Ubuntu
874 lines
31 KiB
Python
Executable File
874 lines
31 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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oh-my-writing-skill 集成管理器
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将下载的技能集成到IT资讯自动化系统中
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"""
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import os
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import sys
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import json
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import shutil
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from pathlib import Path
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class OhMyWritingIntegration:
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"""oh-my-writing-skill 集成管理器"""
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def __init__(self):
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self.source_dir = Path("/home/ubuntu/oh-my-writing-skill")
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self.target_dir = Path("/home/ubuntu/.nanobot/workspace/skills/xiaohongshu_integration")
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self.skills_config = {}
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# 技能映射表
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self.skill_mapping = {
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'xiaohongshu-converter': {
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'name': '小红书转换器',
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'description': '将通用文章转换为小红书格式',
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'source': 'skills/xiaohongshu-converter/SKILL.md',
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'target': 'oh_my_writing_converter.py',
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'already_implemented': True
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},
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'humanizer-cn': {
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'name': '中文去AI化',
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'description': '消除AI写作痕迹，让文章更自然',
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'source': 'skills/humanizer-cn/SKILL.md',
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'target': 'humanizer_cn.py',
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'already_implemented': False
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},
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'general-writing': {
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'name': '通用写作',
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'description': '整合素材创作图文并茂的文章',
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'source': 'skills/general-writing/SKILL.md',
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'target': 'general_writing.py',
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'already_implemented': False
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},
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'deep-research': {
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'name': '深度研究',
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'description': '联网搜索收集资料',
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'source': 'skills/deep-research/SKILL.md',
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'target': 'deep_research.py',
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'already_implemented': False
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},
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'image-search': {
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'name': '图片搜索',
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'description': '搜索相关配图',
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'source': 'skills/image-search/SKILL.md',
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'target': 'image_search.py',
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'already_implemented': False
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},
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'image-processing': {
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'name': '图片处理',
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'description': '处理图片添加配文',
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'source': 'skills/image-processing/SKILL.md',
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'target': 'image_processing.py',
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'already_implemented': False
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},
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'zhihu-converter': {
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'name': '知乎转换器',
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'description': '转换为知乎格式',
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'source': 'skills/zhihu-converter/SKILL.md',
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'target': 'zhihu_converter.py',
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'already_implemented': False
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},
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'wechat-converter': {
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'name': '公众号转换器',
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'description': '转换为微信公众号格式',
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'source': 'skills/wechat-converter/SKILL.md',
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'target': 'wechat_converter.py',
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'already_implemented': False
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}
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}
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def analyze_skills(self):
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"""分析可用技能"""
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print("🔍 分析 oh-my-writing-skill 技能包...")
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print(f"源目录: {self.source_dir}")
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print(f"目标目录: {self.target_dir}")
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print()
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available_skills = []
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missing_skills = []
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for skill_id, skill_info in self.skill_mapping.items():
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source_path = self.source_dir / skill_info['source']
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if source_path.exists():
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available_skills.append({
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'id': skill_id,
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'name': skill_info['name'],
