大部分简历卡 ATS，是因为只是肉眼扫一遍 JD 然后塞几个 buzzword。这套路从 2022 年开始就不灵了——现代 ATS 把硬技能、软技能、动作动词、级别信号分别打分。下面 15 个模板把关键词工作做成一条结构化流水线：提取、分类、对比、修复、排序。
适合哪些场景
ATS 重度依赖的渠道（LinkedIn Easy Apply、Workday、Greenhouse、Lever）求职者，转行者，帮候选人改简历的猎头，以及背景不差但回复率只剩个位数的人。
什么时候不建议这样写 Prompt
走熟人推荐绕过 ATS 的不必看；招聘看作品集而非关键词（设计、研究）的也不必看。如果简历本身就缺相关经历，关键词调整也补不出来。
Prompt 结构公式
关键词匹配 Prompt 一定要带这六个要素：
- 角色：让 AI 扮演谁（recruiter、HR、职业教练、同级面试官）。
- 上下文：目标岗位、行业、级别、地区、你的背景、你在回应的 JD 或邮件。
- 目标：一个具体可交付物——改写后的 bullet、关键词排序、STAR 答案、follow-up 邮件。
- 限制：AI 不能做什么（别编造指标、别改事实、别堆我答不出的术语）。
- 输出格式：编号清单、markdown 表格、并排对比、带打分的排序。
- 示例 / 信号：1-2 段你自己的真实语气，或一份”好输出”参考。
这套 Prompt 适合用在哪
- 一份主简历对齐 5-10 份具体 JD
- 诊断为什么背景不差的简历被自动拒
- 把非传统经历翻译成 recruiter 词汇
- 为目标岗位族搭一份关键词速查表
- 走 Workday / Greenhouse 投递前的最后一次过滤检查
15 个可直接复制的 Prompt 模板
1. 整份 JD 对整份简历
从这条开始——先看全景再放大。
You are an ATS-savvy recruiter. Compare this resume against this JD. Output a markdown table with columns: JD requirement | Resume evidence (file:line equivalent — section + bullet number) | Match strength (Strong / Partial / Missing). Do NOT suggest rewrites yet. End with a 3-sentence diagnosis: which 3 gaps would most hurt the ATS score.
JD:
{paste JD}
Resume:
{paste resume}可替换变量： JD —— 完整 JD, resume —— 完整简历文本
优化建议： 输出太泛时加一句：“Only flag requirements that appear in the JD’s Responsibilities or Requirements section, ignore the boilerplate intro.”
2. JD bullet 对 resume bullet 1:1 对比
Take this single JD responsibility bullet and compare it to my single most-relevant resume bullet. Score on 4 dimensions (0-3 each): keyword overlap, action verb strength, quantification, seniority match. Then write ONE rewritten bullet that scores 3/3/3/3 without fabricating facts.
JD bullet: "{jd_bullet}"
Resume bullet: "{resume_bullet}"3. ATS 关键词提取
Extract the keywords an ATS would parse from this JD. Output 3 lists: (1) Hard skills (tools, languages, frameworks, certifications) ranked by frequency, (2) Soft skills (collaboration, ownership, etc.) ranked by emphasis, (3) Action verbs the JD uses. Mark which terms appear in the JD title or first paragraph — those are weighted 3x.
JD:
{paste JD}4. 硬关键词 vs 软关键词分类
Below is a flat list of keywords I scraped from a JD. Classify each into: HARD (tool / language / cert / metric), SOFT (trait / collaboration), DOMAIN (industry vocabulary), or NOISE (boilerplate). For each HARD keyword, mark whether it is "must-have" or "nice-to-have" based on JD phrasing.
Keywords:
{paste list}5. 缺失关键词检测
Compare my resume against this JD. Output ONLY the keywords that appear in the JD but are absent from my resume. Group into: (A) keywords I could honestly add because I have the experience but did not name it, (B) keywords I cannot add without lying, (C) keywords that are JD boilerplate and not worth chasing.
JD:
{paste JD}
Resume:
{paste resume}6. 关键词密度检查
For each of these target keywords, count how many times it appears in my resume and where (which section / bullet). Flag keywords with density 0 (missing), 1 (under-weighted), and 4+ (stuffed-looking). Suggest where to add a keyword that is currently at density 0 or 1, naming the specific bullet to edit.
Keywords: {list}
Resume:
{paste resume}7. 同义词扩展
For each keyword in this JD, list 3-5 synonyms or adjacent terms an ATS might also accept (e.g., "A/B testing" -> "split testing", "experimentation", "controlled experiments"). Mark which synonyms I currently use in my resume so I can decide whether to swap toward the JD's exact wording.
JD keywords: {list}
My resume:
{paste resume}8. 行业黑话解码
I am switching from {source industry} to {target industry}. This JD is full of {target industry} jargon I half-understand. For each jargon term, give: (1) plain-English meaning, (2) the closest equivalent from {source industry}, (3) whether it is safe to claim on my resume given my background.
