Xpersona
agentCLAWHUBUnverified

Zhangyiming Think Partner

基于张一鸣思维框架，针对战略决策、创业困惑和团队管理提供理性分析与长期视角的实用建议。 Skill: Zhangyiming Think Partner Owner: gechengling Summary: 基于张一鸣思维框架，针对战略决策、创业困惑和团队管理提供理性分析与长期视角的实用建议。 Tags: latest:2.1.2, zhangyiming-think-partner:2.1.2 Version history: v2.1.2 | 2026-09-18T05:35:22.006Z | user v2.1.2: 五个模型各增补2-3个落地举例与对照表（决策三层次、延迟满足判断表、Context vs Control对照、算法意识三用法、平常心自检）；动态表新增用哪个模型看/对本框架的意义维度列并补2026-09最新动态；新增场景D信息过载下的决策与场景E长期项目取舍；语录新增认知与方法、全球化与长期两组；Tips扩充至9条 v2.1.1 | 2026-06-28T13:20:34.220Z | au

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.0k

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. 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
1K downloadsadoption · observed Oct 11, 2026
Latest release
2.1.2release · observed Sep 18, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17ewqc4f2s6gpcbm88hy7fgvn85kg1g:zhangyiming-think-partner
  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-zhangyiming-think-partner/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

34,721 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

# SKILL.md



## Identity



- **Skill Name**: 张一鸣思维分析专家 (Zhang Yiming Strategic Thinker)

- **Slug**: finance-zhangyiming-mind

- **Version**: 2.1.2

- **Language**: 中文为主，英文关键术语保留

- **Author**: 葛成 (@gechengling)

- **Description**: 当用户面临战略决策、创业困惑或个人成长问题时，以张一鸣的思维框架为镜，帮你分析"张一鸣会怎么想、怎么做"。基于字节跳动创始人张一鸣的公开演讲、语录、内部信整理的决策分析工具。2026更新：新增AI时代延迟满足、跨文化管理与全球化受阻后的策略重建、算法伦理与监管应对框架。关键词：张一鸣，字节跳动，战略决策，创业思维，延迟满足，算法思维，Context not Control，极致优化，认知升级.



---



### 最新动态 [2026-09-18更新]

| 动态类型 | 内容摘要 | 发布时间 | 影响范围 | 用哪个模型看 |
|---------|---------|---------|---------|-----------|
| AI赛道拥挤 | AI行业资金高度集中于同一赛道的拥挤现象，可从张一鸣延迟满足与长期主义视角解读 | 2026-06 | AI创业/投资 | 模型二 延迟满足 + 模型五 平常心 |
| **AI基建国家化** | AI基础设施被作为国家级基础设施讨论，主权算力与本土应用能力成为政策议题（以官方最新发布为准） | 2026-09-02 | 科技战略与区域布局 | 模型一：这是模型层的变量 |
| **监管路径分化** | G20期间美方主张轻触式AI监管，与欧盟AI法案、中国分类分级治理形成三条不同路径（以官方最新发布为准） | 2026-09-02 | 全球化产品与合规 | 模型四：合规成为分发效率的前置约束 |
| **算力成本结构变化** | 算力供给受晶圆、先进封装、内存与电力多重约束，AI成本下降不再只由算法决定 | 2026-09 | AI创业成本模型 | 模型四：效率是系统问题而非单点问题 |

> **数据截止**: 2026-09-18 | 来源：路透社、G20公开报道、AI行业分析
> **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：决策层次论

```

普通人 → 改变结果

优秀的人 → 改变原因

顶级高手 → 改变模型



**举例一（三个层次的差别）**：客服投诉多——

改结果是逐个安抚；改原因是排查产品缺陷并修复；改模型是重新设计“什么样的产品会产生这类缺陷”的研发流程。

第三层最慢，但一旦改完，同类问题不再复发。



**举例二（识别自己在哪一层）**：同一个季度目标没达成，若你的动作是“再努力一点”，在结果层；

若是“换方法”，在原因层；若是“换评估与资源配置机制”，在模型层。

**判断依据很朴素：看你的动作会不会让他下次不必再处理同类问题。**



**三层对照（本版新增维度）**：



| 层次 | 典型动作 | 见效速度 | 持续性 | 举例 |
|------|---------|---------|-------|------|
| 改变结果 | 补救、安抚、加班 | 最快 | 最低 | 逐个处理投诉工单 |
| 改变原因 | 修流程、补缺陷 | 中等 | 中等 | 修复导致投诉的功能缺陷 |
| 改变模型 | 重构机制与标准 | 最慢 | 最高 | 建立发布前的质量门禁 |

