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skills/resume-builder/scripts/ats_check.py
Executable file
269
skills/resume-builder/scripts/ats_check.py
Executable file
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#!/usr/bin/env python3
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"""
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ats_check.py — 简历 ATS（Applicant Tracking System）友好度检查 + 关键词覆盖率
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用法：
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python ats_check.py --resume resume.md --industry internet
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python ats_check.py --resume resume.md --industry tech --jd jd.txt
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python ats_check.py --resume resume.docx --industry finance --out report.md
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支持输入：.md / .txt / .docx（docx 走 python-docx，需要 pip install python-docx）
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"""
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from __future__ import annotations
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import argparse
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import re
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import sys
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from pathlib import Path
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INDUSTRIES = {"internet", "tech", "finance", "general"}
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def load_resume_text(path: Path) -> str:
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suffix = path.suffix.lower()
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if suffix in {".md", ".txt"}:
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return path.read_text(encoding="utf-8")
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if suffix == ".docx":
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try:
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from docx import Document
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except ImportError:
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print(
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"✗ 缺少 python-docx，请先安装：pip install python-docx --break-system-packages",
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file=sys.stderr,
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)
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sys.exit(1)
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doc = Document(str(path))
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return "\n".join(p.text for p in doc.paragraphs)
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print(f"✗ 暂不支持的格式：{suffix}", file=sys.stderr)
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sys.exit(1)
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def load_keywords(industry: str, references_dir: Path) -> list[str]:
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path = references_dir / "keywords" / f"{industry}.txt"
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if not path.exists():
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print(f"✗ 关键词库不存在：{path}", file=sys.stderr)
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sys.exit(1)
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return [
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line.strip()
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for line in path.read_text(encoding="utf-8").splitlines()
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if line.strip()
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]
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def coverage(resume_text: str, keywords: list[str]) -> dict:
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text_lower = resume_text.lower()
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hits, missing = [], []
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for kw in keywords:
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# 兼容大小写、中英混排
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if kw.lower() in text_lower:
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hits.append(kw)
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else:
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missing.append(kw)
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return {
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"hits": hits,
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"missing": missing,
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"rate": len(hits) / len(keywords) if keywords else 0,
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}
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def jd_extract_keywords(jd_text: str) -> list[str]:
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"""从 JD 文本里抽取候选关键词。简易版：取常见技能/工具/动词。"""
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# 抓中文 2~10 字、英文 2~30 字的"实词"
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candidates = re.findall(
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r"[A-Za-z][A-Za-z0-9+/.\-_]{1,29}|[一-龥]{2,10}",
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jd_text,
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)
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# 简单去停用词
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stop = {
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"公司", "工作", "我们", "你将", "团队", "需要", "能够", "具备", "熟悉",
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"了解", "良好", "优秀", "经验", "能力", "岗位", "职责", "要求", "以上",
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"相关", "及其", "或者", "进行", "完成", "负责", "推动", "实现", "提升",
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"并且", "包括", "以下", "根据",
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}
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seen = set()
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out = []
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for c in candidates:
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key = c.lower()
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if key in seen or c in stop:
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continue
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seen.add(key)
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out.append(c)
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return out[:80] # 取前 80 个候选
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def ats_friendliness(text: str, source_path: Path) -> tuple[int, list[str]]:
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"""评估 ATS 友好度，返回 (分数 / 10, 警告列表)。"""
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score = 10
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warnings = []
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# 长度
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if len(text) < 200:
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score -= 3
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warnings.append("⚠ 简历文本过短（< 200 字符），可能解析失败或内容不足")
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elif len(text) > 6000:
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score -= 1
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warnings.append("⚠ 简历偏长（> 6000 字符），建议精简到 1~2 页")
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# 联系方式
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has_email = bool(re.search(r"[\w.\-+]+@[\w.\-]+\.\w+", text))
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has_phone = bool(re.search(r"(\+?86[-\s]?)?1[3-9]\d{9}|\d{3}[-\s]?\d{4}[-\s]?\d{4}", text))
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if not has_email:
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score -= 1
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warnings.append("⚠ 没找到邮箱")
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if not has_phone:
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score -= 1
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warnings.append("⚠ 没找到手机号")
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# 装饰符号
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decorative = re.findall(r"[★☆●○◆◇▶▷■□▪▫♦]", text)
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if len(decorative) > 5:
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score -= 1
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warnings.append(f"⚠ 用了 {len(decorative)} 个装饰符号（★●◆等），ATS 可能识别异常，建议精简")
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# emoji
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emojis = re.findall(r"[\U0001F300-\U0001FAFF\U0001F600-\U0001F64F]", text)
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if emojis:
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score -= 1
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warnings.append(f"⚠ 检测到 {len(emojis)} 个 emoji，部分 ATS 会乱码，建议删掉")
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# 数字密度（量化结果是否充分）
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numbers = re.findall(r"\d+(?:\.\d+)?%?", text)
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bullet_count = len(re.findall(r"^\s*[-*•]\s+", text, flags=re.MULTILINE))
