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resume-score

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Resume Score: Heuristic ATS Score Estimator

简历评分：启发式ATS分数估算工具

Invocation: 
/resume score <file>
Scripts: 
scripts/score_resume.py
at the plugin root.
Runs heuristic checks against a resume DOCX or text file and returns per-category scores plus a weighted overall (0–100). Mirrors the rubric in the parent 
resume
skill's 
references/scoring-rubric.md
.
调用方式： 
/resume score <文件>
脚本： 插件根目录下的
scripts/score_resume.py
。
针对DOCX或文本格式的简历文件执行启发式检查，返回各分类分数以及加权后的总分（0–100分）。与父级
resume
技能中
references/scoring-rubric.md
里的评分规则保持一致。

Behavior

行为逻辑

Always interview the user briefly before scoring — even though scoring is technically file-only, the score is more useful when contextualized to the role/seniority the user is targeting. Use 
mcp__conductor__AskUserQuestion
(one question per call) to ask:
  1. Target role and seniority
  2. Years of experience
  3. Page-count preference (1 / 2)
  4. JD available? (paste if yes — used for keyword-coverage commentary)
Skip any answers already inferable from the file's content (e.g., title in header). Then run the score script and present the results with the user's target context applied.
评分前务必先与用户进行简短沟通——尽管技术上仅需文件即可评分，但结合用户目标岗位/职级的上下文后，评分结果会更具参考价值。使用
mcp__conductor__AskUserQuestion
（每次调用提出一个问题）询问：
  1. 目标岗位和职级
  2. 工作年限（YoE）
  3. 简历页数偏好（1页 / 2页）
  4. 是否有岗位描述（JD）？如果有，请粘贴——用于关键词覆盖度分析
跳过任何可从文件内容中推断出的答案（例如页眉中的职位名称）。然后运行评分脚本，并结合用户的目标上下文展示结果。

Quick Reference

快速参考

StepWhat it does
1Brief interview (target role, YoE, page count, optional JD) via AskUserQuestion.
2Extract text from DOCX (zipfile + ElementTree) or read raw text.
3Detect bullets via standard markers + strong-verb prefix heuristic.
4Score 7 categories independently.
5Compute weighted overall and print breakdown with target-role commentary.
步骤操作内容
1通过AskUserQuestion进行简短沟通（目标岗位、工作年限、简历页数、可选的JD）。
2从DOCX文件中提取文本（借助zipfile + ElementTree）或读取原始文本。
3通过标准标记+强动词前缀启发式规则检测项目符号。
4独立对7个分类进行评分。
5计算加权总分，并结合目标岗位说明展示评分明细。

Categories

评分分类

  • Parsability — column/whitespace-bleed detection.
  • Headings — count of standard section headings present.
  • Quantification — fraction of bullets containing a number.
  • Verb variety — flags any verb used 3+ times.
  • Active voice — regex for passive constructions.
  • Vague filler — exact-match list of buzzwords.
  • Length — word count vs typical 1–2 page range.
  • 可解析性 — 检测列/空白溢出问题。
  • 标题规范度 — 统计标准章节标题的数量。
  • 量化程度 — 包含数字的项目符号占比。
  • 动词多样性 — 标记任何使用次数达3次及以上的动词。
  • 主动语态使用 — 用正则表达式检测被动结构。
  • 模糊冗余表述 — 精确匹配常见套话列表。
  • 篇幅长度 — 字数与典型的1-2页范围对比。

Output

输出示例

  ATS score estimate for: my_resume.docx
  --------------------------------------------------
  parsability      100/100
  headings         100/100
  quantification    98/100
    > 27/27 bullets quantified (100%).
  verbs             95/100
    > No verb used 3+ times.
  active_voice      96/100
    > No passive constructions detected.
  vague             96/100
  length            95/100
  visual            95/100
  --------------------------------------------------
  Overall (weighted)     97.0/100
  ATS score estimate for: my_resume.docx
  --------------------------------------------------
  parsability      100/100
  headings         100/100
  quantification    98/100
    > 27/27 bullets quantified (100%).
  verbs             95/100
    > No verb used 3+ times.
  active_voice      96/100
    > No passive constructions detected.
  vague             96/100
  length            95/100
  visual            95/100
  --------------------------------------------------
  Overall (weighted)     97.0/100

Caveat

注意事项

This is a heuristic, not a real ATS scan. For the actual score against a specific scanner, run the file through Jobscan, Resume Worded, Enhancv, or TopResume. Use this skill to catch issues before submission.
这是启发式估算，并非真实的ATS扫描。如果需要针对特定扫描工具的真实分数，请将文件通过Jobscan、Resume Worded、Enhancv或TopResume进行检测。使用本技能可在提交前排查问题。