← Back to Skills Marketplace
luokeer52

getmeastock

by luokeer52 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
99
Downloads
0
Stars
0
Active Installs
1
Versions
Install in OpenClaw
/install getmeastock
Description
全面的A股股票分析工具,提供7大核心模块(核心财务、技术指标、估值分析、股东持股、市场热度、券商盈利预测、K线图表)+ 财报、公告、新闻资讯和交易所互动问答的多维度分析。
README (SKILL.md)

运行方式(任选其一;OpenClaw 等渠道可自行选用 Python 或 Node):

  • Python 3python3 scripts/prana_skill_client.py -m "…" [-t thread_id] [-b base_url]
  • Node.js 18+:先在包根目录执行 npm install,再执行 node scripts/prana_skill_client.js -m "…" [-t thread_id] [-b base_url]

付费技能说明

若本技能为付费技能,支付成功后请访问 https://claw-uat.ebonex.io/api/order/skills 获取购买记录。

鉴权与调用 Claw API 相同:请求头 x-api-key,值为 public_key:secret_key(一个英文冒号连接,与 config/api_key.txt 中单行凭证格式一致)。

Usage Guidance
This package is a thin client that forwards user input to a remote Prana/Claw service and will attempt to obtain and persist API credentials locally. Before installing or running it: 1) Verify the remote base URL (default is https://claw-uat.ebonex.io/) — ensure you trust that endpoint or override NEXT_PUBLIC_URL to a known production endpoint. 2) Prefer setting PRANA_SKILL_PUBLIC_KEY and PRANA_SKILL_SECRET_KEY (or PRANA_SKILL_API_KEY) instead of relying on the client's auto-fetch. 3) If you do allow auto-fetch, set PRANA_SKILL_SKIP_WRITE_API_KEY=1 to avoid persisting secrets to disk, or run in an isolated environment/VM. 4) Do not commit config/api_key.txt to source control. 5) Review the embedded ENCAPSULATION_EMBEDDED skill_key (getmeastock / getmeastock_public) and confirm it matches the service you expect. 6) Avoid running npm install / executing scripts from untrusted origins; inspect the scripts if provenance is unclear. If you need higher assurance about where data goes or who controls the backend, ask the skill provider for a verified homepage/origin and a production endpoint.
Capability Analysis
Type: OpenClaw Skill Name: getmeastock Version: 1.0.0 The skill bundle acts as a 'thin client' designed to forward user queries to a remote stock analysis service at claw-uat.ebonex.io. The scripts (scripts/prana_skill_client.py and scripts/prana_skill_client.js) manage API authentication by reading from environment variables or a local configuration file (config/api_key.txt), and include a feature to auto-fetch and save API keys if they are missing. While the scripts require network and filesystem permissions, their behavior is transparent, well-documented, and strictly aligned with the stated purpose of providing an encapsulated interface to a remote AI agent.
Capability Assessment
Purpose & Capability
The skill description promises A-share analysis but the bundle is a 'prana' encapsulation that contains no business logic locally; instead the provided Python/Node thin clients forward user requests to a remote service (using an embedded skill_key). That design is coherent for an encapsulated skill.
Instruction Scope
SKILL.md instructs running the thin client (Python or Node). The clients will read environment variables, read/write config/api_key.txt, auto-fetch API keys via GET /api/v1/api-keys, and POST user messages and skill_key to the remote Claw/Prana API. These operations are expected for a remote-executed skill but they do transmit user inputs and may persist credentials locally — users should be aware of that data flow.
Install Mechanism
No complex install spec; Node runner requires npm install to get a small 'yaml' dependency. No downloads from arbitrary URLs or extract operations are present in the package. This is low-to-moderate install risk, typical for a Node thin client.
Credentials
No required env vars are declared, but the scripts optionally use many environment variables (NEXT_PUBLIC_URL, PRANA_SKILL_PUBLIC_KEY/SECRET_KEY/PRANA_SKILL_API_KEY, ACCOUNT_ID, flags to disable auto-fetch or prevent writing to disk). The optional envs are relevant to credential management and endpoint selection, but the default behavior will attempt an unauthenticated GET to a default base URL to obtain API keys and will write credentials to config/api_key.txt unless configured otherwise — this is powerful and worth review.
Persistence & Privilege
The skill is not always-enabled and does not request elevated platform privileges, but both clients will persist API credentials into config/api_key.txt by default and will perform network calls. Persisting secrets to disk and automatic fetching of keys increases persistence of sensitive data on the host and should be considered by the user.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install getmeastock
  3. After installation, invoke the skill by name or use /getmeastock
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of the "give-me-a-stock" skill (全面的A股股票分析工具) - Provides 7 core modules: Core Financials, Technical Indicators, Valuation Analysis, Shareholder Holdings, Market Sentiment, Brokerage Forecasts, and K-Line Charts - Supports multi-dimensional analysis with financial reports, announcements, news, and Q&A from exchanges - Usage supported via Python 3 or Node.js 18+ - Includes authentication instructions for paid skill users
Metadata
Slug getmeastock
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is getmeastock?

全面的A股股票分析工具,提供7大核心模块(核心财务、技术指标、估值分析、股东持股、市场热度、券商盈利预测、K线图表)+ 财报、公告、新闻资讯和交易所互动问答的多维度分析。 It is an AI Agent Skill for Claude Code / OpenClaw, with 99 downloads so far.

How do I install getmeastock?

Run "/install getmeastock" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is getmeastock free?

Yes, getmeastock is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does getmeastock support?

getmeastock is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created getmeastock?

It is built and maintained by luokeer52 (@luokeer52); the current version is v1.0.0.

💬 Comments