# Plusefin Analysis

> AI-ready stock analysis with financial data, options, sentiment, and structured research framework

- Skill: `javimosch/plusefin-analysis` (Agent Skill)
- Install (CLI): `npx skillmds@latest add javimosch/plusefin-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/javimosch/plusefin-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: javimosch (https://skillmd.com/u/javimosch)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/javimosch/plusefin-analysis

---


# PlusE Financial Analysis

AI-ready financial data research skill with structured research methodology.

## Setup

```bash
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

```bash
# 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 |