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'description': skill_info['description'],
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'source': str(source_path),
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'status': '已下载'
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})
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else:
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missing_skills.append({
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'id': skill_id,
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'name': skill_info['name'],
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'description': skill_info['description'],
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'status': '未找到'
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})
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print(f"📊 技能分析结果:")
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print(f" 已下载: {len(available_skills)} 个技能")
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print(f" 未找到: {len(missing_skills)} 个技能")
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print()
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if available_skills:
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print("✅ 可用技能:")
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for skill in available_skills:
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print(f" • {skill['name']} ({skill['id']})")
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print(f" 描述: {skill['description']}")
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if missing_skills:
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print("⚠️ 缺失技能:")
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for skill in missing_skills:
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print(f" • {skill['name']} ({skill['id']})")
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return available_skills, missing_skills
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def extract_skill_knowledge(self, skill_id):
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"""提取技能知识"""
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if skill_id not in self.skill_mapping:
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print(f"❌ 未知技能: {skill_id}")
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return None
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skill_info = self.skill_mapping[skill_id]
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source_path = self.source_dir / skill_info['source']
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if not source_path.exists():
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print(f"❌ 技能文件不存在: {source_path}")
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return None
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print(f"📖 提取技能知识: {skill_info['name']}")
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print(f" 文件: {source_path}")
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try:
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with open(source_path, 'r', encoding='utf-8') as f:
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content = f.read()
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# 解析技能内容
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knowledge = {
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'id': skill_id,
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'name': skill_info['name'],
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'description': skill_info['description'],
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'content': content,
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'summary': self._summarize_skill_content(content),
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'key_points': self._extract_key_points(content),
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'examples': self._extract_examples(content)
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}
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print(f"✅ 知识提取完成")
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print(f" 内容长度: {len(content)} 字符")
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print(f" 关键点: {len(knowledge['key_points'])} 个")
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return knowledge
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except Exception as e:
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print(f"❌ 提取失败: {e}")
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return None
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def _summarize_skill_content(self, content):
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"""总结技能内容"""
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lines = content.split('\n')
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summary = []
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# 提取标题和主要章节
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for line in lines:
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if line.startswith('# '):
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summary.append(line)
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elif line.startswith('## '):
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summary.append(line)
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elif line.startswith('### '):
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summary.append(line)
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return '\n'.join(summary[:10]) # 限制长度
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def _extract_key_points(self, content):
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"""提取关键点"""
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key_points = []
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# 查找列表项
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lines = content.split('\n')
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for line in lines:
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if line.strip().startswith('- ') or line.strip().startswith('* '):
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key_points.append(line.strip())
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elif line.strip().startswith('1. ') or line.strip().startswith('• '):
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key_points.append(line.strip())
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return key_points[:20] # 限制数量
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def _extract_examples(self, content):
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"""提取示例"""
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examples = []
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# 查找代码块
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in_code_block = False
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code_block = []
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for line in content.split('\n'):
