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Install in OpenClaw
/install shou-zhuo
Description
守拙 — 中国顶级基金经理虚拟心智模型。 基于朱少醒(16年20%年化)、曹名长(深度价值)、丘栋荣(低估值+成长)、 冯柳(逆向投资赔率模型)、董承非(风控优先)、张坤(巴菲特式长期持有)、 谢治宇(均衡配置)七大顶级经理人的共同素质,提炼为一个完整的投资心智系统。 用途:以"守拙"的视角分析A股/港股投资决策...
Usage Guidance
This skill is internally coherent: it simply instructs the agent to act as a conservative Chinese value-investing persona and includes decision checklists. Before installing, consider: (1) provenance — source is unknown, so verify the author if you need attribution or legal certainty; (2) financial risk — outputs are investment guidance, not guarantees; treat it as a decision-support tool and validate with your own research or a licensed advisor; (3) data sources — the skill doesn't fetch market data itself, so ensure the agent has access to reliable prices/financials if you expect up-to-date analysis; (4) privacy — although this skill doesn't request credentials, avoid pasting sensitive account credentials into prompts. If you want the skill to automatically fetch market data later, require that any added code or configuration be reviewed for network endpoints and credentials first.
Capability Analysis
Type: OpenClaw Skill
Name: shou-zhuo
Version: 1.0.0
The skill bundle consists solely of a persona definition (SKILL.md) and metadata (_meta.json). It instructs the AI agent to adopt a 'value investing' mental model based on prominent Chinese fund managers for financial analysis. There is no executable code, no network activity, and no instructions that encourage data exfiltration or unauthorized system access.
Capability Assessment
Purpose & Capability
Name/description (a Chinese value-investing mental model) align with the content of SKILL.md. The skill is instruction-only and does not request credentials, binaries, or access inconsistent with financial-analysis persona behavior.
Instruction Scope
Runtime instructions are limited to adopting an investment persona, applying specific heuristics and checklists, and answering queries about A-share/HK investments. The SKILL.md does not instruct reading local files, using environment variables, or sending data to external endpoints, nor does it grant broad discretionary data collection.
Install Mechanism
No install specification or code files are included; this is the lowest-risk model (instruction-only). Nothing will be written to disk or downloaded as part of installation.
Credentials
The skill requires no environment variables, credentials, or config paths. There are no unexplained secret requests or cross-service credentials that would be disproportionate to the stated purpose.
Persistence & Privilege
always is false (default) and disable-model-invocation is false (normal). The skill does not request persistent system-wide changes or access to other skills' configuration. Autonomous invocation is allowed by default but not combined with other concerning privileges.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install shou-zhuo - After installation, invoke the skill by name or use
/shou-zhuo - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
v1.0.0: 初始版本,融合朱少醒/曹名长/丘栋荣/冯柳/董承非/张坤/谢治宇七大顶级经理人心智模型,含6大核心投资心智、10条决策清单
Metadata
Frequently Asked Questions
What is 守拙 — 中国基金经理心智模型?
守拙 — 中国顶级基金经理虚拟心智模型。 基于朱少醒(16年20%年化)、曹名长(深度价值)、丘栋荣(低估值+成长)、 冯柳(逆向投资赔率模型)、董承非(风控优先)、张坤(巴菲特式长期持有)、 谢治宇(均衡配置)七大顶级经理人的共同素质,提炼为一个完整的投资心智系统。 用途:以"守拙"的视角分析A股/港股投资决策... It is an AI Agent Skill for Claude Code / OpenClaw, with 103 downloads so far.
How do I install 守拙 — 中国基金经理心智模型?
Run "/install shou-zhuo" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 守拙 — 中国基金经理心智模型 free?
Yes, 守拙 — 中国基金经理心智模型 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does 守拙 — 中国基金经理心智模型 support?
守拙 — 中国基金经理心智模型 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created 守拙 — 中国基金经理心智模型?
It is built and maintained by CatPluZ (@catplus-eric); the current version is v1.0.0.
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