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在 OpenClaw 中安装
/install stock-tenbook-analysis
功能描述
A股个股十书全景分析法 v2.0。基于十本经典投资书籍的多维度分析框架,自动分类(A/B/C/D型)后选择对应分析路径。用于:(1) 用户发送股票名字+代码时自动触发全景分析 (2) 用户指定截止日时进行严格时间线回测 (3) 实时行情分析和操作建议。触发词:股票代码、股票名称、A股分析、十书分析、回测分析。示例...
安全使用建议
This skill appears internally consistent: it uses only public finance APIs and the included reference docs to produce analyses and backtests, and it does not ask for credentials or install code. Things to consider before enabling: (1) outputs are analyses not guaranteed advice — verify data and model conclusions before acting on them; (2) the skill fetches live data from third-party public endpoints (qt.gtimg.cn, sina finance, etc.); confirm those sources meet your accuracy/privacy requirements and your environment allows outbound HTTP to them; (3) backtest rules specify using unadjusted prices and only pre-cutoff financial reports — confirm that behavior matches your expectations for historical testing; (4) because the skill can be invoked by triggers and can call web_search, be mindful of what user-provided strings get sent to search services (sensitive/company-internal tickers or private notes may be exposed in queries). If you need stricter constraints (no web_search usage, whitelisted data sources, or audit logging), request those platform limits before installing.
功能分析
Type: OpenClaw Skill
Name: stock-tenbook-analysis
Version: 2.0.0
The skill bundle provides a comprehensive framework for A-share stock analysis based on established investment literature. It utilizes legitimate public financial APIs from Tencent (gtimg.cn) and Sina (sina.com.cn) to gather market and financial data. The instructions in SKILL.md and the reference files are strictly aligned with the stated purpose of stock evaluation and backtesting, with no evidence of malicious intent, data exfiltration, or unauthorized execution.
能力评估
Purpose & Capability
Name/description (A股十书全景分析法) align with what the skill does: it defines analysis paths, data sources, templates and backtest rules. It does not request credentials or unrelated system access that would be inconsistent with a stock-analysis skill.
Instruction Scope
Runtime instructions are limited to: classify company type, fetch market/financial data from public finance endpoints, apply frameworks from bundled reference files, produce vote tables/ratings, and perform backtests using only pre-cutoff data. The instructions do not ask the agent to read local files, environment secrets, or system-level configuration outside the included reference docs. They do instruct web_search and HTTP fetches to public endpoints (expected for this purpose).
Install Mechanism
No install spec and no code files beyond instruction/reference markdowns. Because it's instruction-only, nothing is written to disk or downloaded at install time — lowest-risk install model.
Credentials
The skill requires no environment variables, credentials, or config paths. All external data access is to public finance endpoints and web search; requested accesses are proportional to a stock analysis tool and no unexplained secrets are needed.
Persistence & Privilege
always is false (not force-installed) and the skill does not request elevated or persistent system privileges or modify other skills. Autonomous invocation is allowed by platform default but not combined with other high-risk factors here.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install stock-tenbook-analysis - 安装完成后,直接呼叫该 Skill 的名称或使用
/stock-tenbook-analysis触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v2.0.0
v2.0 initial release
元数据
常见问题
A股十书全景分析法 是什么?
A股个股十书全景分析法 v2.0。基于十本经典投资书籍的多维度分析框架,自动分类(A/B/C/D型)后选择对应分析路径。用于:(1) 用户发送股票名字+代码时自动触发全景分析 (2) 用户指定截止日时进行严格时间线回测 (3) 实时行情分析和操作建议。触发词:股票代码、股票名称、A股分析、十书分析、回测分析。示例... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 153 次。
如何安装 A股十书全景分析法?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install stock-tenbook-analysis」即可一键安装,无需额外配置。
A股十书全景分析法 是免费的吗?
是的,A股十书全景分析法 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
A股十书全景分析法 支持哪些平台?
A股十书全景分析法 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 A股十书全景分析法?
由 钱道(@w5208111111)开发并维护,当前版本 v2.0.0。
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