📈 Plusefin Analysis OpenClaw Plugin & Skill | ClawHub
Looking to integrate Plusefin Analysis into your AI workflows? This free OpenClaw plugin from ClawHub helps you automate cli utilities tasks instantly, without having to write custom tools from scratch.
What this skill does
AI-ready stock analysis with financial data, options, sentiment, and structured research framework
Install
openclaw skills install @wanghsinche/plusefin-analysisnpx clawhub@latest install plusefin-analysisFull SKILL.md
Open original| name | description |
|---|---|
| plusefin-analysis | AI-ready stock analysis with financial data, options, sentiment, and structured research framework |
SKILL.md content below is scrollable.
PlusE Financial Analysis
AI-ready financial data research skill with structured research methodology.
Setup
export PLUSEFIN_API_KEY=your_api_key
Research Framework
1. Research Setup
- Define target (ticker) and time range (6mo / 1y / 2y)
- Set research objective: valuation analysis / technical outlook / event-driven
2. Data Collection
- Company Fundamentals:
ticker- overview, valuation, ratings - Market Sentiment:
sentiment/sentiment-history - Options Data:
options/options-analyze(IV, Greeks, OI) - Institutional Holdings:
holders- major holders changes - Financial Statements:
statements(income/balance/cash) - Earnings & Insider:
earnings/insiders - Price History:
price-history
3. Hypothesis Formation
Based on data, formulate hypotheses:
- Direction: Bullish / Bearish / Neutral
- Drivers: Valuation reversion, earnings growth, event catalyst, sentiment reversal
4. Evidence Validation
- Use search capabilities to gather research reports, news, announcements
- Cross-validate multi-source data timeline consistency
- Seek evidence supporting or refuting hypotheses
5. Valuation Scenarios
- Bull Case: Valuation assuming upside catalysts materialize
- Base Case: Valuation based on current market expectations
- Bear Case: Valuation assuming downside risks materialize
6. Risk Assessment
- Downside risks
- Key assumption risks
- Potential catalysts and triggers
7. Report Output
Structured output:
- Core thesis
- Evidence summary
- Valuation scenario comparison
- Risk warnings
- Actionable recommendations (if applicable)
Each key conclusion must include source citations.
Usage
# Set API key
export PLUSEFIN_API_KEY=your_api_key
# Run commands
python plusefin.py <command> [args]
Commands
| Command | Usage | Description |
|---|---|---|
ticker |
python plusefin.py ticker <symbol> |
Company overview, valuation, ratings |
price-history |
python plusefin.py price-history <ticker> [period] |
Historical prices (6mo/1y/2y) |
sentiment |
python plusefin.py sentiment |
Market sentiment (Fear & Greed) |
sentiment-history |
python plusefin.py sentiment-history [days] |
Historical sentiment |
options |
python plusefin.py options <symbol> [num] |
Options chain |
options-analyze |
python plusefin.py options-analyze <symbol> |
Options analysis |
holders |
python plusefin.py holders <symbol> |
Institutional holdings |
statements |
python plusefin.py statements <symbol> [type] |
Financial statements (income/balance/cash) |
earnings |
python plusefin.py earnings <symbol> |
Earnings history |
insiders |
python plusefin.py insiders <symbol> |
Insider trading |
news |
python plusefin.py news <symbol> |
Stock news |
fred |
python plusefin.py fred <series_id> |
Macroeconomic data |