Go back
More in category View on ClawHub

Jump to related tools in the same category or review the original source on GitHub.

CLI Utilities @wanghsinche Updated 7/24/2026 1,469 downloads 2 stars Security: Pass

📈 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

Financial data research via PlusE API. Provides stock fundamentals, options analysis, market sentiment (Fear & Greed), institutional holdings, insider trades, financial statements, macroeconomic data (FRED), ML price predictions, and market news. Use when user asks about: stock analysis, ticker research, options trading, options Greeks, implied volatility, market sentiment, Fear & Greed index, earnings reports, financial statements (income/ balance/cash flow), insider trading, institutional holders, 13F, GDP, inflation, CPI, unemployment, interest rates, macroeconomics, CNBC news, Reddit stock discussions, or price prediction forecast.

Install

ClawHub CLI 
openclaw skills install @wanghsinche/plusefin-analysis
Node.js (npx) 
npx clawhub@latest install plusefin-analysis

Full SKILL.md

Open original
Metadata table.
namedescription
plusefin-analysisFinancial data research via PlusE API. Provides stock fundamentals, options analysis, market sentiment (Fear & Greed), institutional holdings, insider trades, financial statements, macroeconomic data (FRED), ML price predictions, and market news. Use when user asks about: stock analysis, ticker research, options trading, options Greeks, implied volatility, market sentiment, Fear & Greed index, earnings reports, financial statements (income/ balance/cash flow), insider trading, institutional holders, 13F, GDP, inflation, CPI, unemployment, interest rates, macroeconomics, CNBC news, Reddit stock discussions, or price prediction forecast.

SKILL.md content below is scrollable.

PlusE Financial Analysis

AI-ready financial data research skill. All data is ML-preprocessed and token-optimized for direct AI consumption — no raw JSON parsing needed.

Setup

export PLUSEFIN_API_KEY=your_api_key

Get a free API key at console.plusefin.com.

Usage

There are three ways to access PlusE data. Use whichever your agent supports.

Option A: MCP (Claude Code / OpenCode)

If the PlusE MCP server is connected, call tools directly. MCP server URL:

https://mcp.plusefin.com/mcp/?apikey=$PLUSEFIN_API_KEY

Each tool is listed in the Data Reference below with its MCP tool name. Call tools like: get_ticker_data("AAPL")

Option B: CLI (Any agent — recommended fallback)

python plusefin.py <command> [args]

The plusefin.py script is bundled with this skill directory.

Option C: curl (Any agent)

curl -s -H "Authorization: Bearer $PLUSEFIN_API_KEY" \
  "https://mcp.plusefin.com/api/tools/<endpoint>"

Data Reference

📊 Company Fundamentals

Data MCP Tool CLI Command curl Endpoint
Overview, valuation, ratings get_ticker_data("AAPL") python plusefin.py ticker AAPL /tools/ticker/AAPL
Price history + TA indicators get_price_history("AAPL", "1y") python plusefin.py price-history AAPL 1y /tools/price-history?ticker=AAPL&period=1y
Financial statements get_financial_statements("AAPL", "income", "annual") python plusefin.py statements AAPL income /tools/statements/AAPL?type=income&frequency=annual
Earnings history get_earnings_history("AAPL") python plusefin.py earnings AAPL /tools/earnings/AAPL
Stock news get_ticker_news_tool("AAPL") python plusefin.py news AAPL /tools/news/AAPL

📈 Options

Data MCP Tool CLI Command curl Endpoint
Options analysis (Greeks, IV, OI) super_option_tool("TSLA") python plusefin.py options-analyze TSLA /tools/options/analyze/TSLA
Options chain — python plusefin.py options TSLA 20 /tools/options/TSLA?num_options=20

🏛️ Institutional Activity

Data MCP Tool CLI Command curl Endpoint
Top 25 institutional holders get_top25_holders("AAPL") python plusefin.py top25 AAPL /tools/top25/AAPL
Insider trades get_insider_trades("AAPL") python plusefin.py insiders AAPL /tools/insiders/AAPL
Institutional holders (same as top25) python plusefin.py holders AAPL /tools/holders/AAPL

😱 Market Sentiment

Data MCP Tool CLI Command curl Endpoint
Fear & Greed, VIX, market breadth get_overall_sentiment_tool() python plusefin.py sentiment /tools/sentiment
Historical Fear & Greed — python plusefin.py sentiment-history 30 /tools/sentiment/history?days=30
Sentiment trend analysis — python plusefin.py sentiment-trend 30 /tools/sentiment/trend?days=30
CNBC market news cnbc_news_feed() python plusefin.py news-market /tools/news/market
Reddit discussions social_media_feed(["AAPL","TSLA"]) python plusefin.py news-social AAPL /tools/news/social?keywords=AAPL

🌍 Macroeconomic Data (FRED)

Data MCP Tool CLI Command curl Endpoint
FRED series by ID get_fred_series("GDP") python plusefin.py fred GDP /tools/fred/GDP
Search FRED series search_fred_series("CPI") python plusefin.py fred-search CPI /tools/fred/search?q=CPI

Common FRED series IDs: GDP (GDP), CPIAUCSL (CPI), UNRATE (unemployment), FEDFUNDS (interest rate), DGS10 (10Y Treasury), SP500 (S&P 500), T10YIE (10Y breakeven inflation).

