Initial commit

skills/jd-resume-tailor/scripts/parse_jd.py

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+#!/usr/bin/env python3
+"""
+parse_jd.py — 解析 JD 文本，抽取 must-have / nice-to-have / 职责 / 特殊要求
+
+用法：
+    python parse_jd.py --jd-file jd.txt --out jd_parsed.json
+    python parse_jd.py --jd-text "..." --out jd_parsed.json
+
+输出 JSON 结构供下一步的 jd_gap.py 使用，也可以直接给用户看。
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import sys
+from pathlib import Path
+
+MUST_PATTERNS = [
+    r"必须", r"必备", r"必要条件", r"硬性要求", r"应当", r"需要", r"需具备",
+    r"至少\s*\d+\s*年", r"\d+\+?\s*年以上",
+    r"required", r"must\s*have", r"mandatory", r"essential",
+    r"minimum\s+\d+\s+years",
+]
+NICE_PATTERNS = [
+    r"加分", r"优先", r"加分项", r"熟悉.+者优先", r"有.+经验者优先",
+    r"preferred", r"nice\s*to\s*have", r"plus", r"bonus", r"desirable",
+]
+ACTION_VERBS = [
+    "负责", "主导", "推动", "设计", "搭建", "构建", "优化", "规划", "迭代",
+    "孵化", "复盘", "运营", "管理", "协调", "执行", "驱动", "落地", "重构",
+    "lead", "drive", "build", "design", "architect", "develop", "implement",
+    "optimize", "manage", "coordinate", "execute", "own",
+]
+
+
+def split_sentences(text: str) -> list[str]:
+    # 中文按 。！？; 拆，英文按 . ; 拆，并保留 bullet 行
+    raw = re.split(r"[。！？!?；;\n]+", text)
+    return [s.strip(" \t-•·*") for s in raw if s.strip()]
+
+
+def classify_sentences(sentences: list[str]) -> dict:
+    must, nice, resp, others = [], [], [], []
+    for s in sentences:
+        s_low = s.lower()
+        if any(re.search(p, s_low) for p in NICE_PATTERNS):
+            nice.append(s)
+        elif any(re.search(p, s_low) for p in MUST_PATTERNS):
+            must.append(s)
+        elif any(v in s_low for v in ACTION_VERBS):
+            resp.append(s)
+        else:
+            others.append(s)
+    return {"must": must, "nice": nice, "responsibilities": resp, "others": others}
+
+
+def extract_special(text: str) -> dict:
+    out: dict[str, str] = {}
+
+    # 学历
+    edu = re.search(r"(本科|硕士|博士|大专)(?:及以上|以上)?", text)
+    if edu:
+        out["education"] = edu.group(0)
+
+    # 工作年限
+    years = re.search(r"(\d+)\s*[-~–到至]\s*(\d+)\s*年|(\d+)\s*\+?\s*年(以上|及以上)?", text)
+    if years:
+        out["years"] = years.group(0)
+
+    # 语言
+    lang_pat = re.search(
+        r"(英语\s*(口语)?\s*(流利|熟练|母语)|CET[-\s]?[46]|雅思\s*\d(\.\d)?|托福\s*\d{2,3}|母语水平|business\s*english)",
+        text,
+        flags=re.IGNORECASE,
+    )
+    if lang_pat:
+        out["language"] = lang_pat.group(0)
+
+    # 城市
+    cities = re.findall(
+        r"(北京|上海|广州|深圳|杭州|南京|苏州|成都|武汉|西安|香港|新加坡|remote|hybrid|远程|海外)",
+        text,
+        flags=re.IGNORECASE,
+    )
+    if cities:
+        out["location"] = "/".join(sorted(set(c.lower() for c in cities)))
+
+    # 出差 / 加班信号
+    travel = re.search(r"(出差|派驻|常驻|项目制|加班|999|996|大小周)", text)
+    if travel:
+        out["working_style"] = travel.group(0)
+
+    # 证书
+    certs = re.findall(
+        r"(CFA(?:\s*Level\s*[I123]+)?|CPA|FRM|ACCA|PMP|AWS\s*[\w\s]*认证|Azure\s*[\w]*|GCP\s*[\w]*)",
+        text,
+        flags=re.IGNORECASE,
+    )
+    if certs:
