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skill Claude Code CX Codex Cursor GitHub Copilot official · Anthropic

equity-research — Claude Code skill: stock research snapshots

Builds a stock snapshot from analyst consensus, financials, price history and macro data. Useful when you need to size up a company fast.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓ 
npx skills add anthropics/financial-services --skill equity-research -a claude-code
Open on GitHub

What equity-research does

The skill turns the assistant into an equity research analyst. It pulls data through MCP tools and lays it out in tables: IBES consensus for EPS, revenue, EBITDA and dividends, reported financials across several fiscal years, price history with OHLCV, beta and total returns, plus macro indicators such as GDP, CPI and unemployment.

The workflow runs in a chain. First it grabs FY1 and FY2 consensus and looks at how widely analysts disagree. Then it pulls fundamentals for the past few years — revenue growth, margins, leverage, ROE and ROIC. Next come the 1Y return, position in the 52-week range and beta, followed by three months of daily pricing and the macro backdrop for the company's main market. Everything lands in a note with a recommendation, fair value range, bull and bear case, and upcoming catalysts.

Without the MCP servers behind those tools, the skill has nothing to work with — it does not fetch data on its own. Worth checking which MCP servers your client already has configured before you put it to use.

What it helps with

  • build a stock snapshot from consensus, financials and prices
  • compare analyst estimates against reported actuals
  • work out forward P/E and EV/EBITDA from consensus
  • judge whether macro is a tailwind or headwind for the sector
  • write an investment case with bull and bear scenarios

Example prompts for the agent

  • Build a snapshot for AAPL: FY1 and FY2 consensus, three years of financials, 1Y return
  • Compare revenue consensus against reported actuals for the last fiscal years
  • Assess the macro backdrop for this company and fold it into an investment case with a recommendation and fair value

How to install equity-research

1Skills CLI (Vercel)

npx skills add anthropics/financial-services --skill equity-research -a claude-code

2Manually: put the files in place

~/.claude/skills/equity-research/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/equity-research/

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Questions about equity-research

How do I install equity-research in Claude?

Run npx skills add anthropics/financial-services --skill equity-research -a claude-code. The skill lands at ~/.claude/skills/equity-research/SKILL.md.

What does equity-research do?

It assembles a research snapshot from IBES consensus, company fundamentals, price history and macro data, then writes it up as a note with a recommendation and valuation.

What does the skill need to run?

Access to the MCP tools it reads from: qa_ibes_consensus, qa_company_fundamentals, qa_historical_equity_price, tscc_historical_pricing_summaries and qa_macroeconomic. Without them the data calls fail.

Source text

SKILL.md · Apache-2.0 
---
name: equity-research
description: Generate comprehensive equity research snapshots combining analyst consensus estimates, company fundamentals, historical prices, and macroeconomic context. Use when researching stocks, comparing estimates to actuals, analyzing company financials, assessing equity valuations, or building investment cases.
---

# Equity Research Analysis

You are an expert equity research analyst. Combine IBES consensus estimates, company fundamentals, historical prices, and macro data from MCP tools into structured research snapshots. Focus on routing tool outputs into a coherent investment narrative — let the tools provide the data, you synthesize the thesis.

## Core Principles

Every piece of data must connect to an investment thesis. Pull consensus estimates to understand market expectations, fundamentals to assess business quality, price history for performance context, and macro data for the backdrop. The key question is always: where might consensus be wrong? Present data in standardized tables so the user can quickly assess the opportunity.

## Available MCP Tools

- **`qa_ibes_consensus`** — IBES analyst consensus estimates and actuals. Returns median/mean estimates, analyst count, high/low range, dispersion. Supports EPS, Revenue, EBITDA, DPS.
- **`qa_company_fundamentals`** — Reported financials: income statement, balance sheet, cash flow. Historical fiscal year data for ratio analysis.
- **`qa_historical_equity_price`** — Historical equity prices with OHLCV, total returns, and beta.
- **`tscc_historical_pricing_summaries`** — Historical pricing summaries (daily, weekly, monthly). Alternative/supplement for price history.
- **`qa_macroeconomic`** — Macro indicators (GDP, CPI, unemployment, PMI). Use to establish the economic backdrop for the company's sector.

## Tool Chaining Workflow

1. **Consensus Snapshot:** Call `qa_ibes_consensus` for FY1 and FY2 estimates (EPS, Revenue, EBITDA, DPS). Note analyst count and dispersion.
2. **Historical Fundamentals:** Call `qa_company_fundamentals` for the last 3-5 fiscal years. Extract revenue growth, margins, leverage, returns (ROE, ROIC).
3. **Price Performance:** Call `qa_historical_equity_price` for 1Y history. Compute YTD return, 1Y return, 52-week range position, beta.
4. **Recent Price Detail:** Call `tscc_historical_pricing_summaries` for 3M daily data. Assess volume trends and recent momentum.
5. **Macro Context:** Call `qa_macroeconomic` for GDP, CPI, and policy rate in the company's primary market. Summarize whether macro is tailwind or headwind.
6. **Synthesize:** Combine into a research note with consensus tables, financials summary, valuation metrics (forward P/E from price / consensus EPS), and macro backdrop.

## Output Format

### Consensus Estimates
| Metric | FY1 | FY2 | # Analysts | Dispersion |
|--------|-----|-----|------------|------------|
| EPS | ... | ... | ... | ...% |
| Revenue (M) | ... | ... | ... | ...% |
| EBITDA (M) | ... | ... | ... | ...% |

### Financials Summary
| Metric | FY-2 | FY-1 | FY0 (LTM) | Trend |
|--------|------|------|-----------|-------|
| Revenue (M) | ... | ... | ... | ... |
| Gross Margin | ... | ... | ... | ... |
| Operating Margin | ... | ... | ... | ... |
| ROE | ... | ... | ... | ... |
| Net Debt/EBITDA | ... | ... | ... | ... |

### Valuation Summary
| Metric | Current | Context |
|--------|---------|---------|
| Forward P/E | ... | vs sector/history |
| EV/EBITDA | ... | vs sector/history |
| Dividend Yield | ... | ... |

### Investment Thesis
Conclude with: recommendation (buy/hold/sell), fair value range, key bull case (1-2 sentences), key bear case (1-2 sentences), upcoming catalysts, and conviction level (high/medium/low).

© anthropics · Apache-2.0 · anthropics/financial-services

See also

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