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Ai hr performance review

Skill allinherog-star/ai-skills/skills/ai-hr-performance-review

AI Skills(ai-skills.ai)是一个面向各行各业的 AI 技能库。可直接用于openclaw,Hermes,qclaw,claude code,codex等智能体平台。为你的职业发展、行业竞争力,增加更多可能。

Install
npx -y skills add allinherog-star/ai-skills --skill ai-hr-performance-review

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绩效评语助手适合运营、产品、销售、software在用户提出“绩效反馈怎么写”这类问题,需要快速拆解目标、判断重点并形成可执行结果时使用,帮助基于输入材料生成运营诊断、商品/流量优化建议、执行清单。

SKILL.md

4.9 KB, ~1.6k tokens by cl100k_base, as published. Nobody here has run it

ai-hr-performance-review 绩效评语助手

快速开始

更多技能

概述

绩效评语助手用于回答「绩效反馈怎么写」、资料整理、数据清理、行动项,适合运营、产品、销售、software在明确业务目标、内容材料或分析对象后调用。 它会结合岗位、绩效或求职材料、粘贴岗位描述、简历、绩效素材、人事沟通草稿、评估标准或反馈目标。等输入,整理关键上下文,并输出运营诊断、商品/流量优化建议、执行清单,便于继续执行、复盘或交付。

什么时候使用

适用场景

  • 用户提出“绩效反馈怎么写”这类问题,需要快速拆解目标、判断重点并形成可执行结果
  • 运营、产品、销售、software需要围绕绩效评语助手生成运营诊断、商品/流量优化建议、执行清单
  • 用户已经准备了处理目标(说明要完成的匹配分析、绩效反馈、表达优化、面试准备或风险检查。)、相关角色(说明候选人、员工、管理者、HRBP、面试官或沟通对象。)、岗位或资料链接(填写公开可访问的岗位、公司、政策或参考资料链接。),希望整理成可执行的分析或优化结果
  • 用户需要把绩效评语助手相关材料转成清晰结论、优先级和下一步动作

调用方式

通过导出的 Python runner 直接调用 AI Skills API:

命令示例

基础调用

python3 scripts/run.py --params '{}'

带常用参数调用

python3 scripts/run.py --params '{"goal":"处理目标"}'

参数说明

参数类型必填默认说明
goalstring-说明要完成的匹配分析、绩效反馈、表达优化、面试准备或风险检查
audiencestring-说明候选人、员工、管理者、HRBP、面试官或沟通对象
materialUrlstring-填写公开可访问的岗位、公司、政策或参考资料链接;需要传可访问的完整 URL
materialFilestring-上传简历、岗位说明、绩效记录、人事政策、面试材料或沟通草稿
materialTextstring-粘贴岗位描述、简历、绩效素材、人事沟通草稿、评估标准或反馈目标
brandRequirementsstring-补充事实依据、敏感信息处理、语气要求、不可承诺内容、法律边界或人工复核重点

完整机器可读参数结构见 references/form-schema.json

参数取值参考

当前技能没有需要额外查表的分类参数。

支持的输入格式

当前技能直接接收 JSON 参数;如果参数里包含链接字段,请传完整、可访问的 URL。

示例请求

下面的示例参数可直接传给 scripts/run.py,runner 会把它们发送给 AI Skills API。

python3 scripts/run.py --params '{"goal":"处理目标"}'

等价的 --params JSON:

{
  "goal": "处理目标"
}

返回结果示例

{
  "success": true,
  "data": {
    "message": "示例结果请以技能真实返回结构为准。"
  },
  "meta": {
    "executionTime": 842,
    "cached": false
  }
}

交付内容

  • 运营诊断、商品/流量优化建议、执行清单:围绕用户目标整理可直接阅读、复盘或交付的核心结果。
  • 输入材料解读:结合处理目标(说明要完成的匹配分析、绩效反馈、表达优化、面试准备或风险检查。)、相关角色(说明候选人、员工、管理者、HRBP、面试官或沟通对象。)、岗位或资料链接(填写公开可访问的岗位、公司、政策或参考资料链接。)提炼关键上下文和判断依据。
  • 下一步动作:给出优先级、执行建议或可继续加工的内容框架。

结果使用建议

  • 先判断输出是否回答了用户关于「绩效评语助手」的核心问题。
  • 再检查结果是否覆盖运营诊断、商品/流量优化建议、执行清单,以及是否给出明确下一步动作。
  • 如果输入材料较少,建议让用户补充目标、受众、限制条件或原始材料后再运行。

运行前准备

  • AISKILLS_BASE_URL:默认 https://ai-skills.ai
  • AISKILLS_API_KEY:必填,用于认证调用
  • AISKILLS_TENANT_ID:默认 default

Gives 0 of the 12 instructions most review quality skills give in ~1.6k tokens

Counted across 1,048 of the 1,783 authors here whose files we hold, read 2026-08-07

  • ask questions one at a timein 81 of 1048, across 64 files
  • provide a recommended answer for each questionin 73 of 1048, across 50 files
  • explore the codebase instead of asking answerable questionsin 66 of 1048, across 42 files
  • resolve dependencies between decisions one-by-onein 42 of 1048, across 17 files
  • interview the user relentlessly about the planin 38 of 1048, across 13 files
  • order findings by severityin 31 of 1048
  • resolve each branch of the decision treein 27 of 1048, across 5 files
  • run a grilling sessionin 26 of 1048, across 5 files
  • update CONTEXT.md immediately when a term is resolvedin 26 of 1048, across 11 files
  • propose precise canonical terms for vague languagein 25 of 1048, across 7 files
  • create documentation files lazilyin 24 of 1048, across 5 files
  • Assign severity to every findingin 24 of 1048

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.