Equity Research

Name: Equity Research
Author: anthropics

anthropics/financial-services

1 installs
34k repo stars
Updated August 4, 2026
anthropics/financial-services

This is a copy of equity-research by anthropics - installs and ranking accrue to the original listing.

equity-research is a Claude skill that combines IBES consensus estimates, fundamentals, prices, and macro data into structured equity research snapshots with an investment thesis.

About

This Claude skill builds structured equity research snapshots by combining IBES analyst consensus estimates, company fundamentals, historical prices, and macroeconomic context from MCP tools. It routes those tool outputs into standardized consensus, financials, and valuation tables and concludes with an investment thesis. An analyst uses it when researching a stock, comparing estimates to actuals, or building an investment case.

Combines IBES consensus, fundamentals, price history, and macro data into a research note
Chains six MCP tools (consensus, fundamentals, prices, macro) into standardized tables
Outputs an investment thesis with buy/hold/sell and fair value range

Equity Research by the numbers

1 all-time installs (skills.sh)
Data as of Aug 5, 2026 (Skillselion catalog sync)

At a glance

equity-research capabilities & compatibility

Capabilities: earnings preview single · initiating coverage · idea generation
Use cases: research · data analysis

From the docs

What equity-research says it does

Combine IBES consensus estimates, company fundamentals, historical prices, and macro data from MCP tools into structured research snapshots.

SKILL.md

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).

SKILL.md

npx skills add https://github.com/anthropics/financial-services --skill equity-research

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Installs	1
repo stars	★ 34k
Last updated	August 4, 2026
Repository	anthropics/financial-services ↗

What it does

Build a structured equity research snapshot and investment thesis for a stock from consensus, fundamentals, and macro data.

Who is it for?

Equity analysts synthesizing consensus, fundamentals, and macro data into an investment thesis.

Skip if: Fixed income or FX analysis, which have dedicated skills.

When should I use this skill?

You are researching a stock and need consensus estimates, fundamentals, and a valuation view.

What you get

A structured research note with consensus, financials, and valuation tables plus a buy/hold/sell thesis.

Consensus estimates table
Financials and valuation summary tables
Investment thesis with recommendation

By the numbers

Chains 6 MCP tools
Pulls last 3-5 fiscal years of fundamentals
Uses 1Y price history and 3M daily data

Files

SKILL.mdMarkdownGitHub ↗

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).