Face Accountability Chart

Q: What does the Face Accountability Chart AI skill do?

用户在组织架构混乱、职责不清、团队出现"这个事该谁负责"的冲突时； 或在问"我们公司缺什么样的人"/"为什么我们总是找不到对的人"/"领导层有没有在对的位置"时激活。 不适用于：个人岗位描述、基层员工绩效评估、早期2-5人创始团队。

Community

ace3000chao

face-accountability-chart

用户在组织架构混乱、职责不清、团队出现"这个事该谁负责"的冲突时；或在问"我们公司缺什么样的人"/"为什么我们总是找不到对的人"/"领导层有没有在对的位置"时激活。不适用于：个人岗位描述、基层员工绩效评估、早期2-5人创始团队。

Overview

Publisher	ace3000chao
Repository	`book2startup`
Skill name	`face-accountability-chart`
Stars	93
Forks	14
Bundled files	1
License	MIT
Links	Skill source Repository

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by ace3000chao on GitHub. Read the source before you install it.

Installation

Install the Face Accountability Chart AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/ace3000chao/book2startup/tree/master/ScalingUp-skills/face-accountability-chart
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/ace3000chao/book2startup/tree/master/ScalingUp-skills/face-accountability-chart

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/ace3000chao/book2startup.git /tmp/book2startup
mkdir -p .claude/skills
cp -r /tmp/book2startup/ScalingUp-skills/face-accountability-chart .claude/skills/face-accountability-chart

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Face Accountability Chart 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 Face Accountability Chart 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 Face Accountability Chart 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.

FACe 功能 Accountability Chart

R — 原文 (Reading)

"The FACe tool provides a list of seats (functions) that all organizations must fill. You want to delegate these functions to people who fit your culture and pass two tests: 1. They don't need to be managed. 2. They regularly wow the team with their insights and output."

— Verne Harnish, Scaling Up, 第3章

I — 方法论骨架 (Interpretation)

FACe（Function Accountability Chart，功能问责表）解决的是"对的人在对的位置上"的问题。

核心逻辑： Scaling Up的障碍之一是"瓶颈永远在瓶口"——组织顶层的功能如果没有合适的人来填，所有决策都堵在那里。FACe用一页纸列出企业必须填补的职能座位，并为每个座位指派KPIs和结果指标。

FACe的三个组成部分：

Seats（功能座位） — 企业必须填补的职能清单，包括：战略/创新、销售/市场、产品/服务、运营/交付、客户成功、财务/合规、人力/文化、技术/IT等
KPIs（关键绩效指标） — 每个功能每天/每周需要追踪的量化指标，让负责人知道自己是否在轨道上
Results/Outcomes（结果/成果） — 最终需要交付的财务或运营结果（如：Revenue/Gross Margin/Net Profit/Cash等）

两个测试决定谁坐在哪个座位：

测试1：他们不需要被管理（自我驱动）
测试2：他们经常用洞察和产出让团队惊叹

与组织架构图的区别： 组织架构图告诉你谁向谁汇报（层级）。FACe告诉你谁对什么结果负责（问责）。层级和问责可以不同——矩阵组织中尤其明显。

典型信号：

"这事我不知道该找谁"
"我们有重叠的职责"
"他明明是VP但产出不够"
"为什么我们总是错过截止日期"

A1 — 书中的应用 (Past Application)

案例1: Infusionsoft的FACe实践

问题: Clate Mask需要在快速扩张中保持组织清晰度
方法论的使用: 用FACe明确每个高管负责的结果（Revenue/Gross Margin/Cash），而不是头衔
结论: 每次会议都对照FACe讨论进展，确保没有遗漏
结果: 年度增长53%，获得5400万投资

案例2: 50人坎的FACe设计

问题: 企业从50人向100人扩张时，职能部门开始出现重叠和真空
方法论的使用: 在这个阶段，CEO需要坐下来画FACe，明确每个VP负责什么结果
结论: 矩阵组织中，职能负责人和业务单元负责人之间必须有清晰分工
结果: 避免"谁都管但谁都不负责"的状态

案例3: Dell招Shock Absorber

问题: Michael Dell在50人时意识到自己成为公司天花板
方法论的使用: 招了专门的HR负责人Barbara Kreisman作为他和组织之间的"缓冲器"
结论: 创始人需要把自己的职能也列在FACe上，然后找人来填
结果: Dell将公司从创始人依赖型变成组织驱动型，最终Scaling到40,000人