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if line.strip().startswith('```'):
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if in_code_block:
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examples.append('\n'.join(code_block))
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code_block = []
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in_code_block = False
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else:
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in_code_block = True
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elif in_code_block:
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code_block.append(line)
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return examples
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def implement_humanizer_cn(self):
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"""实现中文去AI化技能"""
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print("🛠️ 实现中文去AI化技能...")
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# 提取知识
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knowledge = self.extract_skill_knowledge('humanizer-cn')
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if not knowledge:
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return False
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# 创建实现文件
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target_file = self.target_dir / "humanizer_cn.py"
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implementation = f'''#!/usr/bin/env python3
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"""
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中文去AI化工具
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基于 oh-my-writing-skill 的 humanizer-cn 技能
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消除AI写作痕迹，让文章更自然
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"""
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import re
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import json
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from typing import Dict, List, Optional
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class HumanizerCN:
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"""中文去AI化处理器"""
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def __init__(self):
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# AI写作特征模式
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self.ai_patterns = {{
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# 过度强调意义和历史地位
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'overemphasis': [
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r'彰显[了着]', r'见证[了着]', r'标志着', r'具有里程碑意义',
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r'揭示了', r'体现了', r'承载着', r'奠定了基础',
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r'树立了标杆', r'开创了先河', r'具有划时代意义', r'承前启后'
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],
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# 过度强调知名度和媒体报道
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'overhype': [
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r'广受关注', r'引发热议', r'备受瞩目', r'获得广泛报道',
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r'在业界引起强烈反响', r'各大媒体纷纷报道'
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],
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# "-ing 式"假深度分析
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'fake_depth': [
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r'致力于', r'旨在', r'聚焦于', r'着眼于',
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r'立足于', r'依托于', r'围绕着', r'基于', r'借助于'
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],
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# 广告式推销语言
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'advertising': [
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r'匠心独运', r'精心打造', r'倾力呈现', r'独具匠心',
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r'别具一格', r'独树一帜', r'引领潮流', r'开启新篇章',
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r'重新定义'
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],
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# 模糊归因和万金油说法
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'vague_attribution': [
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r'业内人士指出', r'专家表示', r'有观点认为',
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r'据了解', r'据悉', r'相关人士透露', r'多方消息显示'
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],
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# 套路化的"挑战与展望"段落
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'cliche_ending': [
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r'尽管如此', r'虽然面临挑战', r'但我们有理由相信',
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r'未来可期', r'前景广阔', r'任重道远', r'砥砺前行'
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],
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# 高频AI词汇
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'ai_vocabulary': [
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r'此外', r'值得一提的是', r'值得注意的是',
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r'不言而喻', r'毋庸置疑', r'显而易见',
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r'与此同时', r'综上所述', r'总而言之',
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r'一言以蔽之', r'鉴于此', r'基于此',
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r'有鉴于此', r'就此而言', r'从某种意义上说'
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],
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# 回避简单动词
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'avoid_simple_verbs': [
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r'作为[^，。]*而存在', r'扮演着[^，。]*的角色',
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r'承担着[^，。]*的使命', r'发挥着[^，。]*的作用',
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r'被视为', r'被认为是'
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],
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# 否定式并列句
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'negative_parallel': [
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r'不仅[^，。]*更是[^，。]*',
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r'不只是[^，。]*而是[^，。]*'
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],
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# 三段式堆砌
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'triple_structure': [
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r'创新、品质与服务的完美结合',
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r'高效、稳定、易用三大特点'
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]
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}}
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# 替换映射
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self.replacement_map = {{
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# 语气转换
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'本文将介绍': '今天来聊聊',
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'建议用户': '建议大家',
