菜单
智能简历解析系统，支持PDF/Word/图片格式简历的结构化信息提取、岗位匹配度分析、优化建议生成。完全本地运行，无需外部API。使用场景：(1) 解析上传的简历文件提取核心信息，(2) 输入岗位JD计算简历匹配度，(3) 生成简历优化建议，(4) 导出结构化简历数据。
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/whyzsm/tiny-agents --skill resume-parser命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Single-Agent T-MAX Java backend architecture and engineering rule package. Use when Codex must design, implement, review, or diagnose T-MAX backend services involving Java 8, Spring Boot, Kylin, MyBatis XML, TiDB/MySQL, Apollo/Nacos, Feign, AnePageHelper, ResponseResult/DataGridResult, UEP export, Redis, RabbitMQ, or Maven multi-module repositories. Routes one Agent through repository intake, architecture decisions, API/data contracts, implementation diagnosis, quality/security review, and bounded evidence loops while keeping builds, tests, live calls, commits, and pushes as separate authority boundaries.
Create polished consulting-style single-file HTML research reports from messy business notes, screenshots, metrics, and iterative user feedback. Use this skill whenever the user asks for a 咨询风/艾瑞咨询风/研究报告/汇报报告/HTML报告, wants business or AI landing-practice material turned into a management-facing report, asks to add charts/diagrams, or asks to update report metrics and keep the whole HTML report consistent.
Audit HarmonyOS applications for AppGallery/AGC release readiness, including Huawei UX/HMI controls, system-feature integrations, and performance gates for multi-input interaction, latency, frame rate, content display, memory, and CPU. Use when users ask for 鸿蒙上架自检、应用市场审核、AGC发布前检查、控件/系统特性/人机交互/签名/隐私/权限/截图/性能核对或 release 包门禁, especially for Stage-model projects with module.json5, build-profile.json5, hvigorw, and local-first data constraints.
基于 SOC 职业分类
正在显示 SKILL.md
| name | resume-parser |
| description | 智能简历解析系统，支持PDF/Word/图片格式简历的结构化信息提取、岗位匹配度分析、优化建议生成。完全本地运行，无需外部API。使用场景：(1) 解析上传的简历文件提取核心信息，(2) 输入岗位JD计算简历匹配度，(3) 生成简历优化建议，(4) 导出结构化简历数据。 |
python scripts/extract_pdf.py <input-pdf-path>
返回纯文本内容
python scripts/extract_docx.py <input-docx-path>
返回纯文本内容
python scripts/extract_image.py <input-image-path>
返回OCR识别的文本内容
python scripts/parse_resume.py <extracted-text-file>
返回结构化JSON数据
python scripts/match_jd.py <resume-json-path> <jd-text-path>
返回匹配度分析结果
{
"basic_info": {
"name": "",
"phone": "",
"email": "",
"age": null,
"gender": "",
"location": "",
"work_years": null
},
"education": [
{
"school": "",
"major": "",
"degree": "",
"start_date": "",
"end_date": "",
"gpa"
{
"overall_score": 0-100,
"dimensions": [
{
"name": "核心技能匹配",
"score": 0-100,
"weight": 0.4,
"matched": ["匹配的核心技能列表"],
"missing": ["缺失的核心技能列表"],
"analysis": "详细分析说明"
},
{
"name": "岗位职责匹配",
"score": 0-100,
"weight": 0.3,
"matched": ["匹配的职责经验"]
首次使用前安装依赖：
pip install PyPDF2 python-docx pytesseract pillow python-multipart
注意：OCR功能需要安装Tesseract引擎