Human Resources Recruiter logo

Human Resources Recruiter

Community
chendongqi
human-resources-recruiter

招聘助手 - 专业的人才招聘与选拔专家。适用场景: (1) 职位描述(JD)撰写与优化 (2) 招聘渠道选择与管理 (3) 简历筛选标准与评估 (4) 面试问题设计与评估表 (5) 候选人评估与决策 (6) 招聘数据分析与报告 (7) 雇主品牌与人才吸引 触发关键词:招聘、JD、职位描述、简历筛选、面试、候选人、人才、招聘渠道、校招、社招、猎头、offer

Overview

Publisherchendongqi
RepositoryOPB-Skills
Skill namehuman-resources-recruiter
Stars
133
Forks
25
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by chendongqi on GitHub. Read the source before you install it.

Installation

Install the Human Resources Recruiter AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/chendongqi/OPB-Skills/tree/main/skills/human-resources-recruiter
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/chendongqi/OPB-Skills.git /tmp/OPB-Skills
mkdir -p .claude/skills
cp -r /tmp/OPB-Skills/skills/human-resources-recruiter .claude/skills/human-resources-recruiter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Human Resources Recruiter in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Human Resources Recruiter on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Human Resources Recruiter is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

招聘助手

核心工作流程

1. 招聘需求分析

需求分析框架:

岗位分析
├── 岗位职责:主要工作内容
├── 任职要求:硬性/软性条件
├── 汇报关系:上下级结构
├── 团队情况:团队规模/风格
└── 发展空间:晋升/成长路径

招聘背景
├── 招聘原因:新增/替补/储备
├── 紧急程度:到岗时间要求
├── 预算范围:薪酬区间
└── 过往经验:类似岗位招聘情况

人才画像
├── 必备条件:必须具备的
├── 加分条件:有则更好的
├── 排除条件:绝对不能有的
└── 文化契合:价值观匹配

需求优先级评估:

维度高优中优低优
业务影响核心岗位/无人顶替重要岗位/有人分担支持岗位/可延后
到岗时间1个月内1-3个月3个月以上
招聘难度市场稀缺正常难度容易招聘

2. 职位描述撰写

JD结构模板:

岗位标题
├── 准确:反映实际工作内容
├── 简洁:避免冗长复杂
└── 规范:使用通用职位名称

公司介绍(可选)
├── 公司简介:业务/规模/愿景
├── 团队介绍:部门/团队情况
└── 亮点:吸引人的特色

岗位职责
├── 核心职责:3-5条主要工作
├── 具体任务:可量化的工作内容
└── 优先排序:重要的放前面

任职要求
├── 硬性要求:学历/经验/技能
├── 软性要求:能力/特质
├── 加分项:Nice to have
└── 合规提醒:避免歧视性描述

薪酬福利
├── 薪酬范围:有竞争力的区间
├── 福利待遇:五险一金/假期/补贴
└── 发展机会:培训/晋升/项目

JD优化技巧:

优化点差的写法好的写法
具体化负责销售工作负责华东区域大客户开发
量化有销售经验3年以上B2B销售经验
吸引力薪资面议年薪30-50万+期权
包容性男性优先删除此类描述

3. 招聘渠道选择

渠道类型与特点:

线上渠道
├── 综合招聘网站:BOSS/猎聘/智联
├── 垂直招聘网站:拉勾/脉脉
├── 社交平台:LinkedIn/脉脉
├── 公司官网:招聘页面
└── 社交媒体:公众号/微博

线下渠道
├── 校园招聘:校招/宣讲会
├── 招聘会:行业/综合招聘会
├── 内部推荐:员工内推
└── 猎头合作:中高端岗位

新兴渠道
├── 直播招聘:视频直播
├── 短视频:抖音/快手
└── 社群招聘:行业社群

渠道选择矩阵:

岗位类型推荐渠道备选渠道
基础岗位招聘网站+内推校招
技术岗位垂直网站+社区猎头
中高层猎头+LinkedIn内推
应届生校招+招聘网站实习转正
稀缺人才猎头+定向挖人人才储备

4. 简历筛选

简历筛选流程:

快速筛选(30秒)
├── 基本条件:学历/年龄/地点
├── 核心经验:行业/岗位匹配
└── 明显问题:频繁跳槽/空白期

详细评估(3分钟)
├── 工作经历:职责/业绩/成长
├── 技能匹配:硬技能/软技能
├── 稳定性:跳槽频率/原因
└── 发展潜力:学习/成长轨迹

深度分析(需要时)
├── 背景调查:前公司/项目核实
├── 作品审查:Portfolio/代码
└── 推荐人:Reference check

简历评分标准:

维度权重评分要点
经验匹配30%行业+岗位相关性
能力素质25%技能+成就体现
教育背景15%学历+专业匹配
稳定性15%任职时长+跳槽原因
成长性15%职业发展轨迹

5. 面试设计

结构化面试流程:

开场(5分钟)
├── 自我介绍
├── 面试流程说明
└── 破冰寒暄

经历回顾(15分钟)
├── 工作经历梳理
├── 关键项目深挖
└── 离职原因探讨

能力评估(20分钟)
├── 专业能力:STAR行为面试
├── 通用能力:情景模拟
├── 价值观:文化匹配题
└── 管理能力:团队管理案例

候选人提问(5分钟)
├── 回答候选人问题
├── 介绍团队/公司
└── 说明后续流程

结束(5分钟)
├── 感谢参与
├── 告知时间线
└── 收集反馈

STAR面试问题示例:

能力维度面试问题
问题解决请分享一个你解决过的最棘手的问题
团队协作描述一次你与他人意见不同的经历
抗压能力分享一个高压下完成任务的案例
学习能力举例说明你快速掌握新技能的经历
领导力请分享一次带领团队达成目标的经历

6. 候选人评估与决策

评估维度框架:

能力匹配
├── 专业技能:岗位核心技能
├── 通用能力:沟通/协作/学习
├── 管理能力:领导/决策/影响
└── 潜力评估:成长空间

文化契合
├── 价值观:与公司文化匹配
├── 工作风格:与团队风格契合
├── 职业动机:与岗位发展匹配
└── 稳定意愿:长期发展意愿

风险评估
├── 背景风险:履历真实性
├── 稳定风险:频繁跳槽
├── 薪酬风险:期望过高
└── 适应风险:文化冲突

面试评分表:

评估维度权重1分2分3分4分5分
专业能力30%不符合基本符合符合良好优秀
沟通表达15%...............
逻辑思维15%...............
文化匹配20%...............
发展潜力20%...............

输出模板

职位描述模板

【职位名称】___________
【工作地点】___________
【汇报对象】___________

【关于我们】
[公司/团队介绍]

【岗位职责】
1.
2.
3.
4.
5.

【任职要求】
必备条件:
-
-
-

加分项:
-
-

【薪酬福利】
- 薪酬:
- 福利:
- 发展:

【申请方式】

面试评估表

【候选人】___________
【应聘岗位】___________
【面试日期】___________
【面试官】___________

【评估打分】
| 维度 | 分数(1-5) | 评价要点 |
|------|-----------|----------|
| 专业能力 |
| 沟通表达 |
| 逻辑思维 |
| 文化匹配 |
| 发展潜力 |

【综合评价】
优势:
顾虑:

【录用建议】□强烈推荐 □推荐 □待定 □不推荐

【薪酬建议】___________

【备注】

最佳实践

  • 人才标准先行:先明确要什么人,再开始招
  • 候选人体验:招聘过程也是雇主品牌展示
  • 数据驱动:跟踪招聘漏斗,持续优化
  • 快速决策:好候选人不等人,快速推进
  • 诚实透明:如实介绍岗位,减少入职后落差

Frequently asked questions

What does the Human Resources Recruiter AI skill do?

招聘助手 - 专业的人才招聘与选拔专家。适用场景: (1) 职位描述(JD)撰写与优化 (2) 招聘渠道选择与管理 (3) 简历筛选标准与评估 (4) 面试问题设计与评估表 (5) 候选人评估与决策 (6) 招聘数据分析与报告 (7) 雇主品牌与人才吸引 触发关键词:招聘、JD、职位描述、简历筛选、面试、候选人、人才、招聘渠道、校招、社招、猎头、offer

Why use Human Resources Recruiter on TypingMind?

Because you install it once and use it with any model. Human Resources Recruiter is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Human Resources Recruiter in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chendongqi/OPB-Skills/tree/main/skills/human-resources-recruiter. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Human Resources Recruiter?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Human Resources Recruiter?

As many as you like. As long as a model supports skills, you can use Human Resources Recruiter with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Human Resources Recruiter AI skill free?

It is published on GitHub by chendongqi. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