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Install in OpenClaw
/install financial-risk-scanner
Description
Analyze listed company financials to detect 21 fraud risk indicators with severity ratings and cross-validation for accounting anomalies and governance issues.
README (SKILL.md)
Financial Risk Scanner
Scan listed company financial statements for potential fraud signals and accounting anomalies using Tushare data APIs.
Quick Start
python3 scripts/analyze_company.py \x3Cts_code>
Example:
python3 scripts/analyze_company.py 000001.SZ
Workflow
- Fetch Financial Data: Retrieve 10+ years of annual financial statements (balance sheet, income statement, cash flow statement) via Tushare
- Calculate Risk Metrics: Compute 21 risk indicators with historical trend analysis
- Cross-validate: Check company announcements for supporting evidence when anomalies detected
- Generate Report: Produce structured risk assessment with severity ratings and recommendations
Risk Indicators
| Category | Indicator | Detection Criteria |
|---|---|---|
| Asset Reality | Cash-Debt Paradox | Cash > 15% assets + Interest-bearing debt > 30% assets |
| Receivables Anomaly | Receivables growth >> Revenue growth | |
| Inventory Anomaly | Inventory growth >> COGS growth | |
| Prepayments Surge | Prepayments > 5% assets without business rationale | |
| Other Receivables High | Other receivables > 5% net assets | |
| Construction Suspended | Construction long uncompleted or excessive | |
| Profit Quality | Cash-Profit Divergence | High profit + Negative operating cash flow (3+ years) |
| Gross Margin Anomaly | GM far above peers or rising persistently | |
| Sales Expense Anomaly | Sales expense ratio far below peers | |
| Abnormal Non-recurring | Non-recurring items > 30% of profit | |
| Asset Impairment Bath | One-time large impairment charges | |
| Related Party | Related Transaction High | Related purchase/sales > 30% |
| Related Fund Flows | Related party in other receivables/payables high | |
| Related Guarantees | External guarantees > 50% net assets | |
| Capital Structure | Goodwill High | Goodwill > 30% net assets |
| Debt Ratio High | Debt ratio > 70% and rising | |
| Short-term Liquidity | Short-term debt / Cash > 3x | |
| Dual Debt High | Long + Short debt high with cash strain | |
| Audit & Governance | Auditor Changes | Consecutive auditor changes |
| Non-standard Opinion | Audit opinion with emphasis or reservation | |
| Executive Departures | CFO/Board secretary frequent changes |
Key Metrics Formulas
Cash-Debt Paradox Ratio = (Cash / Total Assets) × (Interest Debt / Total Assets)
Cash-Profit Ratio = Operating Cash Flow / Net Profit (threshold: \x3C 0.5 for 3+ years)
Receivables Growth Ratio = Receivables Growth Rate / Revenue Growth Rate
Inventory Turnover Ratio = COGS / Average Inventory
Gross Margin = (Revenue - COGS) / Revenue
Debt Ratio = Total Liabilities / Total Assets
Liquidity Pressure = Short-term Borrowing / Cash Balance
References
For detailed detection logic and thresholds, see references/risk_indicators.md.
