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equity-research

Vendor officialLow riskBy anthropicsLicense: Apache-2.0★ 41,232

Needs a paid data source: an LSEG data subscription. Without it this skill cannot fetch its data.

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.

Use in Claude.ai (no code)

Download equity-research.zip
  1. In Claude.ai, open Settings → Capabilities → Skills
  2. Choose “Upload skill” and pick the zip you downloaded
  3. Turn it on, then describe your task in a chat. Claude uses the skill when it fits

Use in Claude Code and other agents

Claude Code plugin

/plugin marketplace add anthropics/financial-services
/plugin install lseg@claude-for-financial-services

skills CLI (installs every skill in the repository)

npx skills add anthropics/financial-services

Manual: Copy the plugins/partner-built/lseg/skills/equity-research folder to ~/.claude/skills/equity-research/

Safety scan report

Executable scripts: noneSuspicious patterns: 0Files: 1Content hash: cc28c8de1401

The scan is static pattern matching. It catches common problems and is not a guarantee of safety. Read the source before installing a skill that ships scripts.

Source

anthropics/financial-services/plugins/partner-built/lseg/skills/equity-researchcommit 6056506In plugins: lseg
Files (1)
  • SKILL.md
SKILL.md
# 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).