Resume Rocket

v0.1.1

AI 简历火箭 — 一键把你的简历改写成目标 JD 的满分匹配版，可选对接 Boss 直聘自动投递。输入旧简历 + 目标岗位 JD，输出「高命中改写版 + 匹配度评分 + 面试话术卡」。春招/秋招/跳槽必备。

⭐ 0· 79·0 current·0 all-time

by@chenmoon1111

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for chenmoon1111/resume-rocket.

Previewing Install & Setup.

Prompt PreviewInstall & Setup

Install the skill "Resume Rocket" (chenmoon1111/resume-rocket) from ClawHub.
Skill page: https://clawhub.ai/chenmoon1111/resume-rocket
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install resume-rocket

ClawHub CLI

Package manager switcher

npx clawhub@latest install resume-rocket

Security Scan

Capability signals

CryptoCan make purchasesRequires sensitive credentials

These labels describe what authority the skill may exercise. They are separate from suspicious or malicious moderation verdicts.

VirusTotal

Pending

View report →

OpenClaw

Suspicious

high confidence

ℹ

Purpose & Capability

Code and SKILL.md align with the stated purpose: parsing resumes, extracting JD keywords, scoring, rewriting via LLM, exporting DOCX/MD, and a Pro auto-apply flow (stubbed call to boss-zhipin). Minor mismatch: skill.json includes a pricing_server URL that the code does not call (unused/inconsistent). Overall capabilities match the description.

Instruction Scope

SKILL.md claims '简历数据不上传云端', but the code sends resume snippets and JD text to remote LLM providers via the OpenAI client (rewriter.generate and interview generation). Additionally, the documentation and DAY2 report explicitly encourage users to hand over their LLM API key to the developer for testing — that's a social-engineering/privacy risk and not required for normal operation (the code accepts a local key but should never require sending it to third parties). The Pro auto-apply flow uses a local subprocess and currently only prints a stub; the code does attempt to perform network scraping of JD URLs (requests) which is expected for JD fetcher.

✓

Install Mechanism

There is no remote arbitrary download/install payload. Dependencies are standard Python packages listed in requirements.txt (python-docx, pdfplumber, requests, openai). No install spec that fetches code from untrusted URLs and no archive extraction. This is proportionate to the task.

Credentials

The skill sensibly requests an LLM API key (RR_LLM_KEY or common fallbacks) which is necessary for rewrite/interview features. Concerns: (1) SKILL.md and in-repo docs encourage users to share their API key with the developer for testing — unnecessary and risky. (2) The license system uses a local HMAC secret (RR_LICENSE_SECRET) hard-coded default in the repo; publishing that secret allows anyone to generate valid-looking activation codes locally, undermining the paid gating. (3) skill.json marks llm-key required but the code gracefully degrades without a key; this inconsistency may mislead install-time validation.

ℹ

Persistence & Privilege

The skill does not request elevated system privileges or 'always' installation. It writes a usage file under the user's home (~/.openclaw/resume-rocket-usage.json) and outputs to a local ./output directory — expected for this tool. Auto-apply could perform many outbound requests (to job sites) when used in Pro mode; that behavior is opt-in and gated by the license check, but bear in mind account risk when using automated application.

What to consider before installing

Key things to consider before installing/use: - Privacy: Despite the SKILL.md claim that résumé data 'does not upload to the cloud', the tool sends resume fragments and JD text to the configured LLM provider when doing rewrite or generating interview cards. That means your resume content will be transmitted to whichever LLM service you configure. If you are concerned about sensitive personal data, do not enable LLM features or scrub PII first. - Do NOT share your personal LLM API key with the developer or anyone else. The repo/ docs ask users to paste keys for testing — you should refuse. Configure and use your own key locally instead. - License/payments: The project uses a local, HMAC-based offline license scheme with a default secret baked into the code. If published with that default secret, attackers (or anyone with the repo) could generate valid activation codes to bypass paid gating. If you plan to use or distribute this skill, replace RR_LICENSE_SECRET with a secure secret and move license verification to a server you control. - Private payments / off-platform activation: The payment flow described (QR + manual activation via codes) is off-platform and hard to audit. Expect limited buyer protections and manual delivery delays. - Auto-apply risk: The Pro auto-apply feature automates applications to job platforms and admits the risk of account suspension; use conservatively and prefer manual control. - If you want to proceed: audit or remove the scripts that generate licenses (scripts/gen-license.py) and the default secret before publishing; run the code locally using your own LLM key; never give your key to the maintainer; and verify the skill.json pricing_server usage if you expect server-side verification. If you are uncomfortable with these issues, mark the skill as untrusted or require more changes from the author before using.

Like a lobster shell, security has layers — review code before you run it.