Cross-Validation Sources
When anomalies are detected, cross-validate with:
- Company announcements (via Tushare announcement API)
- Audit reports in annual reports
- Regulatory filings and enforcement notices
- News and media coverage
Output Format
Reports are saved to ~/.openclaw/workspace/memory/financial-risk/\x3Ccompany_name>_\x3Cdate>.md
Report sections:
- Company Overview: Basic info, industry, listing date
- Risk Summary: Total risks, severity distribution, top concerns
- Detailed Analysis: Each indicator with historical trend charts
- Cross-validation: Supporting evidence from announcements
- Recommendations: Priority actions and monitoring points
Severity Levels
| Level | Symbol | Criteria |
|---|---|---|
| Critical | 🔴 | Multiple indicators triggered, strong fraud signals |
| High | 🟠 | Single strong indicator or 3+ moderate signals |
| Moderate | 🟡 | Anomaly detected but needs verification |
| Low | 🟢 | Minor concern, monitor periodically |
Environment Variables
| Variable | Required | Description |
|---|---|---|
TUSHARE_TOKEN |
✅ Yes | Tushare Pro API Token for Chinese A-share market data |
How to obtain TUSHARE_TOKEN:
- Register at https://tushare.pro/register
- Get your token from the user center
- Set environment variable:
export TUSHARE_TOKEN="your_token_here"
Python Dependencies
| Package | Version | Purpose |
|---|---|---|
tushare |
>=1.2.0 | A-share market data API |
pandas |
>=1.3.0 | Data manipulation and analysis |
Install dependencies:
pip install tushare pandas
Notes
- Annual data preferred for stability; quarterly available for recent trends
- Industry comparison essential for margin and expense analysis
- Historical context matters: 10+ years for trend significance
Limitations
| Limitation | Description |
|---|---|
| API Token Required | Requires a Tushare Pro API token to fetch data |
| Market Scope | Only supports Chinese A-share market listed companies |
| Historical Data | Limited to last 10 years of financial data |
| Related Party Data | Transaction data with related parties requires manual verification from annual report notes |
| Industry Comparison | Some industry metrics may not have sufficient peer data for comparison |
Data Sources
| Source | Type | Description |
|---|---|---|
| Tushare Pro API | Primary | Chinese A-share financial data, market data, and announcements |
Provenance
| Field | Value |
|---|---|
| Author | laigen |
| License | MIT |
| Homepage | https://clawhub.ai/skills/financial-risk-scanner |
| Registry | ClawHub |
Usage Guidance
What to check before installing or running this skill:
- Metadata mismatch: the registry metadata claims no required env vars but SKILL.md and the code require a TUSHARE_TOKEN. Do not assume the skill needs no credentials — it does. Ask the publisher to correct the registry metadata.
- TUSHARE_TOKEN is sensitive: it grants access to a paid data API tied to your account. Use a dedicated/limited API token (or trial account) rather than your primary account token. Monitor API usage/quota to avoid unexpected charges.
- Inspect the repo yourself (you have the Python files). The code is readable and the network calls are to tushare.pro via the tushare client; there are no obvious obfuscated endpoints. If you are not comfortable reading code, run it in an isolated environment (VM or container) and with a test token first.
- Files written to disk: reports are saved under ~/.openclaw/workspace/memory/financial-risk/. If you are concerned about data persistence or leakage, change the output path or run in a disposable environment.
- API quota and batch mode: example scripts include batch analysis and loops over peer lists — these can consume large numbers of API calls. If you run batch analyses, throttle requests and watch your Tushare rate limits/quotas to avoid denial-of-service or unexpected billable usage.
- Trust and provenance: the skill source and homepage shown in the registry are minimal/unknown. Prefer skills with a verifiable homepage, published repository, or known author. Ask the owner for source repository link or sign-off.
If the owner provides corrected registry metadata (listing TUSHARE_TOKEN) and a verifiable homepage or repository, the inconsistencies would be resolved and confidence in the package would increase. If you can't validate the publisher, run the code only in a sandbox and use a token with limited access.
Capability Analysis
Type: OpenClaw Skill
Name: financial-risk-scanner
Version: 1.0.4
The financial-risk-scanner bundle is a legitimate tool designed to analyze Chinese A-share market data for accounting anomalies. It uses the Tushare Pro API to fetch financial statements and implements 21 specific risk calculation logics (e.g., cash-debt paradox, inventory anomalies) within 'scripts/risk_calculator.py'. The code is well-structured, lacks any form of obfuscation, and does not contain any evidence of data exfiltration, unauthorized network calls, or malicious prompt injection instructions. All file operations are restricted to the local workspace for report generation in 'scripts/report_generator.py'.