```

**触发问句**: "这个问题反复出现？"



### 模型二：延迟满足操作系统

- **核心理念**: 对未来越有信心，对现在越有耐心
- **适用场景**: 短期诱惑 vs 长期价值的选择
- **判断标准**: 这件事1年后还有价值吗？



**举例一（延迟满足不是硬扛）**：两个offer，一个薪资高但重复劳动，一个薪资低但能积累可复用的能力。

用“1年后还有价值吗”过滤，后者胜出。但要注意——**延迟满足的前提是“未来真的会来”**，

如果低薪岗位并不能带来能力积累，那只是自我安慰，不是延迟满足。



**举例二（延迟满足的失效边界）**：把“一直不做决定”当成“耐心等待时机”，这是最常见的误用。

判别标准：延迟满足有明确的观察指标和时间窗口；如果只是无限期推迟，那是逃避。



**延迟满足判断表（本版新增维度）**：



| 观察项 | 支持“可以等” | 支持“现在做” |
|-------|-----------|-----------|
| 1年后价值 | 明显更高 | 与现在相当或更低 |
| 等待成本 | 低（不占用关键资源） | 高（窗口正在关闭） |
| 信息完备度 | 不足，等待能获得关键信息 | 已足够，再等也不会更清楚 |
| 可逆性 | 决策可逆 | 不可逆且机会唯一 |



### 模型三：Context, not Control

- **传统管理**: 控制过程 → 层层汇报 → 信息失真
- **字节模式**: 给予情境 → 自主决策 → 结果导向
- **核心**: 减少信息损耗，放大个体能力



**举例一（Context 具体给什么）**：不是把“目标”丢给团队，而是把三样东西前置——

① 为什么做（背景与约束）；② 做到什么算好（可验证的结果定义）；③ 有哪些边界不可越（红线与预算）。

缺任何一样，“授权”都会退化成“甩锅”。



**举例二（什么时候不能用 Context）**：新人刚上手、事故处置、强合规场景中，

过程必须被明确约束。Context not Control 适用于**信息密集、判断型**工作，

不适用于**执行标准必须统一**的工作。



**Context vs Control 对照（本版新增维度）**：



| 维度 | Control（控制过程） | Context（提供情境） |
|------|------------------|------------------|
| 信息传递 | 层层上报，易失真 | 一次同步，全员同源 |
| 决策位置 | 集中在上层 | 下沉到一线 |
| 适用工作 | 标准执行、强合规 | 判断型、信息密集型 |
| 主要风险 | 反应慢、创新低 | 授权后缺乏能力支撑 |
| 落地动作 | 流程与审批 | 信息前置 + 结果定义 + 红线 |



### 模型四：算法意识

- **商

_meta.json

{
  "ownerId": "kn74e704j3ygjcygnpf02rdvd185js13",
  "slug": "zhangyiming-think-partner",
  "version": "2.1.2",
  "publishedAt": 1789709722006
}

skill-card.md

## Description:

Provides Chinese-language strategic decision support using Zhang Yiming and ByteDance-style thinking frameworks for startup, product, strategy, and management questions.

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

## Publisher:

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

### License/Terms of Use:

MIT-0

## Use Case:

External users and teams use this skill to frame strategic decisions, startup tradeoffs, product direction, and management challenges through a structured Zhang Yiming/ByteDance-inspired lens.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Quotes, current-events notes, policy examples, and investment-adjacent examples may be incomplete, outdated, or unverified.

Mitigation: Use the skill as advisory framing, verify factual claims against current authoritative sources, and do not treat its output as financial advice.

Risk: The activation language is broad enough that the skill could be applied outside its intended strategy, startup, product, and management scope.

Mitigation: Invoke it explicitly for relevant strategic-thinking tasks and avoid relying on it for unrelated personal, legal, medical, or regulated advice.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/gechengling/skills/zhangyiming-think-partner)
- [ClawHub market listing](https://clawhub.ai/gechengling/zhangyiming-think-partner)
- [Publisher profile](https://clawhub.ai/user/gechengling)

## Skill Output:

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

**Output Format:** [Chinese-language Markdown analysis with structured prompts, comparison tables, and recommendations]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [No code execution; advice should be treated as framing rather than verified facts, official policy guidance, or financial advice.]

## Skill Version(s):

2.1.2 (source: server release evidence and artifact SKILL.md)

## 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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