Shiwei is a comprehensive 10-dimension financial analysis system designed for AI agents to evaluate stock fundamentals, mitigate investment risks, and identify high-value long-term investment targets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install shiwei
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install shiwei using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Shiwei (Ten Dimensions Stock Financial Analysis)?
The Shiwei Stock Value Judgment System is a highly structured, developer-friendly financial analysis framework built to empower AI agents with deep fundamental analysis capabilities. By operating on the core principle of "layered progression, from surface to core" (层层递进、由表及里), Shiwei guides AI workflows through a rigorous 10-dimensional evaluation process. It systematically inspects a company's survival capacity, quality of earnings, track competitiveness, and valuation levels before delivering a final standardized scorecard.
Optimized for growth and blue-chip stock evaluation, this system is exceptionally adept at identifying potential bottom-reversal targets after prolonged declines. By integrating this workflow into your AI agent platforms through Openclaw Skills, you can automate institutional-grade stock screening, financial red-flag detection, and intrinsic value estimations without relying on subjective biases.
Shiwei (Ten Dimensions Stock Financial Analysis) Use Cases
- Automated Financial Due Diligence: Scan target equities across 10 vital financial dimensions to detect anomalies, earnings manipulation, or hidden solvency risks.
- Bottom-Reversal Stock Screening: Identify high-quality growth or blue-chip stocks that have experienced prolonged price declines but maintain solid business fundamentals.
- Institutional-Grade Valuation Benchmarking: Conduct multi-dimensional peer reviews, comparing ROE, operating cash flows, and operating margins against core industry rivals.
- Automated Risk Alerting & Scoring: Automatically generate stock pass/fail ratings and comprehensive risk reports based on quantitative threshold rules.
How Shiwei (Ten Dimensions Stock Financial Analysis) Works
- Initial Scope and Asset Profiling: The system determines the company's industry vertical (e.g., consumer, technology, cyclical) and business model characteristics.
- Multi-Source Data Retrieval: The skill triggers data fetching tools (such as akshare-stock, a-stock-data, or financial search APIs) to collect 5 years of historical financial records and recent quarterly data.
- 10-Dimensional Quant-Qual Analysis: The AI processes the target across 10 structured modules: Revenue/Profit Growth, ROE, Balance Sheet solvency, Cash Flow quality, Shareholder Returns, Market Track, Peer Benchmarking, Shareholding changes, Shareholding Concentration, and Valuation Metrics.
- Standardized Scoring & Rating: Each module is marked as "Pass" or "Fail" based on concrete metrics. The aggregate score categorizes the stock as High Quality (>= 8 passes), Neutral (6-7), or Fail (<= 5).
- Report Generation & Risk Advisory: Emits an executive report summarizing core business attributes, the scorecard breakdown, and critical investment risk warnings.
Shiwei (Ten Dimensions Stock Financial Analysis) Setup
To get started with the Shiwei skill within your Openclaw Skills ecosystem, configure your AI agent host environment with the required stock data fetchers and analytical tools.
1. Prerequisite Tool Installation
Ensure your AI agent has access to the underlying data scraping and analysis utilities:
# Install the core stock data fetchers
npm install @openclaw/tool-akshare-stock @openclaw/tool-a-stock-data
# Install web research and search tools for industry analysis
npm install @openclaw/tool-market-researcher @openclaw/tool-financial-search
2. Configure Skill Keywords
Define the execution triggers in your agent's routing manifest. The primary trigger keyword is 十维 (Shiwei). Secondary trigger patterns include:
- 十维分析 (Shiwei Analysis)
- 价值研判 (Value Judgment)
- 财务分析标准流程 (Standard Financial Analysis Process)
- 财务排雷 (Financial Red-flag Detection)
- 基本面全维度分析 (All-dimensional Fundamental Analysis)
3. Agent Integration
Provide the agent with the shiwei instruction prompt. The agent will automatically call the matched search tools sequentially depending on the stock's market listing (e.g., A-shares use akshare-stock, while US/HK shares fallback to stock-analyzer).
Shiwei (Ten Dimensions Stock Financial Analysis) Data Schema & Taxonomy
The Shiwei skill structures its output and data analysis models to ensure uniform evaluation outputs. Below is the metadata schema and scoring matrix applied during execution.
Evaluation Matrix Table
| Dimension | Key Indicators | Target Criteria |
|---|---|---|
| 1. Revenue & Net Profit | YoY Growth (5-10 years) | Consistent positive growth matching or beating industry averages. |
| 2. ROE (Return on Equity) | 5-Year ROE Trend | Growth Stocks: >= 15%; Value Stocks: >= 10%. |
| 3. Balance Sheet Solvency | Total Cash / Interest-bearing Debt | Total cash >= interest-bearing debt; receivables/revenue <= 15%. |
| 4. Cash Flow Quality | Net Cash Ratio (Operating Cash Flow / Net Profit) | Net cash ratio >= 1.0 (ideally >= 1.2 to prove earnings quality). |
| 5. Shareholder Returns | Dividend Payout Ratio & History | Active payouts for >= 5 years; stable or growing payout ratios. |
| 6. Market Track Analysis | CAGR of the Industry Sector | Industry growth >= 10% with sustainable demand drivers. |
| 7. Peer Benchmarking | Margin & ROE comparison with 3-5 rivals | Metrics must consistently rank in the top tier of competitive peers. |
| 8. Shareholding Changes | Buybacks and Insider Trading | No major insider dumps (accumulated selling < 5% over 3 years). |
| 9. Shareholder Concentration | Per-capita shareholding value | Preferred >= 200k RMB per-capita value (indicates high institutional backing). |
| 10. Valuation Analysis | Double-Halving Theory & PB/PE percentiles | Price decline >= 75% from high with intact fundamentals, or bottom PB/PE. |
Final Output Structure
All generated reports adhere to a clean markdown hierarchy containing:
- Core Attributes: Industry category, market standing, and business model type.
- 10-Dimension Scorecard: Standardized table tracking passes/fails.
- Core Conclusion: Categorized as High Quality (⭐⭐⭐), Neutral (⭐⭐), or Fail (⭐).
- Risk Disclosures: Key threats to keep under observation.
Shiwei (Ten Dimensions Stock Financial Analysis) Advanced Features
- Dynamic Industry-Adaptive Thresholds: Automatically adjusts rating benchmarks based on sector characteristics (e.g., accommodates high-debt structures for financial institutions and fluctuating ROE for cyclical commodities).
- Dual-Halving Bottom-Fisher Logic: Leverages specialized algorithmic valuation filters to identify highly undervalued "double-halved" equities (down over 75% from historical highs) that still retain pristine fundamental health.
- Automated Anti-Fraud Triangulation: Triangulates revenue growth figures against net cash flow ratios and shareholder payout records to proactively detect potential accounting manipulations.
- Multi-Agent Orchestration Support: Seamlessly plugs into broad-scale financial analysis pipelines, enabling autonomous data collection agents, research agents, and risk evaluation agents to cooperate within the Openclaw Skills framework.
SKILL.md
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