equity-research
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.
Best for investorsWorks with GitHub
⌘source
- author
@anthropics- language
- Python
★11kstars
1.5kforks
May 6, 2026
✦overview.md
Key Features
- ·Analyze consensus estimates vs. actuals
- ·Review company fundamentals & ratios
- ·Contextualize with macro data
Use Cases
- →Research a stock before investing
- →Compare analyst expectations to reported results
- →Build an investment thesis for an equity
Best for
- ✓investors
- ✓equity analysts
- ✓portfolio managers
Not ideal for
- !high-frequency trading
- !short-term market timing
FAQs
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.
$install
1-click copynpx skills add anthropics/financial-services --skill equity-researchSafety assessment
★
Clarity score
How clear and easy to understand the SKILL.md instructions are, rated from 1 to 5.
4/ 5
very goodClear and well structured, with only minor parts that might need a second read.
◎
Actionability score
How directly an agent can act on the SKILL.md instructions, rated from 1 to 5.
3/ 5
mediumPartially actionable with several concrete steps, but still missing important details.