Human Resources Performance Manager logo

Human Resources Performance Manager

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chendongqi
human-resources-performance-manager

绩效管理助手 - 专业的绩效体系设计与员工发展专家。适用场景: (1) 绩效考核体系设计(OKR/KPI/BSC) (2) 绩效指标分解与目标设定 (3) 绩效评估方案与流程设计 (4) 绩效面谈与反馈辅导 (5) 绩效改进计划(PIP)制定 (6) 绩效数据分析与报告 (7) 绩效薪酬联动方案 触发关键词:绩效管理、绩效考核、OKR、KPI、BSC、绩效评估、绩效面谈、绩效改进、目标管理、考核指标、360评估

Overview

Publisherchendongqi
RepositoryOPB-Skills
Skill namehuman-resources-performance-manager
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 Performance Manager 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-performance-manager
  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-performance-manager .claude/skills/human-resources-performance-manager
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Human Resources Performance Manager 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 Performance Manager 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 Performance Manager 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.

绩效管理助手

核心工作流程

1. 绩效体系选择

主流绩效体系对比:

体系特点适用场景注意事项
KPI结果导向、量化明确成熟业务、可量化岗位避免唯KPI论
OKR目标对齐、挑战性创新业务、知识工作与考核适度分离
BSC四维平衡、战略承接战略管理、中高层实施复杂度高
MBO目标管理、自我控制管理层、专业岗需要成熟度
360度多维反馈、全面评估管理能力评估文化适应性

体系选择决策树:

组织特征判断
├── 业务成熟度高 + 可量化 → KPI为主
├── 创新驱动 + 快速变化 → OKR为主
├── 战略转型 + 平衡发展 → BSC框架
└── 混合型 → KPI+OKR组合

岗位特征判断
├── 销售/生产 → 结果KPI为主
├── 研发/创意 → OKR+过程指标
├── 管理岗位 → BSC+360反馈
└── 职能支持 → KPI+行为指标

2. 目标设定与分解

目标分解方法:

战略目标分解
├── 公司级目标:年度战略目标
├── 部门级目标:承接公司目标
├── 团队级目标:承接部门目标
└── 个人级目标:承接团队目标

目标分解原则
├── 纵向承接:上下级目标对齐
├── 横向协同:跨部门目标配合
├── SMART原则:具体可衡量
└── 挑战适度:跳一跳够得着

OKR设定框架:

Objective(目标)
├── 定性描述:鼓舞人心
├── 聚焦重点:3-5个
├── 时间周期:季度为主
└── 挑战性:60-70%完成率

Key Results(关键结果)
├── 量化指标:可衡量
├── 结果导向:不是任务
├── 每个O下:3-5个KR
└── 评分标准:0-1.0

KPI指标设计:

指标类型说明示例
结果指标最终成果销售额、利润率
过程指标关键行为拜访量、响应时长
能力指标素质能力专业技能、领导力
态度指标行为表现协作、主动性

3. 绩效评估流程

评估周期设计:

年度评估
├── 全面评估:绩效+能力+潜力
├── 薪酬调整:年度调薪依据
├── 晋升决策:职级变动参考
└── 发展规划:IDP制定

季度回顾
├── 目标进度:OKR/KPI达成
├── 问题诊断:障碍与支持
├── 目标调整:必要时修正
└── 反馈辅导:及时纠偏

月度跟踪
├── 进度同步:关键指标
├── 异常预警:偏差分析
└── 即时反馈:小问题及时解决

评估方法组合:

自评
├── 目标达成自述
├── 工作亮点总结
├── 问题与挑战
└── 发展需求

上级评估
├── 业绩表现评分
├── 能力素质评价
├── 综合等级评定
└── 反馈意见

360度评估
├── 上级评价:业绩+管理
├── 同级评价:协作+专业
├── 下级评价:领导+支持
└── 自我评价:对照参考

4. 绩效面谈与反馈

绩效面谈流程:

面谈准备
├── 回顾绩效数据
├── 准备反馈要点
├── 规划面谈结构
└── 预约时间地点

面谈开展
├── 开场:营造氛围
├── 回顾:绩效表现
├── 反馈:优点+改进
├── 倾听:员工想法
├── 共识:改进方向
└── 计划:下阶段目标

面谈跟进
├── 记录面谈内容
├── 跟进改进计划
├── 提供必要支持
└── 定期检查进展

反馈技巧(STAR+AR):

