179 lines
6.5 KiB
Python
Executable File
179 lines
6.5 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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star_story_builder.py — 从简历文本里抽出工作 / 项目经历，生成"故事矩阵"骨架
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用法：
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python star_story_builder.py --resume resume.md --out stories.md
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输出一份 markdown，包含：
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- 检测到的 N 个经历（按时间倒序）
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- 每个经历的 STAR 骨架占位（让用户 / 模型补全）
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- 每个故事可以回答的行为题清单
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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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COMMON_BEHAVIORAL_QUESTIONS = {
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"ownership": [
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"讲一次你 ownership 体现得最好的经历",
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"你做过的最有成就感的项目",
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"讲一次你主动推动事情",
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],
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"collab": [
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"讲一次你跨部门 / 跨团队推动事情",
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"讲一次你和同事意见不合，怎么解决",
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"讲一次你说服别人改变想法",
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],
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"challenge": [
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"讲一次你在资源 / 时间不足下完成目标",
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"讲一次你做艰难决策的经历",
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"讲一次你打破常规 / 创新的经历",
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],
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"failure": [
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"讲一次你失败 / 没达成目标的经历",
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"讲一次你犯过的最大错误",
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"讲一次接到负面反馈，怎么应对",
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],
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"learning": [
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"讲一次你快速学会全新领域的经历",
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"你最近学到的最重要的事是什么",
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],
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}
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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("✗ 缺少 python-docx", file=sys.stderr)
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sys.exit(1)
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return "\n".join(p.text for p in Document(str(path)).paragraphs)
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print(f"✗ 暂不支持 {suffix}", file=sys.stderr)
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sys.exit(1)
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def extract_experiences(text: str) -> list[dict]:
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"""
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简易抽取：找形如 "公司 | 岗位 | 时间" 或 "项目名 | ..." 的行作为锚点，
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然后取该行后面、下一个锚点之前的内容作为经历内容。
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"""
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lines = text.splitlines()
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experiences: list[dict] = []
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current = None
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# 匹配锚点（含 | 或 ｜，且包含日期/年份）
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anchor_re = re.compile(
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r"^(?P<title>[^\n]+?[|｜][^\n]+?[|｜][^\n]+)$"
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)
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for line in lines:
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if anchor_re.match(line.strip()):
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if current and current["bullets"]:
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experiences.append(current)
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current = {"title": line.strip(), "bullets": []}
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else:
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stripped = line.strip()
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if current and stripped.startswith(("-", "*", "•")):
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current["bullets"].append(stripped.lstrip("-*• "))
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if current and current["bullets"]:
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experiences.append(current)
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return experiences
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def categorize_story(bullets: list[str]) -> list[str]:
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"""根据 bullet 关键词，判断这个故事最适合回答哪一类行为题。"""
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text = " ".join(bullets).lower()
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cats = []
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if any(k in text for k in ["主导", "owner", "推动", "drive", "lead", "0-1"]):
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cats.append("ownership")
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if any(k in text for k in ["跨部门", "跨团队", "协作", "对接", "合作"]):
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cats.append("collab")
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if any(k in text for k in ["紧急", "时间紧", "资源", "决策", "突破"]):
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cats.append("challenge")
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if any(k in text for k in ["失败", "下线", "回滚", "复盘", "教训", "踩坑"]):
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cats.append("failure")
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if any(k in text for k in ["新", "学习", "陌生", "首次", "从零"]):
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cats.append("learning")
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return cats or ["ownership"]
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def render(experiences: list[dict]) -> str:
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if not experiences:
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return (
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"# 故事矩阵（未检测到经历）\n\n"
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"无法从简历里自动抽取出工作 / 项目经历。可能是因为：\n"
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"1. 简历格式不是 `公司 | 岗位 | 时间` 的常见结构\n"
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"2. 经历用普通段落写，没有明显的锚点\n\n"
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"建议：手工告诉我你最有代表性的 3~5 段经历，我来帮你做 STAR 拆解。\n"
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)
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out = ["# 故事矩阵（Story Matrix）", ""]
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out.append(f"从简历里检测到 {len(experiences)} 段经历，按 STAR 拆解如下。")
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out.append("**请补充每个 STAR 段落里 `[占位]` 的内容**，准备好后这些故事可以覆盖 80% 的行为面问题。")
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out.append("")
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for idx, exp in enumerate(experiences[:8], start=1):
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cats = categorize_story(exp["bullets"])
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out.append(f"## 故事 {idx}：{exp['title']}")
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out.append("")
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out.append("**简历原始 bullet：**")
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for b in exp["bullets"][:5]:
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out.append(f"- {b}")
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out.append("")
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out.append("**STAR 拆解（请补全）：**")
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out.append("- **S（背景）**：[占位 - 一句话点明背景 / 痛点]")
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out.append("- **T（任务）**：[占位 - 你的具体任务和目标]")
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out.append("- **A（动作）**：[占位 - 分 2~4 步，每步带动词 + 决策依据]")
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out.append("- **R（结果）**：[占位 - 量化结果 + 一句反思]")
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out.append("")
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question_pool = []
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for cat in cats:
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question_pool.extend(COMMON_BEHAVIORAL_QUESTIONS.get(cat, []))
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out.append(f"**最适合回答的行为题（{', '.join(cats)}）：**")
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for q in question_pool[:4]:
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out.append(f"- {q}")
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out.append("")
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out.append("---")
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out.append("")
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out.append("## 使用建议")
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out.append("")
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out.append("- 把每个故事的 STAR 段落填好，每段控制在 30~90 秒讲完")
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out.append("- 面试时灵活组合：同一个故事可以从不同角度回答不同题")
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out.append("- 至少准备 **3 个完整故事**（成功 + 失败 + 协作 各一个），覆盖 80% 行为题")
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out.append('- 每个故事里强调「我」做了什么，避免大量「我们」')
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return "\n".join(out)
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--resume", required=True)
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parser.add_argument("--out")
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args = parser.parse_args()
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resume_text = load_resume_text(Path(args.resume).expanduser())
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experiences = extract_experiences(resume_text)
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report = render(experiences)
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if args.out:
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Path(args.out).write_text(report, encoding="utf-8")
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print(f"✓ 故事矩阵已生成：{args.out}")
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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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