Capability Assessment
Purpose & Capability
The name/description (financial fraud/risk scanning) align with the included Python scripts and use of Tushare + pandas. The code implements fetching balancesheets, income, cashflow, indicators, computing 21 risk metrics, and generating Markdown reports — all consistent with the stated purpose. Minor inconsistency: the registry metadata provided to you lists no required environment variables, but the SKILL.md and code clearly require a TUSHARE_TOKEN; that mismatch is unexplained and notable.
Instruction Scope
SKILL.md and the scripts confine runtime actions to: calling the Tushare API, computing indicators, cross-validating with announcements/news via Tushare, and writing Markdown reports to ~/.openclaw/workspace/memory/financial-risk/. There are no instructions to read unrelated local files, access other credentials, or contact arbitrary external endpoints beyond Tushare. The skill supports batch analysis, which can cause high-volume API usage but is consistent with its analysis purpose.
Install Mechanism
No install spec is present (instruction-only from platform perspective), and included code is plain Python using standard public packages (tushare, pandas). There are no downloads from unknown URLs, no extract/install steps, and no packaging that would place binaries in unusual locations. The lack of an install script means a user must install Python deps manually before running the scripts.
Credentials
The code and SKILL.md require a single sensitive credential: TUSHARE_TOKEN (Tushare Pro API token) which is appropriate and necessary for fetching Chinese A-share financial data. However, the registry metadata (presented in the evaluation header) stated 'Required env vars: none' — this discrepancy is problematic because a user could install/enable the skill without realizing it will request and transmit a paid/privileged API token. No other unrelated secrets (AWS, GitHub, etc.) are requested.
Persistence & Privilege
The skill does not request always:true, does not modify other skills, and does not require elevated system privileges. It writes reports to a subdirectory under the user's home (~/.openclaw/workspace/memory/financial-risk), which is within expected behavior for a reporting tool. Autonomous invocation (model-invocation enabled) is the platform default and is not by itself a new risk here.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install financial-risk-scanner - After installation, invoke the skill by name or use
/financial-risk-scanner - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.4
- No functional or documentation changes in this version.
- Version bump only; skill.json updated.
v1.0.3
- Added author, license, and homepage metadata to skill definition.
- Expanded environment variable details with scope and usage clarification.
- Added "Limitations", "Data Sources", and "Provenance" sections to documentation.
- Clarified requirement for Tushare Pro API token and market coverage.
- No core functionality changes.
v1.0.2
- Updated internal metadata in skill.json; no user-facing changes or new features.
v1.0.1
- Added skill manifest (skill.json) for improved discoverability and configuration.
- Updated documentation to specify required environment variables and Python dependencies.
- Clarified how to obtain and set up the necessary Tushare API token.
- Enhanced setup instructions, including installation of dependencies and environmental requirements.
v1.0.0
- Initial release of financial-risk-scanner.
- Scans listed company financial statements for 21 types of potential financial fraud risks and accounting anomalies.
- Fetches 10+ years of company data via Tushare APIs and calculates historical risk metrics.
- Generates a comprehensive risk report with severity ratings, cross-validation from announcements, and actionable recommendations.
- Supports input of a single stock code and outputs structured markdown reports.
Metadata
Frequently Asked Questions
What is Financial Risk Scanner for Listed Companies?
Analyze listed company financials to detect 21 fraud risk indicators with severity ratings and cross-validation for accounting anomalies and governance issues. It is an AI Agent Skill for Claude Code / OpenClaw, with 101 downloads so far.
How do I install Financial Risk Scanner for Listed Companies?
Run "/install financial-risk-scanner" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Financial Risk Scanner for Listed Companies free?
Yes, Financial Risk Scanner for Listed Companies is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Financial Risk Scanner for Listed Companies support?
Financial Risk Scanner for Listed Companies is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Financial Risk Scanner for Listed Companies?
It is built and maintained by 赖根 (@laigen); the current version is v1.0.4.
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