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JD + 简历 → 面试题预测助手
by
antonia huang
· GitHub ↗
· v1.0.0
· MIT-0
407
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Install in OpenClaw
/install jd-interview-prep
Description
上传岗位描述（JD）和个人简历，AI 自动预测面试题（必问/针对性/追问三类）， 给出 STAR 答题框架，分析简历与 JD 匹配度，导出备考手册。
README (SKILL.md)
JD + 简历 → 面试题预测助手 🎯
你能做什么
上传岗位描述（JD）和个人简历，我帮你：
- 预测面试题 — 分三类共 15 道，覆盖必问、针对、追问
- 给出答题框架 — 每题配 STAR 结构思路 + 关键词提示
- 评估匹配度 — 你的简历和 JD 有多契合，哪里是弱点
- 生成备考手册 — 一键导出 Markdown，随时温习
使用方式
基本用法
直接粘贴 JD 和简历文本：
JD：
[粘贴岗位描述]
简历：
[粘贴简历内容]
文件上传
请分析我的面试准备，JD 文件：/path/to/jd.txt，简历：/path/to/resume.pdf
支持格式：.txt / .md / .pdf / .docx
输出格式
一、匹配度分析
📊 简历与 JD 匹配度：78%
✅ 优势匹配项（重点展示）
- Python 5年经验 ↔ JD要求：Python 3年以上 ✓
- 带过5人团队 ↔ JD要求：有团队管理经验 ✓
⚠️ 待补强项（重点准备）
- JD 要求 Kubernetes 经验 → 简历未提及
- JD 强调客户沟通能力 → 简历案例较少
二、面试题预测（15题）
📌 必问题（5题）
岗位通用高频题，几乎必问
- 请简单介绍一下你自己
- 答题要点：30秒版本 + 2分钟版本各准备一个
- STAR框架：背景→核心技能→最大成就→为何适合这个岗位
🎯 针对性题（5题）
根据你简历 vs JD 的 gap 生成，面试官大概率会追问的薄弱点
...
🔍 追问题（5题）
针对简历中的亮点/可疑点，深挖细节
...
三、备考手册（导出）
运行导出命令后生成 interview_prep_YYYY-MM-DD.md，包含所有题目+答题框架。
工具调用
# 解析文件（PDF/DOCX → 文本）
exec: python3 SKILL_DIR/scripts/parse_file.py "/path/to/file.pdf"
# 生成面试题报告
exec: python3 SKILL_DIR/scripts/generate_questions.py \
--jd "JD文本或文件路径" \
--resume "简历文本或文件路径" \
--output "/tmp/interview_prep.md"
注意事项
- JD 和简历都可以粘贴纯文本，不需要特定格式
- PDF 解析需要
pdfplumber：pip install pdfplumber - DOCX 解析需要
python-docx：pip install python-docx - 没有安装时自动 fallback 到纯文本输入
Usage Guidance
This skill appears to do what it claims (parse JD/resume and call an LLM to produce interview questions), but before installing consider: 1) The code sends your JD and resume to an external LLM endpoint (default https://api.deepseek.com). If you provide an API key, your personal data will be transmitted to that provider—verify their privacy policy and trustworthiness. 2) The skill's metadata does not declare the required OPENAI_API_KEY/DEEPSEEK_API_KEY env var; SKILL.md also omits the need for an API key—ask the author to document required credentials and the exact endpoint. 3) SKILL.md references a scripts/ path that doesn't match the included files; expect possible runtime path errors. 4) Run it in an isolated environment and avoid using a high-privilege API key (use a scoped/test key) until you confirm behavior. If you want, I can draft a request you can send to the author asking them to declare required env vars, confirm the API provider, and fix the path/instruction mismatches.
Capability Analysis
Type: OpenClaw Skill
Name: jd-interview-prep
Version: 1.0.0
The skill bundle is a legitimate interview preparation tool that analyzes job descriptions and resumes using an LLM. It consists of two main Python scripts: `generate_questions.py`, which handles the interaction with the DeepSeek/OpenAI API, and `parse_file.py`, which extracts text from PDF, DOCX, and text files. The code is transparent, follows its stated purpose, and uses standard libraries and secure subprocess calls without any evidence of malicious intent, data exfiltration to unauthorized endpoints, or prompt injection attacks.
Capability Assessment
Purpose & Capability
The name/description align with the included Python scripts: parse uploadable files and call an LLM to generate interview questions and a markdown report. Requiring PDF/DOCX parsers and an LLM call is reasonable for the stated functionality.
Instruction Scope
SKILL.md shows exec commands and mentions pdfplumber/python-docx, but it does NOT mention the requirement to supply an LLM API key. The code sends JD and resume text to an external LLM endpoint (API_BASE + '/chat/completions') which will transmit potentially sensitive resume data off-host. Also SKILL.md refers to 'SKILL_DIR/scripts/parse_file.py' while the repository places parse_file.py at skill root—this path mismatch may cause runtime errors or confusion.
Install Mechanism
There is no install spec or remote download; the skill is instruction + local Python scripts. No high-risk installers or external archives are fetched by the skill itself.
Credentials
generate_questions.py expects an API key via OPENAI_API_KEY or DEEPSEEK_API_KEY and a configurable API_BASE/LLM_MODEL. The skill registry metadata listed no required env vars/credentials. Requesting an LLM API key is proportionate to the functionality, but the omission from manifest/instructions is a material mismatch. The default API_BASE is set to https://api.deepseek.com—users should verify this provider and where their data will be sent.
Persistence & Privilege
The skill does not request permanent/system-wide privileges, does not set always:true, and does not modify other skills' configurations. It runs as invoked and uses subprocesses to call local scripts.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install jd-interview-prep - After installation, invoke the skill by name or use
/jd-interview-prep - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release
Metadata
Frequently Asked Questions
What is JD + 简历 → 面试题预测助手?
上传岗位描述（JD）和个人简历，AI 自动预测面试题（必问/针对性/追问三类）， 给出 STAR 答题框架，分析简历与 JD 匹配度，导出备考手册。 It is an AI Agent Skill for Claude Code / OpenClaw, with 407 downloads so far.
How do I install JD + 简历 → 面试题预测助手?
Run "/install jd-interview-prep" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is JD + 简历 → 面试题预测助手 free?
Yes, JD + 简历 → 面试题预测助手 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does JD + 简历 → 面试题预测助手 support?
JD + 简历 → 面试题预测助手 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created JD + 简历 → 面试题预测助手?
It is built and maintained by antonia huang (@antonia-sz); the current version is v1.0.0.
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