🔮 Price Prediction

Data MCP Tool CLI Command curl Endpoint
ML price forecast + probability price_prediction("AAPL") python plusefin.py prediction AAPL /tools/prediction/AAPL

🧮 Calculator

Data MCP Tool
Execute Python expressions calculate("2 + 2")

No CLI/curl equivalent needed. Use the calculate tool directly in MCP-native agents.

⏰ Time

Data MCP Tool
Current time (ISO 8601) get_current_time()

Research Workflows

Workflow 1: Stock Deep Dive

When user asks "analyze AAPL" or "what do you think about TSLA":

1. Fundamentals    → ticker(symbol)           → overview, valuation, ratings
2. Technicals      → price-history(symbol, 1y) → price data + TA indicators
3. Sentiment check → sentiment()               → Fear & Greed, VIX
4. Institution     → top25(symbol)             → who holds it, recent changes
5. Options market  → options-analyze(symbol)   → IV, Greeks, OI
6. Macro context   → fred(GDP), fred(UNRATE)   → economic backdrop
7. Synthesize into structured report with bull/base/bear cases

Workflow 2: Earnings Preparation

When user asks "earnings coming up for MSFT" or "what to expect from NVDA earnings":

1. Past earnings   → earnings(symbol)          → surprise history, trend
2. Recent news     → news(symbol)              → developments, catalysts
3. Options market  → options-analyze(symbol)    → IV crush, expected move
4. Social buzz     → news-social(symbol)        → retail sentiment
5. ML forecast     → prediction(symbol)         → probability of decline
6. Summarize expectations with key levels to watch

Workflow 3: Market Pulse

When user asks "how's the market looking today":

1. Fear & Greed    → sentiment()                → overall market mood
2. Market news     → news-market()              → CNBC headlines
3. Social pulse    → news-social("market,economy,stocks") → Reddit sentiment
4. Key indicators  → fred(DGS10), fred(FEDFUNDS), fred(T10YIE)
5. Quick summary of risk-on/risk-off environment

Workflow 4: Macroeconomic Context

When user asks "what's the macro picture" or "how's the economy":

1. GDP             → fred(GDP)                   → economic growth
2. Inflation       → fred(CPIAUCSL)              → CPI trend
3. Employment      → fred(UNRATE)                → unemployment
4. Rates           → fred(FEDFUNDS), fred(DGS10) → monetary policy
5. Markets         → fred(SP500)                 → market level context
6. Synthesize macro regime and implications for equities

Workflow 5: Options Strategy Research

When user asks "analyze options for AAPL" or "find options opportunities":

1. Options analysis → options-analyze(symbol)   → full Greeks, IV, OI
2. Options chain    → options(symbol, 20)       → specific strikes/expiry
3. Price context    → price-history(symbol, 6mo) → recent price action
4. Sentiment check  → sentiment()                → market mood alignment
5. Report: IV rank, put/call skew, key strikes, implied move

Analysis Framework

When producing a research report, structure output with these sections:

Core Thesis

  • Direction: bullish / bearish / neutral
  • Key drivers: valuation, earnings growth, catalyst, sentiment reversal
  • Confidence level and time horizon

Evidence Summary

  • Cite specific data points from tools used (fundamentals, technicals, options, sentiment)
  • Note conflicting signals if any

Valuation Scenarios

  • Bull case: upside catalysts, target valuation, key levels
  • Base case: expected outcome under current conditions
  • Bear case: downside risks, key levels to watch
  • Assign probability weights to each scenario

Risk Assessment

  • Company-specific risks
  • Macro/industry risks
  • Key assumptions that, if wrong, change the thesis

Actionable Recommendation

  • Directional view with conviction level
  • Suggested position sizing guidance
  • Key levels and triggers to monitor
ClawHub Registry URL: https://clawhub.ai/wanghsinche/skills/plusefin-analysis

Related skills

If this matches your use case, these are close alternatives in the same category.

13-day-sprint-method

Productivity system based on Maya calendar with 13 natural tones for project management and personal development.

a-share-short-decision

A-share short-term trading decision skill for 1-5 day horizon.

activity-analyzer

Use ActivityWatch to analyze user's computer activity (Requires Node.js)

advisory-council

**You MUST actually execute the Python command using your shell/exec tool.** Read the real output.

aetup-automatik

Facilitate the installation and management of VPS solutions using the Setup Automatik engine (powered by Orion.

agent-commerce-engine

A production-ready universal engine for Agentic.