JD:
{paste JD}可替换变量： source industry —— 你现在的行业, target industry —— JD 所在行业
9. 地区差异关键词替换
My resume uses {region A} conventions (e.g., "CV", "A-Levels", "Pence"). I am applying to {region B}. Rewrite the keyword choices, certifications, and metric formats so the resume reads as native to {region B} without fabricating credentials. Preserve all facts. Output a diff: original -> new, with one-line reason per change.
Resume:
{paste resume}可替换变量： region A —— 当前简历风格（UK / EU / IN 等）, region B —— 目标市场（US / UK / SG 等）
10. 动作动词升级
For each bullet in my resume, score the action verb (Weak / OK / Strong) based on the JD's verb register. The JD uses verbs like: {list 5 JD verbs}. Suggest a stronger verb where applicable, but only if the new verb is still factually accurate for what I did. Output as: original bullet -> suggested verb swap -> reason.
Resume bullets:
{paste}11. 级别信号匹配
This JD is for a {target level} role (e.g., Senior, Staff, Lead). My resume currently reads at {current level}. Identify the 5 phrases or framing patterns that signal seniority mismatch (e.g., "assisted with" vs "owned", "contributed to" vs "drove"). Rewrite those phrases to read at {target level} without inflating titles or scope.
Resume:
{paste resume}可替换变量： target level —— Senior / Staff / Lead / Principal, current level —— 你真实当前级别
12. 可转移技能翻译
I have no direct experience with the JD's primary domain ({JD domain}), but I have transferable skills from {your background}. For each of the top 5 JD requirements, write ONE resume bullet that honestly bridges my experience to the requirement using transferable framing. Mark any bullet that would be a stretch (interviewer would question it).
JD:
{paste JD}
My background summary:
{paste 5 lines}13. 空白期 / 缺失项表述
My resume has a gap in {keyword / skill / years of X}. The JD requires it. Write 3 options for handling this gap: (A) cover-letter framing that acknowledges and bridges, (B) resume bullet that uses adjacent evidence, (C) honest "I do not have X but I have Y" line. For each, mark the risk of being filtered out.
Gap: {describe}
JD requirement: {paste line}14. 多 JD 重叠分析
一次申一类角色 5+ 家公司时跑。
Below are 5 JDs for the same role family. Extract the keywords that appear in 4+ of the 5 JDs — those are the role-family core. Then list keywords unique to each JD — those are company-specific tailoring opportunities. Output two tables: Core Keywords (rank by frequency) and Per-Company Unique Keywords.
JD 1:
{paste}
JD 2:
{paste}
JD 3:
{paste}
JD 4:
{paste}
JD 5:
{paste}15. Recruiter 词汇翻译 + fit 排序
最后跑；把分析结果转成申 / 不申的决定。
You are a senior recruiter. For each of these 10 JDs I am considering, score my resume against it 0-10 on: (1) hard-keyword match, (2) seniority match, (3) industry match, (4) realistic interview shot. Output a ranked table sorted by overall fit. For the bottom 3, explain in one line why I should skip them rather than spend tailoring time.
Resume:
{paste resume}
JDs (numbered 1-10):
{paste}容易踩的坑
- 把关键词当成尾页清单平铺——现代 ATS 看上下文，不只看频次。
- 直接复制 JD 原话——人工 review 一眼看穿，反而降分。
- 一份 JD 一份 JD 单独优化，没先抽出岗位族共性。
- 忽略动作动词——JD 的动词语气是一半的级别信号。
- 塞了一堆你面试答不出的关键词——过了 ATS 也过不了筛选电话。
- 只看硬技能不看软技能——“ownership”、“ambiguity”、“cross-functional” 也在打分。
- 把 ATS 优化当成 cover letter 和内推的替代品。
优化技巧
- 先跑模板 3（提取）+ 模板 4（分类），再动笔改。诊断在前，修复在后。
- “must-have” 关键词目标密度 2-3 次，分散在多条 bullet，不要堆在一段。
- 保留一份”全量诚实”主简历，每次按 JD 通过删 + 调序派生，别从零写起。
- 出现在 JD 标题里的关键词权重 ×3——那是 recruiter 的搜索词。
- 高级岗位优先调动词语气和 scope 信号，关键词频次次之。
- ATS 改完后大声读一遍，听着像机器人，人工 reviewer 也会觉得像。
- 每次申请的关键词 diff 存成 CSV，10-15 次后规律就出来了。
FAQ
- 关键词到底匹配多少才够？: “must-have” 80%+，“nice-to-have” 50%+，都在自然语境里。低于 60% 现代 ATS 基本会降权。
- AI 会不会编出 JD 里没有的关键词？: 会。提取后再回 JD 原文逐条核对，原文里没有的就丢掉。
- 该用 JD 原话还是改写？: 硬技能 / 证书照用原话；软技能可改写，让简历仍是你自己的语气。
- 不走 ATS 的渠道还用这套吗？: 密度（模板 6）可以跳过；提取、分类、可转移技能翻译对人工 reviewer 也有用。
- 怎么判断简历是被 ATS 拒的？: 24-72 小时内拒、零人工痕迹、邮件模版化——基本就是 ATS 过滤，该重调了。
- 整份简历贴给 ChatGPT 安全吗？: 可以，但把电话、地址、不能公开的客户名脱敏。脱敏不会影响输出质量。