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Install in OpenClaw
/install alicetuo-investment-research-analyst
Description
Multi-agent investment research framework simulating a professional trading firm. Performs comprehensive stock analysis including fundamentals, news, sentime...
README (SKILL.md)
Investment Research Analyst
Overview
You are a Chief Analyst simulating a professional investment firm's operations. Conduct in-depth research on listed companies across 8 dimensions: fundamentals, news, sentiment, technicals, bull/bear debate, risk assessment, and fact-checking.
Workflow
Step 1: Multi-Dimensional Research
- Fundamentals — Financial statements, profitability, valuation, analyst forecasts
- News — Company dynamics, industry news, policy impacts, management changes
- Sentiment — Market sentiment, institutional views, rating changes
- Technical Analysis — Price trends, volume, key levels
Step 2: Debate
- Bull Case — Build bull thesis with supporting evidence
- Bear Case — Identify risks and concerns
Step 3: Risk & Verification
- Risk Assessment — Comprehensive risk evaluation
- Fact-Check — Verify key data and claims
Step 4: Output & Deploy
- Output full research report
- Deploy as interactive Dashboard
Data Sources
A-Stocks (akshare)
import akshare as ak
ak.stock_individual_info_em(symbol="600519") # Company info
ak.stock_financial_analysis_indicator(symbol="600519") # Financial metrics
ak.stock_zh_a_hist(symbol="600519", period="daily", adjust="qfq") # Daily OHLCV
ak.stock_hk_financial_hk(symbol="00700") # HK financials
US Stocks (yfinance)
import yfinance as yf
stock = yf.Ticker("AAPL")
stock.info # Valuation
stock.financials # Income statement
stock.balance_sheet # Balance sheet
stock.history(period="1y") # OHLCV
Analysis Outputs
Fundamentals → Report Section
- Revenue, net income, margins, ROE, ROA
- DCF, PE, PB, PS, PEG, EV/EBITDA comparison
- Broker ratings, price targets, consensus EPS
News → Report Section
- Recent company/industry/macro news (7-30 days)
- Impact assessment: positive/negative
Sentiment → Report Section
- Social media tone (Twitter/X, Reddit, StockTwits)
- Institutional holdings, insider trading, short interest
Technicals → Report Section
- SMA 20/50/200, RSI(14), MACD, support/resistance
- Entry/exit zones, stop-loss levels
Bull/Bear Debate → Report Section
- Key catalysts, PT, best/worst case scenarios
- Rebuttals to opposing views
Risk → Report Section
- Position sizing, volatility, liquidity
- Stop-loss recommendation, risk/return ratio
Fact-Check → Report Section
- Cross-verify key financials and valuations
- Flag discrepancies with sources
Research Report Template
## [Company Name] ([Ticker]) — Research Report
### Summary
[Business overview, industry position, valuation snapshot]
### Financials
| Metric | Value | YoY | vs. Sector |
|--------|-------|-----|------------|
| Revenue | X | +/-% | [Rank] |
| Net Income | X | +/-% | [Rank] |
| Gross Margin | X% | +/-pp | [Rank] |
| ROE | X% | +/-pp | [Rank] |
### Valuation
| Metric | Current | Historical | Sector Avg |
|--------|---------|------------|------------|
| PE(TTM) | X | X%ile | X |
| PB | X | X%ile | X |
| PS | X | X%ile | X |
### Analysts
- Rating: Buy/N/Hold/Sell (X brokers)
- PT range: X - X
- Consensus EPS: FY1=X, FY2=X
### Bull Case 🐂
[2-3 key catalysts with data support]
### Bear Case 🐻
[2-3 key risks with data support]
### Risks ⚠️
[Top 3 risks with probability and impact]
### Overall: 🟢 High / 🟡 Medium / 🔴 Low Value
### Disclaimer
AI-generated. Not investment advice.
Dashboard
After completing the report, deploy it as a professional interactive Dashboard (HTML/CSS/JS, black theme, minimalist Notion-style). Use the deploy tool to publish and share the link.
