Investment Research Analyst

Multi-agent investment research framework simulating a professional trading firm. Performs comprehensive stock analysis including fundamentals, news, sentime...

Security Scan

The skill's instructions broadly match an investment-research purpose, but it omits required dependency/credential declarations and directs publishing via an unspecified 'deploy' tool — these gaps could lead to unexpected network activity or data sharing.

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.

What to consider before installing

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.

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

1. Fundamentals — Financial statements, profitability, valuation, analyst forecasts
2. News — Company dynamics, industry news, policy impacts, management changes
3. Sentiment — Market sentiment, institutional views, rating changes
4. Technical Analysis — Price trends, volume, key levels

Step 2: Debate

5. Bull Case — Build bull thesis with supporting evidence
6. Bear Case — Identify risks and concerns

Step 3: Risk & Verification

7. Risk Assessment — Comprehensive risk evaluation
8. Fact-Check — Verify key data and claims

Step 4: Output & Deploy

9. Output full research report
10. 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.