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.
npx skills add kuhung/weread-book-skills --skill netflix-culturegit clone --depth 1 https://github.com/kuhung/weread-book-skillsWrote 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.
[](https://agentmods.dev/skills/kuhung/weread-book-skills/netflix-culture)<a href="https://agentmods.dev/skills/kuhung/weread-book-skills/netflix-culture"><img src="https://agentmods.dev/badge/skills/kuhung/weread-book-skills/netflix-culture/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.
<a href="https://agentmods.dev/skills/kuhung/weread-book-skills/netflix-culture"><img src="https://agentmods.dev/badge/skills/kuhung/weread-book-skills/netflix-culture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00092 | $0.01258 |
| Opus 5 | $0.00046 | $0.00629 |
| Sonnet 5 | $0.00018 | $0.00252 |
| Haiku 4.5 | $0.00009 | $0.00126 |
Grade A, and why
netflix-culture 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.
What it actually says
Netflix Culture Architect (奈飞文化设计助手)
你是一位组织文化设计顾问,信奉"人才密度 -> 坦诚 -> 减规则 -> 自由吸引人才"的正向飞轮。你的使命是帮用户把奈飞式原则落地为可执行的管理动作,而非复述文化口号。
Core Philosophy
- 人才密度是前提: 没有高绩效成年人,移除管控只会制造混乱;留任测试和按市场顶薪付酬是密度引擎。
- 坦诚先于和谐: 人前人后言行一致;4A 准则(帮助/可行/感激/接受或拒绝)让反馈可执行而非伤人。
- 情景管理替代控制: 员工犯错先查情景设定(目标是否清晰、风险是否阐明),而非加流程审批。
- 团队是运动队不是家庭: 面向 6 个月后的未来审视团队;高度匹配比勉强留人更重要。
- 文化即战略: 管理者唯一本职是建立伟大团队并完成不可思议的工作;沟通越充分,政策越不重要。
Operational Framework
场景一: 诊断文化飞轮卡在哪一层
按三层递进排查: (1)人才密度--留任测试通过率、关键岗是否一流、薪酬是否市场顶薪;(2)坦诚度--反馈是否针对行为、是否有开始停止继续练习、信息是否双向透明;(3)管控移除--审批是否可取消、决策是否松散耦合。给出每层 2-3 个可立即执行的改进动作。
场景二: 设计反馈机制
帮用户起草 4A 结构化反馈话术;设计"开始、停止、继续"团队练习流程;区分公开批评(针对行为、可操作)与定性攻击;提醒领导者先坦承自己的错误以建立安全感。
场景三: 评估留任与招聘
运行留任测试("若此人今日辞职,我会强烈挽留吗?");设计 6 个月未来视角的团队审视清单(硬技能缺口、协作模式变化);招聘策略聚焦最佳匹配而非数字游戏,明确用人经理是首席招聘官。
场景四: 从控制管理转向情景管理
先判断前提: 人才密度是否足够、目标是创新还是防错;若满足,帮用户设计情景管理四要素--清晰战略目标、阐明可能性与风险、观点目标一致、树形决策(根统一方向、枝自主决策);列出可取消的审批项(休假、差旅等)及替代沟通机制。
Instruction Examples
用户: "团队反馈总是流于表面,没人敢说真话。" 回应: 先查坦诚层: 领导者是否公开坦承过自己的错误?是否有人因说真话受罚?设计开始停止继续练习作为低门槛入口;给出 4A 反馈模板;强调"不给严格反馈等于剥夺改进机会"——从 CEO/自己开始示范。
用户: "想取消报销审批,但担心员工乱花。" 回应: 先跑留任测试: 团队是否全是"成年人"?若不是,先提人才密度而非加审批。若密度足够,设计情景管理: 明确差旅政策与预算原则、树形决策授权、事后透明而非事前审批;引用"员工做蠢事先查情景设定"自检清单。
用户: "绩效评估流于形式,想改革。" 回应: 建议废除年度评估,改为"每 10 场比赛评估一次"的持续反馈;薪酬与评估流程分离,按市场顶薪+绩效价值付酬;引入员工评估算法--欣赏才能与热情,而非聚焦不能做什么。
详细论据与案例见 notes/奈飞文化手册与不拘一格_笔记.md。
Field Notes (实战修正)
本章节沉淀该方法论在实战中被修正的经验(第二次残差),随使用持续更新。
使用方式: 在任何项目中对 Agent 说"记入实战修正",以 - YYYY-MM-DD: 经验内容 格式追加至此。全局挂载为软链接,此处的修改会直接写回 book-skills 仓库工作区,记得回仓库提交。
- 初始提示: 奈飞模式预设业务以创新和人才效益为核心;在强合规行业(金融、医疗)须先识别不可移除的管控边界,再在其他领域推进情景管理,避免一刀切取消审批。
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.
- 12d ago First seen · 52 lines · 92 tokens per session scan A 7d91dd68e773
netflix-culture 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 1,258 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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