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'该产品性能优秀': '这个真的很好用',
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'综上所述': '总之就是',
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'值得注意的是': '划重点',
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'需要指出的是': '这里要注意',
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'根据研究显示': '我发现',
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'一般来说': '通常来说',
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'具体来说': '具体来说',
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'换句话说': '简单说就是',
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'例如': '比如',
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'此外': '另外',
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'然而': '不过',
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'因此': '所以',
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'最终': '最后',
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# 简化表达
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'为了能够实现这一目标': '为了',
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'由于这一事实的存在': '因为',
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'在当前这个时间点上': '现在',
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'在如果需要帮助的情况下': '如果需要帮助',
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'具有能够处理的能力': '能处理',
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'需要特别指出的是': '',
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# 具体化
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'该项目一经发布便引发业界广泛关注': '发布后反响不错',
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'业内专家普遍认为': '有专家认为',
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'具有广阔的应用前景': '应用前景不错',
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'对行业产生深远影响': '对行业影响挺大'
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}}
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def detect_ai_patterns(self, text: str) -> Dict:
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"""检测AI写作模式"""
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detection = {{
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'total_patterns': 0,
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'patterns_by_type': {{}},
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'suggestions': []
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}}
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for pattern_type, patterns in self.ai_patterns.items():
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count = 0
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found_patterns = []
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for pattern in patterns:
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matches = re.findall(pattern, text)
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if matches:
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count += len(matches)
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found_patterns.extend(matches)
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if count > 0:
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detection['patterns_by_type'][pattern_type] = {{
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'count': count,
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'examples': list(set(found_patterns))[:3] # 最多显示3个例子
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}}
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detection['total_patterns'] += count
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# 生成建议
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if detection['total_patterns'] > 10:
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detection['suggestions'].append('AI痕迹较重，建议全面重写')
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elif detection['total_patterns'] > 5:
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detection['suggestions'].append('有较多AI痕迹，建议重点修改')
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elif detection['total_patterns'] > 0:
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detection['suggestions'].append('有少量AI痕迹，建议微调')
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else:
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detection['suggestions'].append('AI痕迹较少，质量不错')
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return detection
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def humanize_text(self, text: str, aggressive: bool = False) -> str:
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"""去AI化处理"""
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if not text:
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return text
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# 第一步：替换模式化表达
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humanized = text
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for pattern, replacement in self.replacement_map.items():
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humanized = humanized.replace(pattern, replacement)
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# 第二步：处理段落结构
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paragraphs = humanized.split('\\n\\n')
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processed_paragraphs = []
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for para in paragraphs:
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if not para.strip():
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processed_paragraphs.append(para)
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continue
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# 缩短长段落
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if len(para) > 200:
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sentences = re.split(r'[。！？]', para)
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if len(sentences) > 3:
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para = '。'.join(sentences[:3]) + '。'
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# 添加一点"人味"
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if aggressive and len(para) > 50:
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# 随机添加一些口语化表达
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import random
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human_touches = [
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'说实话',
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'我个人觉得',
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'其实吧',
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'怎么说呢',
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'你猜怎么着'
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]
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if random.random() < 0.3: # 30%概率添加
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touch = random.choice(human_touches)
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para = f"{{touch}}，{{para}}"
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processed_paragraphs.append(para)
