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Equity Research

AnthropicOfficialCreated by LSEGDistributed by Anthropic

  • Skill
  • Content generator
  • Business and productivity
  • Apache-2.0
  • No issues found
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From the author

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.
— the description by LSEG

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill enables an AI agent to function as an equity research analyst, systematically gathering and synthesizing financial data to support investment decision-making. By orchestrating multiple data-focused tools, the agent compiles a comprehensive research note that includes analyst consensus, historical financial performance, equity price trends, and relevant macroeconomic indicators. The output is structured into standardized tables and a narrative investment thesis, allowing users to quickly evaluate market expectations, business quality, and valuation metrics.

What it does

  • Retrieves IBES analyst consensus estimates including EPS, revenue, and EBITDA.
  • Analyzes historical company financials to calculate growth, margins, and leverage ratios.
  • Processes historical equity price data to determine performance, beta, and momentum.
  • Integrates macroeconomic indicators to provide context for sector-specific performance.
  • Synthesizes disparate data points into a structured investment thesis with bull and bear cases.
  • Calculates key valuation metrics like forward P/E and EV/EBITDA.

When to use it

  • When conducting fundamental analysis on a specific stock.
  • When comparing market consensus estimates against reported company financials.
  • When assessing the impact of macroeconomic conditions on a company's sector.
  • When building a preliminary investment case or valuation model.
  • When performing a quick check on a company's historical financial trends and price performance.

Advantages

  • Standardizes the research process for consistency across different stocks.
  • Reduces manual data collection time by automating the retrieval of financial and macro data.
  • Facilitates rapid identification of potential discrepancies between market expectations and fundamentals.
  • Provides a clear, structured format for evaluating investment opportunities.

Usage examples

“Generate an equity research snapshot for AAPL.”
The agent retrieves consensus estimates, historical financials, and price data to produce a structured report including a valuation summary and investment thesis for Apple.
“What is the current market sentiment and valuation for MSFT compared to its historical performance?”
The agent compiles a report showing current analyst consensus, historical P/E ratios, and recent price trends to provide a comparative analysis.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/anthropics/equity-research/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip equity-research.zip -d .agents/skills

Documentation by LSEG

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
MetricFY1FY2# AnalystsDispersion
EPS............%
Revenue (M)............%
EBITDA (M)............%
Financials Summary
MetricFY-2FY-1FY0 (LTM)Trend
Revenue (M)............
Gross Margin............
Operating Margin............
ROE............
Net Debt/EBITDA............
Valuation Summary
MetricCurrentContext
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).

Files

Requirements and compatibility

Written for: Claude Code, Claude apps

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
LSEG
Publisher
Anthropic (Official)
Distributed by
Anthropic
Source
anthropics/financial-services · plugins/partner-built/lseg/skills/equity-research · commit 6056506
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by LSEG or Anthropic.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download equity-research.zip