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79downloads

0stars

2versions

Updated 5d ago

v0.1.1

MIT-0

Resume Rocket · AI 简历火箭

一句话：把你的简历按目标 JD 重写，命中率从 30% 拉到 90%。

为什么需要它

投 100 份简历 → HR 用 ATS 系统按关键词过滤 → 90% 直接淘汰
每家公司 JD 关键词都不一样 → 人工一份份改到怀疑人生
改完自己看不出问题 → 还是没反馈

Resume Rocket 做的事：

解析你的简历（PDF/Word/纯文本）
抓取目标岗位 JD（Boss 直聘链接 / 拉勾 / 手动粘贴）
LLM 对齐：把你简历里本来就有的经历，用 JD 要求的语言重新表达
输出匹配度评分 + 改写版 + 面试话术

不做虚假包装。只做"你真的做过的事，用招聘方想听的话说出来"。

快速开始

# 安装
clawhub install resume-rocket

# 免费试用（每天 1 次）
openclaw skill run resume-rocket \
  --resume "C:\path\to\my-resume.pdf" \
  --jd "https://www.zhipin.com/job_detail/xxx.html"

# 输出：
# ./output/
#   ├── resume-optimized.docx    改写版简历
#   ├── match-report.md           匹配度分析
#   └── interview-cards.md        面试话术（STAR 格式）

核心功能

1. 智能解析

✅ PDF / DOCX / MD / TXT 自动识别
✅ 提取工作经历、项目、技能、教育、证书
✅ 识别隐含能力（从项目描述反推技能栈）

2. JD 深度理解

✅ Boss 直聘链接自动抓取
✅ 手动粘贴 JD 文本
✅ 提取核心关键词 / 优先级 / 加分项

3. 匹配度评分（满分 100）

技术栈匹配：85/100  ✅ Python / SQL / 数据建模  ❌ 缺：Spark
工作年限：    10/10 ✅ 3 年经验 符合 JD "3-5 年"
学历：        10/10 ✅ 本科 符合
亮点项目：    20/25 ⚠️ 建议突出"用户留存提升 15%"的 A/B 测试项目
综合评分：    88/100

4. 改写建议

语言重写：把"做了用户分析" → "搭建用户留存监控体系，通过 RFM 模型..."
关键词注入：JD 要 Spark 你没有 → 从你的"Hadoop 离线计算"合理过渡
STAR 法则：每段经历 Situation/Task/Action/Result 结构化

5. 面试卡片（免费版不含）

针对 JD 痛点生成 10 张话术卡：

"你为什么想来我们公司？" → 3 个定制答案
"你的缺点？" → 避雷 + 巧妙回答
可能被问的技术深挖（按 JD 技术栈）

6. Pro 版 · 自动投递

openclaw skill run resume-rocket --mode auto-apply \
  --jd-list boss.txt \
  --daily-limit 50

读 JD 列表 → 每个 JD 定制一份改写版 → 用 boss-zhipin skill 自动投递
日报：投了多少 / 打招呼 / HR 回复率 / 建议改进

定价

版本	价格	说明
免费	¥0	1 次/天，无面试卡，无自动投递
单次	¥29	单份简历 × 单个 JD，全功能
月卡	¥99	30 天无限改写 + 面试卡
Pro	¥299/月	月卡 + 自动投递 + 数据分析

💳 购买方式（扫码付款 → 加微信发码）

步骤

扫下方二维码付款（支付宝 / 微信任选）
备注你的订单：rr- + 档位首字母 + 你的联系方式
- 例：rr-S-13427xxx = 单次 ¥29（S=Single）
- 例：rr-M-13427xxx = 月卡 ¥99（M=Monthly）
- 例：rr-P-13427xxx = Pro ¥299（P=Pro）
加微信 AGI-竹（扫付款码同名）
发付款截图给我，1 小时内发激活码
配置环境变量：
```
set RR_LICENSE_KEY=S-20261231-xxxxxxxx
```

支付宝收款

支付宝

微信收款

退款政策

付款后 24 小时内，未使用激活码可全额退款
已使用则不退（激活码绑定你的本机账号）
有问题加微信直接找我

配置

首次使用需配置 LLM：

openclaw skill config resume-rocket \
  --llm-provider alibaba \
  --llm-model qwen-plus \
  --llm-key sk-xxx

推荐模型：

🏆 qwen-plus（阿里百炼，速度快 + 中文强）
✅ deepseek-chat（便宜 + 质量好）
✅ glm-4（智谱，中文优秀）

常见问题

Q: 会不会造假？ A: 不会。工具只基于你简历里已有的事实重新表达，绝不编造经历。你可以对着改写版问自己："这事我真做过吗？"答案必须是"是"。

Q: 多久能改完一份？ A: 免费/单次版 30-60 秒；Pro 批量投递时每份 10-15 秒。

Q: 隐私？ A: 简历数据不上传云端。LLM 调用走你自己配置的 Key，数据只在本机 + LLM 服务商之间传输。

Q: 为什么不走平台付款？ A: ClawHub 是 skill 注册中心，不处理支付。所以我们走私域收款 + 手动发码。优点：无平台抽成，你付多少我拿多少（除了税）。缺点：不是 24×7 自动化，高峰期可能等 1-4 小时发码。

Q: Boss 投递会封号吗？ A: Pro 版内置频控（默认 50/天、随机间隔）+ 模拟真人行为，但投递本身有被 Boss 风控的风险，建议不要开太高频率。账号安全由用户自行承担。

路线图

v0.1 PDF/DOCX 解析 + LLM 改写 + 匹配评分
v0.2 Boss JD 抓取 + 批量模式
v0.3 面试卡片生成
v0.4 Pro 自动投递
v0.5 投递效果数据分析 + 话术 AB 测试
v1.0 正式版 + 多平台（拉勾/脉脉/LinkedIn）

作者 & 支持

开发：OpenClaw 社区
反馈：提 Issue 或飞书群
商务合作：企业批量授权请联系

⚠️ 合规声明

本 skill 只做内容优化，不做：

❌ 伪造学历/证书/工作经历
❌ 代人面试 / 代发声
❌ 绕过平台风控的高频投递

使用过程中的账号风险由用户自行承担。

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