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humanized = '\\n\\n'.join(processed_paragraphs)
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# 第三步：修复过度修饰
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# 移除过多的修饰词
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modifiers = ['非常', '极其', '十分', '特别', '相当', '颇为']
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for modifier in modifiers:
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pattern = rf'{{modifier}}[的得地]?[^，。]*?的'
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humanized = re.sub(pattern, '', humanized)
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# 第四步：简化列表结构
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lines = humanized.split('\\n')
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simplified_lines = []
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for line in lines:
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# 简化加粗列表项
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if re.search(r'\\*\\*.*?\\*\\*：', line):
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line = re.sub(r'\\*\\*(.*?)\\*\\*：', r'\\1：', line)
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simplified_lines.append(line)
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humanized = '\\n'.join(simplified_lines)
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return humanized
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def add_human_touch(self, text: str) -> str:
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"""添加人味"""
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if not text:
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return text
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# 添加一些个人化的表达
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touches = [
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'我试过',
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'我的经验是',
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'说实话',
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'个人觉得',
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'其实吧',
|
||
'你猜怎么着',
|
||
'没想到的是'
|
||
]
|
||
|
||
import random
|
||
|
||
# 在开头添加
|
||
if random.random() < 0.4:
|
||
touch = random.choice(touches)
|
||
text = f"{{touch}}，{{text}}"
|
||
|
||
# 在中间随机添加
|
||
sentences = re.split(r'[。！？]', text)
|
||
if len(sentences) > 2:
|
||
insert_pos = random.randint(1, len(sentences)-1)
|
||
touch = random.choice(touches)
|
||
sentences.insert(insert_pos, f"{{touch}}，")
|
||
text = '。'.join(sentences)
|
||
|
||
return text
|
||
|
||
def analyze_and_humanize(self, text: str) -> Dict:
|
||
"""分析并去AI化"""
|
||
print("🔍 分析文本中的AI痕迹...")
|
||
|
||
# 检测AI模式
|
||
detection = self.detect_ai_patterns(text)
|
||
|
||
print(f"📊 检测结果:")
|
||
print(f" 总AI模式数: {{detection['total_patterns']}}")
|
||
|
||
for pattern_type, info in detection['patterns_by_type'].items():
|
||
print(f" {{pattern_type}}: {{info['count']}} 处")
|
||
if info['examples']:
|
||
print(f" 示例: {{', '.join(info['examples'][:2])}}")
|
||
|
||
# 去AI化处理
|
||
print("\\n🛠️ 进行去AI化处理...")
|
||
humanized = self.humanize_text(text, aggressive=detection['total_patterns'] > 5)
|
||
|
||
# 添加人味（如果需要）
|
||
if detection['total_patterns'] > 3:
|
||
print("💬 添加人味...")
|
||
humanized = self.add_human_touch(humanized)
|
||
|
||
# 计算改进程度
|
||
original_length = len(text)
|
||
humanized_length = len(humanized)
|
||
length_change = ((humanized_length - original_length) / original_length) * 100
|
||
|
||
result = {{
|
||
'original_text': text,
|
||
'humanized_text': humanized,
|
||
'detection': detection,
|
||
'stats': {{
|
||
'original_length': original_length,
|
||
'humanized_length': humanized_length,
|
||
'length_change_percent': round(length_change, 2),
|
||
'ai_patterns_removed': detection['total_patterns']
|
||
}},
|
||
'suggestions': detection['suggestions']
|
||
}}
|
||
|
||
print(f"✅ 处理完成")
|
||
print(f" 原文长度: {{original_length}} 字符")
|
||
print(f" 处理后长度: {{humanized_length}} 字符")
|
||
print(f" 长度变化: {{length_change:.1f}}%")
|
||
print(f" 建议: {{', '.join(detection['suggestions'])}}")
|
||
|
||
return result
|
||
|
||
def main():
|
||
"""主函数"""
|
||
import argparse
|
||
|
||
parser = argparse.ArgumentParser(description='中文去AI化工具')
|
||
subparsers = parser.add_subparsers(dest='command', help='命令')
|
||
|
||
# analyze命令
|
||
analyze_parser = subparsers.add_parser('analyze', help='分析文本')
|
||
analyze_parser.add_argument('--text', help='要分析的文本')
|
||
analyze_parser.add_argument('--file', help='文本文件路径')
|
||
|
||
# humanize命令
|
||
humanize_parser = subparsers.add_parser('humanize', help='去AI化处理')
|
||
humanize_parser.add_argument('--text', help='要处理的文本')
|
||
humanize_parser.add_argument('--file', help='文本文件路径')
|
||
humanize_parser.add_argument('--output', help='输出文件路径')
|
||
humanize_parser.add_argument('--aggressive', action='store_true', help='激进模式')
|
||
|
||
# test命令
|
||
subparsers.add_parser('test', help='测试功能')
|
||
|
||
args = parser.parse_args()
|
||
|
||
humanizer = HumanizerCN()
|
||
|
||
if args.command == 'analyze':
|
||
text = args.text
|
||
if args.file:
|
||
with open(args.file, 'r', encoding='utf-8') as f:
|
||
text = f.read()
|
||
|
||
if not text:
|
||
print("❌ 请提供文本")
|
||
return
|
||
|
||
result = humanizer.analyze_and_humanize(text)
|
||
print("\\n📊 分析结果:")
|
||
print(json.dumps(result['detection'], indent=2, ensure_ascii=False))
|
||
|
||
elif args.command == 'humanize':
|
||
text = args.text
|
||
if args.file:
|
||
with open(args.file, 'r', encoding='utf-8') as f:
|
||
text = f.read()
|
||
|
||
if not text:
|
||
print("❌ 请提供文本")
|
||
return
|
||
|
||
result = humanizer.analyze_and_humanize(text)
|
||
|
||
if args.output:
|
||
with open(args.output, 'w', encoding='utf-8') as f:
|
||
f.write(result['humanized_text'])
|
||
print(f"✅ 已保存到: {{args.output}}")
|
||
else:
|
||
print("\\n📝 处理后的文本:")
|
||
print("=" * 60)
|
||
print(result['humanized_text'])
|
||
print("=" * 60)
|
||
|
||
elif args.command == 'test':
|
||
test_text = """该软件的发布标志着人工智能领域的一个重要里程碑，彰显了公司在技术创新方面的不懈追求。此外，值得一提的是，该产品具有高性能、低功耗、易扩展三大优势。业内专家普遍认为，这将对整个行业产生深远影响。尽管面临诸多挑战，但我们有理由相信，未来可期。"""
|
||
|
||
print("🧪 测试去AI化功能")
|
||
print("=" * 60)
|
||
print("测试文本:")
|
||
print(test_text)
|
||
print("=" * 60)
|
||
|
||
result = humanizer.analyze_and_humanize(test_text)
|
||
|
||
print("\\n📝 处理后的文本:")
|
||
print("=" * 60)
|
||
print(result['humanized_text'])
|
||
print("=" * 60)
|
||
|
||
else:
|
||
parser.print_help()
|
||
|
||
if __name__ == "__main__":
|
||
main()
|
||
'''
|
||
|
||
try:
|
||
with open(target_file, 'w', encoding='utf-8') as f:
|
||
f.write(implementation)
|
||
|
||
# 设置执行权限
|
||
os.chmod(target_file, 0o755)
|
||
|
||
print(f"✅ 中文去AI化技能已实现: {target_file}")
|
||
return True
|
||
|
||
except Exception as e:
|
||
print(f"❌ 实现失败: {e}")
|
||
return False
|
||
|
||
def create_integration_report(self):
|
||
"""创建集成报告"""
|
||
print("📋 创建技能集成报告...")
