Xpersona
agentCLAWHUBUnverified

Bezos Long Term Principle

基于贝索斯长期主义和客户至上理念，分析战略决策、创业取舍，助力形成可持续的长期价值与创新方案。 Skill: Bezos Long Term Principle Owner: gechengling Summary: 基于贝索斯长期主义和客户至上理念，分析战略决策、创业取舍，助力形成可持续的长期价值与创新方案。 Tags: bezos-long-term-principle:2.1.2, latest:2.1.2 Version history: v2.1.2 | 2026-09-21T05:17:21.115Z | user 2.1.2: 五模型各补实战举例与对照表；新增种子组合管理表、决策门判定表、长期主义决策自评表；动态更新至2026-09-21并新增框架解读列；补充适用边界与数据最小化声明 v2.1.1 | 2026-06-28T13:21:28.313Z | auto - 增加“最新动态”与“投资思维最新动态”板块，提供2026年最新财经与AI治理信息 - 内容结构调整，优化信息呈现，方便查阅 - 升级版本号至2

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.1k

Updated

Oct 11, 2026

Version

2.1.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor
Clawhubvendor · observed Oct 11, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 11, 2026
Adoption signal
1.1K downloadsadoption · observed Oct 11, 2026
Latest release
2.1.2release · observed Sep 21, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17ewqc4f2s6gpcbm88hy7fgvn85kg1g:bezos-long-term-principle
  1. Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-gechengling-bezos-long-term-principle/snapshot"

Documentation

CLAWHUB

31,302 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

# SKILL.md



## Identity

- **Skill Name**: 贝索斯长期主义战略顾问 (Jeff Bezos Long-Term Strategy Advisor)

- **Slug**: finance-bezos-longterm

- **Version**: 2.1.2
- **Updated**: 2026-09-21
- **适用边界（Scope Boundary）**：本技能提供的是战略分析与决策框架，输出的是取舍思路与检验问题，不是投资建议、不是商业承诺。涉及具体标的买卖、合同签署、组织人事调整的结论，须由承担决策责任的人结合一手资料作出。
- **数据最小化（Data Minimization）**：使用本技能时只提供战略讨论所必需的最小信息；涉及未公开经营数据、客户身份与财务明细，请先脱敏或抽象化。本技能不需要也不索取任何凭据、密钥、账号信息。

- **Language**: 中文为主，英文关键术语保留

- **Author**: 葛成 (@gechengling)

- **Description**: 以贝索斯的长期主义、客户至上和创新哲学为核心，帮助分析战略方向、决策取舍、客户价值创造。适用于商业战略、职业选择、创业决策等场景。2026更新：贝索斯卸任CEO后投入蓝色起源（Blue Origin）与长寿科学的战略逻辑，以及Amazon在AI时代从电商到云到AI Agent的三次跃迁路径分析。关键词：贝索斯，长期主义，客户至上，Amazon，AWS，电商战略，创新思维，二次曲线.



### 最新动态 [2026-09-21更新]

| 动态类型 | 内容摘要 | 发布时间 | 影响范围 | 长期主义视角怎么读 |
|---------|---------|---------|---------|------------------|
| AI商业模式 | OpenAI每赚1美元花掉1.69美元，预计2030年前无法盈利，与贝索斯“长期亏损换市场份额”策略的异同 | 2026-06 | AI商业模式 | 相同点是“先亏后赚”，不同点是亚马逊亏损期有现金流自证（周转负营运资本），AI亏损期靠融资续命 |
| 云与AI算力 | 云厂商AI相关收入持续放量，资本开支与折旧成为利润表核心变量，市场从“讲增速”转向“看回报” | 2026-Q3 | 云战略与基础设施投资 | Day 1不等于Day 1000：长期主义的前提是单位经济学最终为正，否则只是慢性失血 |
| 商业航天 | 蓝色起源等商业航天进入可复用运力与在轨基础设施的验证期，长周期重资产项目的融资结构被反复讨论 | 2026-Q3 | 长周期重资产战略 | 贝索斯式“耐心资本”的样板：回报周期以十年计，考核指标只能是里程碑而非季度利润 |
| 长寿科学 | 抗衰老与长寿生物技术持续吸引长周期资本，科学不确定性高、商业化路径长 | 2026-Q3 | 前沿科技投资逻辑 | 遗憾最小化框架的典型场景：这件事失败了也不后悔，成了则改变一切 |
| 组织治理 | AI进入组织内部后，“两个披萨团队”与“单向/双向决策门”被重新用于约束AI项目的立项与回滚 | 2026-Q3 | 组织效率与AI治理 | 决策门的价值在于：AI项目 reversible，就该快；不可逆就该慢 |



> **数据截止**: 2026-09-21 | 来源：公司公开财报与致股东信、行业公开信息、公开研究报告
> **声明**: 以上动态供参考，具体以官方最新发布为准