A2 — 触发场景 (Future Trigger) ★

用户会在什么情境下需要这个skill？

企业在30-50人时出现"谁负责什么"的混乱 — 业务扩张但架构没跟上，经常出现职责真空或重叠
招聘新高管时不知道该设什么KPI — 新VP进来后给什么指标衡量他？
高管会议变成"汇报表演"而非决策会议 — 因为没有清晰的问责，每个人只说自己想说的
团队出现"瓶颈在顶部"的症状 — 所有审批都到CEO那里，CEO成为决策堵点
并购或扩张后组织需要重新对齐 — 买了新公司，但不知道如何整合进现有组织

语言信号（用户的话里出现这些就应激活）

"这事到底该谁负责"
"我们有太多重叠的职能"
"他需要被管着才动"
"VP进来不知道该做什么"
"我们需要一个组织架构调整"
"为什么每次都卡在这里"
"谁是Revenue的负责人"
"这个KPI该归谁管"

与相邻skill的区分

与 pace-process-chart 的区别: FACe是纵向（职能/层级），PACe是横向（跨职能流程）。需要两者结合才能完整覆盖组织。
与 topgrading 的区别: FACe解决"谁坐哪个位置"，Topgrading解决"怎么找到对的人坐那个位置"。FACe在前，Topgrading在后。

E — 可执行步骤 (Execution)

为skill被激活后，按以下步骤执行：

列出企业必须填补的职能座位（Seats）
- 完成标准: 有8-12个功能清晰列出，包括：CEO(战略/文化)、Sales/Revenue、Marketing/Brand、Product/Service、Operations/Delivery、Finance/Accounting、HR/People、Technology/IT
- 不遗漏: 思考"我们的业务流程中哪些环节有人负责？"——没有列出的意味着真空
为每个座位指派KPIs（可量化指标）
- 完成标准: 每个功能有1-2个每周追踪的量化指标
- 指标类型: 时间（Cycle Time）、成本（Cost %）、质量（Defect Rate）、数量（Volume）
- 判停条件: 若某个功能找不到可量化的KPI，说明这个功能的定义不清晰，回去重写功能定义
为每个座位指派Results（财务结果）
- 完成标准: 每个功能对应一个或多个财务/运营结果（通常对应P&L上的行项目）
- 例子: CFO→Cash/DSO/Expense %；VP Sales→Revenue/Gross Margin；COO→Delivery Cost/Quality
识别"空座"并制定填补计划
- 完成标准: 明确标出哪些座位目前有人坐、哪些是空座、哪些坐的人不对
- 判停条件: 若"坐的人不对"，启动Topgrading流程找对的人替换
季度回顾FACe
- 完成标准: 每季度对照FACe复盘，确认每个座位的表现
- 判停条件: 若某个座位连续两个季度表现差，启动替换或培训流程

B — 边界 (Boundary) ★

不要在以下情况使用此skill

2-10人早期创始团队 — 每个人都是多面手，FACe的精细分工反而限制灵活性
个人岗位描述或绩效评估 — FACe是高管层的组织工具，不适合用于基层员工日常考核
试图用它解决所有组织问题 — FACe只解决"谁对什么负责"，不解决"流程怎么跑"（那是PACe的工作）

作者在书中警告的失败模式

把FACe做成职位描述清单 — FACe强调的是结果和KPI，不是"每天做什么"
不更新的FACe — 市场在变，FACe每季度必须更新一次
只有人没有KPIs — "有人坐在那里"不等于"那个功能在运转"，必须有KPI追踪

作者的盲点/时代局限

远程/分布式组织: 书中主要讨论的是实体组织，远程团队的功能分工可能有不同逻辑
平台型组织: 平台+生态的模式下，"座位"的定义可能变成"内部创业者"而非传统职能

容易混淆的邻近方法论

OKR: OKR是个人/团队目标工具，FACe是组织级问责工具。OKR服务于FACe，而非相反
组织架构图: 组织图是层级汇报图，FACe是结果问责图。看组织图知道"谁向谁报告"，看FACe知道"谁对什么结果负责"

审计信息

验证通过: V1 ✓ / V2 ✓ / V3 ✓
测试通过率: 待阶段4测试
蒸馏时间: 2026-04-26
示范批次: 5个示范SKILL.md之一

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

test-prompts.json3 KB

Frequently asked questions

What does the Face Accountability Chart AI skill do?

Why use Face Accountability Chart on TypingMind?

Because you install it once and use it with any model. Face Accountability Chart 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 Face Accountability Chart in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ace3000chao/book2startup/tree/master/ScalingUp-skills/face-accountability-chart. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Face Accountability Chart?

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 Face Accountability Chart?

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

Is the Face Accountability Chart AI skill free?

Yes. It is published on GitHub by ace3000chao under the MIT license. 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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Face Accountability Chart

Overview