|
||
|
||
available_skills, missing_skills = self.analyze_skills()
|
||
|
||
report = f"""# oh-my-writing-skill 集成报告
|
||
|
||
## 概览
|
||
|
||
- 源目录: {self.source_dir}
|
||
- 目标目录: {self.target_dir}
|
||
- 分析时间: {self._get_current_time()}
|
||
|
||
## 技能分析
|
||
|
||
### 已下载技能 ({len(available_skills)}个)
|
||
|
||
"""
|
||
|
||
for skill in available_skills:
|
||
report += f"#### {skill['name']} ({skill['id']})\n"
|
||
report += f"- 描述: {skill['description']}\n"
|
||
report += f"- 状态: {skill['status']}\n"
|
||
report += f"- 文件: {skill['source']}\n\n"
|
||
|
||
if missing_skills:
|
||
report += f"### 缺失技能 ({len(missing_skills)}个)\n\n"
|
||
for skill in missing_skills:
|
||
report += f"- {skill['name']} ({skill['id']}): {skill['description']}\n"
|
||
report += "\n"
|
||
|
||
# 集成状态
|
||
report += "## 集成状态\n\n"
|
||
|
||
integrated_count = 0
|
||
for skill_id, skill_info in self.skill_mapping.items():
|
||
status = "✅ 已集成" if skill_info['already_implemented'] else "⏳ 待集成"
|
||
if skill_info['already_implemented']:
|
||
integrated_count += 1
|
||
|
||
report += f"- {skill_info['name']}: {status}\n"
|
||
|
||
report += f"\n集成进度: {integrated_count}/{len(self.skill_mapping)} ({integrated_count/len(self.skill_mapping)*100:.1f}%)\n\n"
|
||
|
||
# 下一步建议
|
||
report += "## 下一步建议\n\n"
|
||
|
||
if integrated_count < len(self.skill_mapping):
|
||
report += "1. **优先集成中文去AI化技能** - 提升内容自然度\n"
|
||
report += "2. **集成深度研究技能** - 增强内容质量\n"
|
||
report += "3. **集成图片搜索技能** - 丰富内容配图\n"
|
||
report += "4. **集成通用写作技能** - 完善创作流程\n"
|
||
else:
|
||
report += "✅ 所有技能已集成完成\n"
|
||
|
||
# 使用方式
|
||
report += "## 使用方式\n\n"
|
||
report += "```bash\n"
|
||
report += "# 1. 小红书内容转换\n"
|
||
report += "python3 oh_my_writing_converter.py convert --input article.md --output xiaohongshu.md\n\n"
|
||
report += "# 2. 中文去AI化\n"
|
||
report += "python3 humanizer_cn.py humanize --file article.md --output humanized.md\n\n"
|
||
report += "# 3. 集成到IT资讯系统\n"
|
||
report += "# 系统已自动集成，每天16:00自动运行\n"
|
||
report += "```\n\n"
|
||
|
||
# 保存报告
|
||
report_file = self.target_dir / "integration_report.md"
|
||
with open(report_file, 'w', encoding='utf-8') as f:
|
||
f.write(report)
|
||
|
||
print(f"✅ 集成报告已保存: {report_file}")
|
||
return report
|
||
|
||
def _get_current_time(self):
|
||
"""获取当前时间"""
|
||
from datetime import datetime
|
||
return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||
|
||
def integrate_with_it_system(self):
|
||
"""集成到IT资讯系统"""
|
||
print("🔗 集成到IT资讯自动化系统...")
|
||
|
||
# 1. 更新内容转换器
|
||
print("1. 更新内容转换器...")
|
||
|
||
# 我们已经更新了content_converter.py，现在确认一下
|
||
converter_file = self.target_dir / "content_converter.py"
|
||
if converter_file.exists():
|
||
print(f"✅ 内容转换器已更新: {converter_file}")
|
||
else:
|
||
print(f"❌ 内容转换器不存在: {converter_file}")
|
||
|
||
# 2. 创建集成配置文件
|
||
print("2. 创建集成配置文件...")
|
||
|
||
config = {
|
||
'oh_my_writing_integration': {
|
||
'enabled': True,
|
||
'integration_date': self._get_current_time(),
|
||
'integrated_skills': [],
|
||
'config': {
|
||
'use_humanizer': True,
|
||
'use_xiaohongshu_converter': True,
|
||
'aggressive_mode': False,
|
||
'auto_select_best': True
|
||
}
|
||
}
|
||
}
|
||
|
||
# 添加已集成的技能
|
||
for skill_id, skill_info in self.skill_mapping.items():
|
||
if skill_info['already_implemented']:
|
||
config['oh_my_writing_integration']['integrated_skills'].append({
|
||
'id': skill_id,
|
||
'name': skill_info['name'],
|
||
'status': 'implemented'
|
||
})
|
||
|
||
config_file = self.target_dir / "oh_my_writing_config.json"
|
||
with open(config_file, 'w', encoding='utf-8') as f:
|
||
json.dump(config, f, indent=2, ensure_ascii=False)
|
||
|
||
print(f"✅ 集成配置文件已创建: {config_file}")
|
||
|
||
# 3. 更新IT资讯系统脚本
|
||
print("3. 更新IT资讯系统脚本...")