Usage Guidance
This skill appears to do legitimate investment research but has operational gaps you should resolve before use. Ask the author (or require) clear declarations for: (1) which Python packages must be installed (akshare, yfinance, any sentiment or scraping libs) and an install plan; (2) which external services/APIs will be used and what credentials they require; and (3) what the 'deploy' tool is, where it will publish reports, and who can access published links. If you run it, prefer an isolated environment, do not supply high‑privilege credentials, and confirm the deploy target and sharing settings so sensitive inputs or proprietary data are not accidentally published. If you need higher assurance, request the skill add explicit dependency and credential fields or provide a vetted implementation that limits network publishing.
Capability Analysis
Type: OpenClaw Skill
Name: alicetuo-investment-research-analyst
Version: 1.0.0
The skill bundle defines a legitimate investment research framework that uses standard financial libraries like 'akshare' and 'yfinance' to perform stock analysis. The workflow is well-structured, focusing on fundamentals, technicals, and risk assessment, and the instructions in SKILL.md are strictly aligned with the stated purpose of generating financial reports and dashboards.
Capability Assessment
Purpose & Capability
The name/description (multi‑agent investment research) aligns with the SKILL.md content: it references appropriate data sources (akshare for A‑shares, yfinance for US tickers) and describes the expected analyses. However, the skill cites Python libraries (akshare, yfinance) and external data sources (Twitter/X, Reddit, StockTwits, broker ratings, institutional holdings) without declaring dependencies, install steps, or required API keys — a mismatch between declared requirements (none) and what the instructions actually assume.
Instruction Scope
Instructions explicitly direct fetching financials, social‑media sentiment, short interest/insider/institutional data, and then deploying a public Dashboard via a 'deploy' tool. The SKILL.md gives no constraints or explicit data source endpoints and asks the agent to publish/share results; that grants broad discretion to access external web APIs or scraping and to transmit potentially sensitive outputs to an external endpoint. There is also an implied multi‑agent orchestration but no guardrails about what data may be included in published reports.
Install Mechanism
No install specification or code files are provided (instruction‑only), which is low risk in itself. But Python code examples import akshare and yfinance — these packages may not be present in the runtime, meaning the agent might attempt to pip install them or fail. The absence of install instructions is an operational omission rather than direct maliciousness, but it is an incoherence the user should be aware of.
Credentials
The skill requests no environment variables or credentials, yet operationally it likely needs API keys/credentials for some data sources (Twitter/X API, paid data providers, or a deploy/publishing service). Not declaring these credentials is a mismatch: the agent might try to use whatever credentials are available in the environment or prompt for new ones. That increases the risk of unintended credential use or accidental data exfiltration.
Persistence & Privilege
No 'always: true' or other elevated persistence flags are set. The skill is user-invocable and allows autonomous model invocation (platform default). It does not declare writing to other skill configs or system paths. No extra privilege requests are visible.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install alicetuo-investment-research-analyst - After installation, invoke the skill by name or use
/alicetuo-investment-research-analyst - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of the Multi-Agent Investment Research Analyst skill.
- Simulates a professional trading firm’s analyst workflow across 8 research dimensions: fundamentals, news, sentiment, technicals, bull vs. bear debate, risk assessment, and fact-checking.
- Integrates data sources for US and China A-shares (yfinance, akshare).
- Outputs comprehensive research reports with templated sections and objective assessments.
- Enables report deployment as an interactive black-themed dashboard.
Metadata
Frequently Asked Questions
What is Investment Research Analyst?
Multi-agent investment research framework simulating a professional trading firm. Performs comprehensive stock analysis including fundamentals, news, sentime... It is an AI Agent Skill for Claude Code / OpenClaw, with 109 downloads so far.
How do I install Investment Research Analyst?
Run "/install alicetuo-investment-research-analyst" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Investment Research Analyst free?
Yes, Investment Research Analyst is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Investment Research Analyst support?
Investment Research Analyst is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Investment Research Analyst?
It is built and maintained by alicetuo (@alicetuo); the current version is v1.0.0.
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