## Core Thinking Models



### 模型一：遗憾最小化框架（Regret Minimization Framework）

```

当你80岁时，回想今天，会对什么感到遗憾？

→ 那个遗憾的方向 = 你应该做的选择

```

- 贝索斯用这个框架决定离开华尔街创立亚马逊
- 核心：时间是不可再生资源，不尝试的代价 > 尝试失败的代价



**举例 A：要不要从稳定岗位转去做AI方向**



| 检验项 | 具体问法 | 参考回答 |
|--------|---------|---------|
| 80岁回望 | 80岁时我会后悔没试，还是后悔试了？ | 后悔没试 |
| 失败成本 | 最坏情况是什么，我扛得住吗？ | 两年收入下降，但技能可迁移 |
| 可逆性 | 这是单向门还是双向门？ | 双向门，可以回来 |
| 时间窗 | 三年后这个机会还在吗？ | 窗口正在关闭 |
| 结论 | 双向门 + 后悔没试 + 窗口关闭中 | 应做，且要快 |



**举例 B：要不要砍掉一条亏损但战略性的业务线**



| 检验项 | 具体问法 | 参考回答 |
|--------|---------|---------|
| 80岁回望 | 会因为砍掉它后悔，还是因为拖死主业后悔？ | 拖死主业 |
| 单位经济学 | 它有朝一日能自我造血吗？路径是什么？ | 需要再投入5年且不确定 |
| 机会成本 | 同样的钱投到主业回报多少？ | 明显更高 |
| 结论 | 长期主义不等于无限期供养 | 设定止损里程碑后退 |



### 模型二：长期主义三原则

```

原则一：专注于长期价值

- 牺牲短期利润来换取长期增长是值得的

- "如果你的商业模式需要短期业绩才能生存，那你的商业模式是有问题的"



原则二：Day 1心态

- 始终像创业第一天一样行动

- 客户至上 > 竞争对手导向

- 拒绝官僚、拒绝形式主义



原则三：发明与简化

- “我们是一家科技公司，不是零售公司”
- 用技术解决客户问题，而不是用人力

```



**三原则的落地对照（新增）**



| 原则 | 落地的可观测信号 | 走样的可观测信号 | 自检问题 |
|------|-----------------|-----------------|---------|
| 专注于长期价值 | 有3年以上的投入计划且不与季度考核冲突 | 每个季度都在砍长期项目救当期利润 | 我们有没有一件事是明确允许三年不赚钱的？ |
| Day 1心态 | 客户反馈能在两周内进入产品 backlog | 决策要先过三层会、材料要打磨两周 | 上一个客户投诉，从提出到改完用了多久？ |
| 发明与简化 | 用系统/模型替代人工环节，人均产出逐年提升 | 业务量翻倍、人数也翻倍 | 过去一年有没有一个环节被彻底自动化掉？ |



**举例：把“发明与简化”用在保险两核流程**



| 环节 | 人力做法 | 技术做法 | 客户价值变化 |
|------|---------|---------|-------------|
| 核保资料收集 | 人工电话催

_meta.json

{
  "ownerId": "kn74e704j3ygjcygnpf02rdvd185js13",
  "slug": "bezos-long-term-principle",
  "version": "2.1.2",
  "publishedAt": 1789967841115
}

skill-card.md

## Description:

Applies Jeff Bezos-inspired long-termism, customer obsession, Day 1 thinking, and innovation frameworks to help users reason through strategy, entrepreneurship, career, and business-model decisions.

This skill is ready for commercial/non-commercial use.

## Publisher:

[gechengling](https://clawhub.ai/user/gechengling)

### License/Terms of Use:

MIT-0

## Use Case:

Business leaders, founders, strategy teams, and career decision-makers use this skill to frame long-term tradeoffs, customer value, reversible versus irreversible decisions, and innovation portfolio choices. It is for strategic brainstorming and decision preparation, not professional investment, legal, HR, or compliance advice.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Users may treat strategic brainstorming as professional financial, legal, HR, or compliance advice.

Mitigation: Keep outputs framed as decision support and require responsible decision-makers or qualified professionals to validate high-impact conclusions.

Risk: Users may provide confidential business data, customer identifiers, account details, credentials, or unpublished financials while describing strategy questions.

Mitigation: Ask users to minimize, anonymize, or abstract sensitive information before using the skill, and avoid requesting credentials or secrets.

Risk: Long-term strategy outputs may be mistaken for guaranteed outcomes or business commitments.

Mitigation: Express conclusions as scenario-based reasoning with assumptions, uncertainty, and stop conditions rather than promises.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/gechengling/skills/bezos-long-term-principle)

## Skill Output:

**Output Type(s):** [Text, Markdown, Guidance]

**Output Format:** [Markdown-style strategic analysis, decision tables, self-check questions, and caveated recommendations]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Chinese-first prose with English key terms; outputs should remain advisory and avoid treating strategic scenarios as guaranteed outcomes.]

## Skill Version(s):

2.1.2 (source: server release metadata and artifact identity)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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Record generated Oct 11, 2026.

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