度", check_expression_dna),
+        ("诚实边界", check_honest_boundary),
+        ("内在张力", check_tensions),
+        ("一手来源占比", check_primary_sources),
+    ]
+
+    print(f"质量检查: {skill_path.name}")
+    print("=" * 50)
+
+    passed_count = 0
+    total = len(checks)
+
+    for name, check_fn in checks:
+        passed, detail = check_fn(content)
+        status = "✅ PASS" if passed else "❌ FAIL"
+        print(f"  {name:<12} {status}  {detail}")
+        if passed:
+            passed_count += 1
+
+    print("=" * 50)
+    print(f"结果: {passed_count}/{total} 通过")
+
+    if passed_count == total:
+        print("🎉 全部通过，可以交付")
+    elif passed_count >= total - 1:
+        print("⚠️ 基本通过，建议修复不通过项后交付")
+    else:
+        print("❌ 多项不通过，建议回到Phase 2迭代")
+
+    sys.exit(0 if passed_count == total else 1)
+
+
+if __name__ == '__main__':
+    main()