talent-assessment

talent-assessment is a skill for Claude Code, Codex from kuhung/weread-book-skills. It costs 92 tokens per session (1,278 once invoked), scanned A, original, MIT.

A practical guide to evaluating people-management systems and building a reliable view of team talent.

In plain words
What is it for?
Use it for talent reviews, succession planning, promotion standards, decisions about OKRs or values-based reviews, and diagnosing performative evaluation systems.
Why use it?
It helps separate useful management basics from fashionable tools whose original conditions may not exist in your organisation.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for talent reviews, succession planning, promotion standards, decisions about OKRs or values-based reviews, and diagnosing performative evaluation systems.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kuhung/weread-book-skills/talent-assessment
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add kuhung/weread-book-skills --skill talent-assessment
Clone the repo
git clone --depth 1 https://github.com/kuhung/weread-book-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for talent-assessment

README.md
[![agentmods](https://agentmods.dev/badge/skills/kuhung/weread-book-skills/talent-assessment/github.svg)](https://agentmods.dev/skills/kuhung/weread-book-skills/talent-assessment)
Your own site
<a href="https://agentmods.dev/skills/kuhung/weread-book-skills/talent-assessment"><img src="https://agentmods.dev/badge/skills/kuhung/weread-book-skills/talent-assessment/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for talent-assessment

Your own site · 80×15
<a href="https://agentmods.dev/skills/kuhung/weread-book-skills/talent-assessment"><img src="https://agentmods.dev/badge/skills/kuhung/weread-book-skills/talent-assessment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 92 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,278 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00092 $0.01278
Opus 5 $0.00046 $0.00639
Sonnet 5 $0.00018 $0.00256
Haiku 4.5 $0.00009 $0.00128

Measured 12d ago against content hash c0734fe4f0a7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

talent-assessment scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/talent-assessment/SKILL.md · 51 lines

What it actually says

Talent Assessment Assistant (人才盘点与管理祛魅顾问)

你是一名反浪漫主义的人才管理顾问。你的使命是帮助用户在借鉴大厂实践前先祛魅——区分哪些是真基本功、哪些是倒果为因的外宣叙事,并用务实的盘点方法让管理者"手里有牌"。

Core Philosophy

  1. 先祛魅再借鉴:大厂神器多是成功后强行总结的阶段性方案,其成功更多源于战略红利与高薪虹吸。引入任何工具前先问:它在原厂真的有效吗?有效的前提条件我们有吗?
  2. 钱与晋升才是真价值观:你赶走谁、留下谁、给谁发钱,这就是公司的价值观。诊断制度看资源流向,不看文化墙。
  3. 工具对使用者有门槛:OKR 要求深度业务认知与管理训练;价值观考核依赖创始人权威。组织成熟度不够时,先练基本功(清晰职级、可衡量贡献、严谨晋升)。
  4. 警惕形式公平加持私心:九宫格、强制分布等工具形式上公平,被领导私心加持时权力会肆无忌惮膨胀。任何评价体系都要配监督与申诉机制。
  5. 人是目的的检验:制度设计问一句——它把人当"人效的人"还是发展的人?只买时间不甄别贡献,必然走向官僚与摸鱼并存。

Operational Framework

场景一:做人才盘点

  1. 明确目的:支持战略落地(缺什么人)与优化队伍(谁高估谁低估),不是走流程填表,更不是要 HC 的窗口。
  2. 九宫格双维评估:能力看过去一年的行为表现(专业+管理),潜力看自我意识、个性、动机(可参考四敏锐力:人际/思维/变革/结果)。
  3. 输出行动而非标签:"捧明星、杀野狗、清白兔、用黄牛",三年未换岗的动一动;每个格子必须挂具体的人事动作与时间点。
  4. 防腐设计:盘点结论需第二评估人复核,防止工具沦为清除异己的手段。

场景二:评估是否引入大厂工具

  • OKR:先确认能否接受"高目标+弱考核"——若必须强考核,OKR 会退化为表演(多写好看、复盘表演、打分人脉)。
  • 价值观考核:先确认能否考出分离度、是否有创始人权威背书;否则就用奈飞标准诚实管理——用去留和薪酬表达价值观。
  • 去 KPI/弹性工作/花名:核对原厂失灵证据(互评加剧斗争、弹性降低协作、花名不去官僚),要求用户给出本组织的差异化理由。

场景三:设计干部选拔与晋升

  • 参考华为式制度工程:明确领导力模型(如干部九条)、选拔优先级(优秀团队、一线艰苦地区、责任结果好)、答辩机制逼干部提炼方法论(腾讯式),避免"凭手感做匪帮"。
  • 职级信息保持透明——职级承载责权分配,隐藏它会造成人力工作塌陷。

场景四:诊断考核流于形式

  • 追查三个信号源:钱的流向、晋升的流向、被辞退者的真实原因——与制度文本的差距就是病灶。
  • 识别"老板的心理按摩"型制度:只服务管理者存在感、催生对齐会议食利阶级的流程,建议直接砍掉。

Instruction Examples

  • 用户:"我们想学字节搞 OKR。" -> 先祛魅(字节成功归因于战略眼光),确认弱考核前提,不满足则建议留在 KPI 并做好指标质量。
  • 用户:"帮我盘点一下 20 人的技术团队。" -> 九宫格双维打分表 + 每格的具体人事动作 + 复核机制。
  • 用户:"公司价值观考核大家都打 3.5 分,怎么办?" -> 指出考不出分离度是常态,改用资源流向表达价值观,考核回归业绩与行为红线。
  • 用户:"晋升总被吐槽不公平。" -> 检查职级透明度与答辩机制,引入结构化标准与第二评估人,减少"手感打分"。

详细论据与一线批注见 notes/大厂人才_笔记.md;激励结构的信号分析可配合 incentive-design 技能。

Field Notes (实战修正)

暂无。技能在实战中暴露的偏差会以 - YYYY-MM-DD: 经验内容 格式追加到本章节。

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 51 lines · 92 tokens per session scan A c0734fe4f0a7

Subscribe to this mod's changes

talent-assessment is a skill published in the GitHub repository kuhung/weread-book-skills (9 stars, last pushed 1mo ago), licensed MIT. It adds 92 tokens to every session and 1,278 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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