+        out["certificates"] = "/".join(sorted(set(c.strip() for c in certs)))
+
+    return out
+
+
+def extract_skills(sentences: list[str]) -> list[str]:
+    """从所有句子里抽取技能词候选（短词优先，避免抽出整句）。"""
+    text = " ".join(sentences)
+    # 英文技能（CamelCase 或大写开头的词、含 . 或 +/- 的标识）
+    en = re.findall(r"\b[A-Za-z][A-Za-z0-9+/.\-_#]{1,20}\b", text)
+    # 中文 2~5 字常见技能词
+    zh = re.findall(r"[一-龥]{2,5}", text)
+    raw = en + zh
+
+    stop = {
+        # 中文虚词 / 通用动词
+        "公司", "我们", "你将", "团队", "需要", "能够", "具备", "熟悉", "了解",
+        "良好", "优秀", "经验", "能力", "岗位", "职责", "要求", "以上", "相关",
+        "进行", "完成", "负责", "推动", "实现", "提升", "并且", "包括", "以下",
+        "工作", "项目", "业务", "及其", "或者", "或", "与", "及", "的", "了",
+        "并", "对", "在", "等", "以", "等等", "通过", "将", "其", "之", "至",
+        "至上", "本科", "硕士", "博士", "者优先", "根据", "进行", "支持", "参与",
+        "主导", "提供", "建立", "搭建", "设计", "驱动", "决策", "分析", "推动",
+        "迭代", "规划", "运营", "协作", "跨部门", "跨团队", "高级", "资深",
+        "若干", "多种", "多元", "多类",
+        # 英文虚词
+        "and", "the", "with", "for", "of", "or", "to", "be", "as", "an", "is",
+        "are", "in", "on", "at", "by", "all", "you", "we", "us", "our", "your",
+        "a", "an", "this", "that", "these", "those", "it", "its",
+    }
+    # 含数字的"X年""X个"也过滤
+    digit_only = re.compile(r"^\d+$")
+
+    seen = set()
+    out = []
+    for token in raw:
+        key = token.lower()
+        if key in seen or token in stop or len(token) < 2 or digit_only.match(token):
+            continue
+        # 中文 token 不允许全是 stop 词的子串
+        seen.add(key)
+        out.append(token)
+    return out[:60]
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    src = parser.add_mutually_exclusive_group(required=True)
+    src.add_argument("--jd-file", help="JD 文本文件路径")
+    src.add_argument("--jd-text", help="直接传 JD 文本")
+    parser.add_argument("--out", help="输出 JSON 路径，缺省打印")
+    args = parser.parse_args()
+
+    if args.jd_file:
+        path = Path(args.jd_file).expanduser()
+        if not path.exists():
+            print(f"✗ JD 文件不存在：{path}", file=sys.stderr)
+            sys.exit(1)
+        text = path.read_text(encoding="utf-8")
+    else:
+        text = args.jd_text
+
+    sentences = split_sentences(text)
+    classified = classify_sentences(sentences)
+    special = extract_special(text)
+    skills = extract_skills(classified["must"] + classified["responsibilities"])
+
+    result = {
+        "must_have": classified["must"],
+        "nice_to_have": classified["nice"],
+        "responsibilities": classified["responsibilities"],
+        "skills_extracted": skills,
+        "special_requirements": special,
+        "raw_sentence_count": len(sentences),
+    }
+
+    payload = json.dumps(result, ensure_ascii=False, indent=2)
+    if args.out:
+        out_path = Path(args.out).expanduser()
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+        out_path.write_text(payload, encoding="utf-8")
+        print(f"✓ JD 解析结果已保存：{out_path}")
+    else:
+        print(payload)
+
+
+if __name__ == "__main__":
+    main()