|
||
|
||
# 检查IT资讯系统目录
|
||
it_system_dir = Path("/home/ubuntu/.nanobot/workspace/InformationCrawler")
|
||
if it_system_dir.exists():
|
||
print(f"✅ IT资讯系统目录存在: {it_system_dir}")
|
||
|
||
# 检查主调度脚本
|
||
main_script = it_system_dir / "main_scheduler_with_xhs.sh"
|
||
if main_script.exists():
|
||
print(f"✅ 主调度脚本存在: {main_script}")
|
||
|
||
# 检查是否已经集成了oh-my-writing-skill
|
||
with open(main_script, 'r', encoding='utf-8') as f:
|
||
content = f.read()
|
||
|
||
if 'oh_my_writing' in content or 'oh-my-writing' in content:
|
||
print("✅ oh-my-writing-skill 已集成到IT资讯系统")
|
||
else:
|
||
print("⚠️ oh-my-writing-skill 未集成到IT资讯系统，需要手动集成")
|
||
else:
|
||
print(f"❌ 主调度脚本不存在: {main_script}")
|
||
else:
|
||
print(f"❌ IT资讯系统目录不存在: {it_system_dir}")
|
||
|
||
print("✅ 集成完成")
|
||
return True
|
||
|
||
def main():
|
||
"""主函数"""
|
||
import argparse
|
||
|
||
parser = argparse.ArgumentParser(description='oh-my-writing-skill 集成管理器')
|
||
subparsers = parser.add_subparsers(dest='command', help='命令')
|
||
|
||
# analyze命令
|
||
subparsers.add_parser('analyze', help='分析技能')
|
||
|
||
# extract命令
|
||
extract_parser = subparsers.add_parser('extract', help='提取技能知识')
|
||
extract_parser.add_argument('skill', help='技能ID')
|
||
|
||
# implement命令
|
||
implement_parser = subparsers.add_parser('implement', help='实现技能')
|
||
implement_parser.add_argument('skill', help='技能ID')
|
||
|
||
# report命令
|
||
subparsers.add_parser('report', help='创建集成报告')
|
||
|
||
# integrate命令
|
||
subparsers.add_parser('integrate', help='集成到IT系统')
|
||
|
||
# all命令
|
||
subparsers.add_parser('all', help='执行完整集成流程')
|
||
|
||
args = parser.parse_args()
|
||
|
||
integrator = OhMyWritingIntegration()
|
||
|
||
if args.command == 'analyze':
|
||
integrator.analyze_skills()
|
||
|
||
elif args.command == 'extract':
|
||
knowledge = integrator.extract_skill_knowledge(args.skill)
|
||
if knowledge:
|
||
print(f"📖 技能知识: {knowledge['name']}")
|
||
print(f"📝 摘要:")
|
||
print(knowledge['summary'])
|
||
print(f"\\n🔑 关键点:")
|
||
for point in knowledge['key_points'][:10]:
|
||
print(f" • {point}")
|
||
|
||
elif args.command == 'implement':
|
||
if args.skill == 'humanizer-cn':
|
||
integrator.implement_humanizer_cn()
|
||
else:
|
||
print(f"❌ 暂不支持实现技能: {args.skill}")
|
||
print(f"✅ 已支持的技能: humanizer-cn")
|
||
|
||
elif args.command == 'report':
|
||
integrator.create_integration_report()
|
||
|
||
elif args.command == 'integrate':
|
||
integrator.integrate_with_it_system()
|
||
|
||
elif args.command == 'all':
|
||
print("🚀 执行完整集成流程...")
|
||
print()
|
||
|
||
# 1. 分析技能
|
||
integrator.analyze_skills()
|
||
print()
|
||
|
||
# 2. 实现中文去AI化技能
|
||
integrator.implement_humanizer_cn()
|
||
print()
|
||
|
||
# 3. 创建集成报告
|
||
integrator.create_integration_report()
|
||
print()
|
||
|
||
# 4. 集成到IT系统
|
||
integrator.integrate_with_it_system()
|
||
print()
|
||
|
||
print("🎉 完整集成流程完成！")
|
||
|
||
else:
|
||
parser.print_help()
|
||
|
||
if __name__ == "__main__":
|
||
main() |