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 ace3000chao/book2startup --skill a-player-definitiongit clone --depth 1 https://github.com/ace3000chao/book2startupWrote 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/ace3000chao/book2startup/a-player-definition)<a href="https://agentmods.dev/skills/ace3000chao/book2startup/a-player-definition"><img src="https://agentmods.dev/badge/skills/ace3000chao/book2startup/a-player-definition.svg" alt="Measured on agentmods" 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 | $0.00127 | $0.02932 |
| Opus 5 | $0.00063 | $0.01466 |
| Sonnet 5 | $0.00025 | $0.00586 |
| Haiku 4.5 | $0.00013 | $0.00293 |
Grade A, and why
A Player定义（A类人才识别标准） 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A Player定义（A类人才识别标准）
R — 原文 (Reading)
"An A Player is someone in the top 10% of the available talent pool who is willing to accept your specific offer. You need to attract the largest and most capable talent pool excited about the job and willing to accept your compensation package."
— Verne Harnish, Scaling Up, 第4章 The Team
中文翻译：A类人才是可用人才池中排名前10%且愿意接受你特定offer的人。你需要吸引最大、最有能力的候选人才池，让他们对这份工作感到兴奋，并愿意接受你的薪酬方案。
I — 方法论骨架 (Interpretation)
A类人才的定义包含两个不可分割的条件：客观能力（人才池前10%）+ 主观意愿（接受你的offer）。缺一不可。
为什么这个定义重要？
传统招聘的误区是只关注"这个人厉不厉害"，而不问"他愿不愿意来"或"他来了能待多久"。结果是：你以为招到了A类，但他最终没来或来了很快走——实际产出等于零。
两个维度的拆解：
- "人才池"决定分母 — 你在哪找？面向什么群体？同样是销售，招聘本地小企业主和招聘上市公司CFO，人才池完全不同，10%的门槛含义也完全不同
- "特定offer"决定可行性 — A类人才有多个选项，你的offer能否打动他，不只是薪酬，还包括成长机会、文化、工作内容、地点
A类 vs B/C类的本质区别：
- 一个A类人才 = 三个B/C类人才的生产力
- B类人才在A类人才手下工作会成为更好的自己；C类人才在任何环境下都会拖累团队
- 留住A类人才的方法：给他足够挑战、足够自主、足够回报
九宫格工具（配套）：
- 横轴：执行力（现在能做什么）
- 纵轴：成长性（未来能做什么）
- 右上角（A类）：高执行×高成长 → 培养晋升
- 左上角（B类）：高成长×低执行 → coaching或调岗
- 右下角（伪A）：高执行×低成长 → 可能是专业杀手，需善用
- 左下角（C类）：低执行×低成长 → 快速决策（调岗或离开）
A1 — 书中的应用 (Past Application)
案例 1: 韦尔奇与GE的人才密度革命
- 问题: GE拥有30万员工，但大量中庸之辈占据管理岗位，导致组织臃肿、创新乏力
- 方法论的使用: 韦尔奇推行"人才九宫格"和强制排序：每年对所有管理者进行横向比较，强制将20%评为明星（A类），70%中坚（B类），10%垫底（C类）
- 结论: 人才密度是竞争优势的来源——不是某个A类人才的贡献，而是整体人才质量让GE强大
- 结果: 韦尔奇时代GE市值从130亿增长到4800亿，人才文化影响全球管理界
案例 2: 硅谷初创公司"聪明但不对"的人才陷阱
- 问题: 一家AI创业公司招聘了大量"顶尖名校+顶级公司背景"的工程师，履历光鲜但项目交付持续延期
- 方法论的使用: 创始人开始追问：这些人是"人才池"的前10%，还是只是"看起来像前10%"？发现很多人是因为公司品牌背书而成功，而非个人能力。重新定义人才池：不是"常春藤毕业生"，而是"在约束条件下交付过可量化结果的10%"
- 结论: 品牌背书≠个人能力，必须在相同的成功标准下比较
- 结果: 裁员30%，留下的A类人才产出提升3倍
案例 3: 餐饮连锁的区域扩张人才判断
- 问题: 一家连锁餐饮向新城市扩张，需要任命新城市负责人。两个候选人：一个是在大城市有10年经验的外地人，一个是在本地工作5年的本地人
- 方法论的使用: 用人才池定义：谁在"可接受这个岗位的候选人"里是前10%？外地候选人：全国有类似经验的人约5000人，本地候选人：本地有这个年限的人约200人。如果要求全国视野，外地候选人在更大人才池里排名前10%；如果要求本地资源整合，本地候选人在小池子里可能只是前30%。取决于真正的岗位需求
- 结论: 人才池的划定决定谁才是A类，不同标准不同答案
- 结果: 明确岗位需求后选择外地候选人，新城市首年达标率从40%提升至85%
A2 — 触发场景 (Future Trigger) ★
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 174 lines · 127 tokens per session scan A d8ea1541921c
A Player定义（A类人才识别标准） is a skill published in the GitHub repository ace3000chao/book2startup (76 stars, last pushed 4mo ago), licensed MIT. It adds 127 tokens to every session and 2,932 once invoked, about $0.0006 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 2026-08-30.
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