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if bullet_count > 0:
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density = len(numbers) / bullet_count
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if density < 0.4:
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score -= 1
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warnings.append(
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f"⚠ 量化密度低（每条 bullet 平均 {density:.2f} 个数字），"
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f"建议在工作 / 项目经历里多加数字"
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)
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# docx 特定
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if source_path.suffix.lower() == ".docx":
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try:
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from docx import Document
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doc = Document(str(source_path))
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tables = len(doc.tables)
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images = sum(1 for s in doc.inline_shapes)
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if tables > 1:
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score -= 1
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warnings.append(
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f"⚠ docx 里有 {tables} 个表格，部分 ATS 解析表格会丢字段，"
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f"建议改成正文段落"
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)
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if images > 0:
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score -= 1
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warnings.append(
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f"⚠ docx 里有 {images} 张图片（含证件照），ATS 不读图，"
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f"重要信息别只放在图里；证件照可保留"
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)
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except Exception:
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pass
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return max(0, score), warnings
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def render_report(
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industry_cov: dict,
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industry: str,
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jd_cov: dict | None,
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ats_score: int,
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ats_warnings: list[str],
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) -> str:
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lines = [
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"# 简历 ATS 检查报告",
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"",
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f"## 行业关键词覆盖（{industry}）",
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f"- 命中率：**{industry_cov['rate'] * 100:.1f}%** "
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f"({len(industry_cov['hits'])} / {len(industry_cov['hits']) + len(industry_cov['missing'])})",
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"",
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"**已命中**：" + (", ".join(industry_cov["hits"]) or "（无）"),
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"",
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"**建议补充**（前 15 个）：" + (", ".join(industry_cov["missing"][:15]) or "（无）"),
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"",
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]
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if jd_cov is not None:
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lines += [
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"## JD 关键词覆盖",
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f"- 命中率：**{jd_cov['rate'] * 100:.1f}%**",
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"",
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"**已命中**：" + (", ".join(jd_cov["hits"][:30]) or "（无）"),
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"",
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"**JD 出现但简历没有**（重点补这些）：" + (", ".join(jd_cov["missing"][:20]) or "（无）"),
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"",
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]
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lines += [
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f"## ATS 友好度评分：**{ats_score}/10**",
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"",
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]
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if ats_warnings:
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lines += ats_warnings
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else:
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lines.append("✅ 没有明显问题")
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lines += [
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"",
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"---",
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"## 改进建议优先级",
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"",
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"1. 先补 JD 命中率 → 这是 ATS 通过率的最直接信号",
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"2. 再补行业关键词 → 让简历能在更宽的搜索里被捞到",
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"3. 最后调 ATS 友好度 → 移除装饰符号、emoji、表格、图片",
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"",
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"注意：覆盖率不是越高越好，**关键词必须出现在真实的成就 bullet 里**，",
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"不要把关键词单独列一长串当 skills，会被 HR 一眼识破。",
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]
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return "\n".join(lines)
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--resume", required=True, help="简历文件路径 (.md/.txt/.docx)")
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parser.add_argument(
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"--industry", choices=list(INDUSTRIES), required=True, help="行业方向"
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)
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parser.add_argument("--jd", help="可选：JD 文本文件，做精准对比")
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parser.add_argument("--out", help="输出报告路径，缺省直接打印")
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parser.add_argument(
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"--references-dir",
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default=str(Path(__file__).resolve().parent.parent / "references"),
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)
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args = parser.parse_args()
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resume_path = Path(args.resume).expanduser()
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if not resume_path.exists():
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print(f"✗ 简历文件不存在：{resume_path}", file=sys.stderr)
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sys.exit(1)
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text = load_resume_text(resume_path)
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references_dir = Path(args.references_dir)
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industry_keywords = load_keywords(args.industry, references_dir)
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industry_cov = coverage(text, industry_keywords)
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jd_cov = None
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if args.jd:
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jd_path = Path(args.jd).expanduser()
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if not jd_path.exists():
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print(f"✗ JD 文件不存在：{jd_path}", file=sys.stderr)
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sys.exit(1)
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jd_keywords = jd_extract_keywords(jd_path.read_text(encoding="utf-8"))
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jd_cov = coverage(text, jd_keywords)
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ats_score, ats_warnings = ats_friendliness(text, resume_path)
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report = render_report(industry_cov, args.industry, jd_cov, ats_score, ats_warnings)
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if args.out:
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out_path = Path(args.out).expanduser()
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out_path.parent.mkdir(parents=True, exist_ok=True)
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out_path.write_text(report, encoding="utf-8")
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print(f"✓ 报告已生成：{out_path}")
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else:
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print(report)
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if __name__ == "__main__":
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main()
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