步骤说明示例
Situation具体情境"上周五的客户会议上..."
Task任务目标"你负责演示产品方案..."
Action具体行为"你准备了详细的对比分析..."
Result实际结果"客户当场决定签约..."
Alternative更好做法"如果能提前演练会更好"
Reinforce强化期望"继续保持这种专业态度"

5. 绩效改进计划(PIP)

PIP制定流程:

问题诊断
├── 绩效差距:具体差距是什么
├── 原因分析:为什么产生差距
│   ├── 能力问题:不会做
│   ├── 态度问题:不想做
│   ├── 资源问题:做不了
│   └── 环境问题:不让做
└── 改进可能:是否可以改进

计划制定
├── 改进目标:明确期望
├── 行动计划:具体措施
├── 时间节点:里程碑
├── 支持资源:培训/辅导
└── 考核标准:如何评判

执行跟踪
├── 定期检查:进展跟踪
├── 及时反馈:问题指出
├── 调整支持:必要帮助
└── 结果评估:是否达标

PIP文档模板要素:

要素内容
员工信息姓名、部门、岗位
绩效问题具体的绩效差距描述
改进目标SMART目标
行动计划具体改进措施
时间周期通常30-90天
检查节点周期性回顾时间
支持措施培训、辅导等
后果说明未改进的处理方式

6. 绩效结果应用

绩效等级分布:

强制分布(参考)
├── S/优秀:10-15%
├── A/良好:20-25%
├── B/合格:50-60%
├── C/待改进:10-15%
└── D/不合格:0-5%

分布调整因素
├── 团队整体表现
├── 业务发展阶段
├── 历史分布情况
└── 特殊情况考量

结果应用矩阵:

应用领域绩优员工合格员工待改进员工
薪酬调整大幅调薪正常调薪不调/少调
奖金发放高系数标准系数低/无系数
晋升机会优先考虑正常考虑暂不考虑
培训发展高潜培养技能提升改进辅导
岗位调整扩展职责维持现状PIP/调岗

输出模板

绩效考核方案

【考核周期】___________
【适用范围】___________

【考核体系】
考核方式:□KPI □OKR □BSC □360度 □混合

【指标体系】
| 指标类型 | 权重 | 指标名称 | 目标值 | 评分标准 |
|----------|------|----------|--------|----------|

【评估流程】
1. 自评:
2. 上级评估:
3. 结果校准:
4. 绩效面谈:
5. 结果应用:

【等级标准】
| 等级 | 定义 | 分数区间 | 建议分布 |
|------|------|----------|----------|

【申诉机制】

绩效面谈记录

【员工姓名】___________
【面谈日期】___________
【面谈人】___________

【绩效回顾】
本周期绩效等级:
主要业绩表现:

【优点与贡献】
1.
2.
3.

【待改进方面】
1.
2.

【下阶段目标】
| 目标 | 衡量标准 | 时间节点 |
|------|----------|----------|

【发展计划】

【员工反馈】

【双方签字】

最佳实践

  • 目标对齐:确保个人目标与组织目标一致
  • 持续反馈:不要等到年底才反馈
  • 公平公正:一致的标准、透明的流程
  • 发展导向:绩效管理的目的是帮助员工成长
  • 区分对待:对不同绩效的员工采取不同策略

Frequently asked questions

What does the Human Resources Performance Manager AI skill do?

绩效管理助手 - 专业的绩效体系设计与员工发展专家。适用场景: (1) 绩效考核体系设计(OKR/KPI/BSC) (2) 绩效指标分解与目标设定 (3) 绩效评估方案与流程设计 (4) 绩效面谈与反馈辅导 (5) 绩效改进计划(PIP)制定 (6) 绩效数据分析与报告 (7) 绩效薪酬联动方案 触发关键词:绩效管理、绩效考核、OKR、KPI、BSC、绩效评估、绩效面谈、绩效改进、目标管理、考核指标、360评估

Why use Human Resources Performance Manager on TypingMind?

Because you install it once and use it with any model. Human Resources Performance Manager 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 Performance Manager in TypingMind?

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

Which AI models can use Human Resources Performance Manager?

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 Performance Manager?

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

Is the Human Resources Performance Manager 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.

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