equity-research

Name: equity-research
Author: anthropics

github.com/anthropicsData & databases40.9k3.2k installsUpdated today

About equity-research

A research workflow skill for combining IBES estimates, company fundamentals, equity prices, and macro data into structured investment snapshots. It is for equity analysis that links tool outputs to a thesis and presents estimates, financials, valuation, and risks.

Key features

01Gather consensus estimates and fundamentals
02Analyze historical prices and macro indicators
03Chain data tools into a research workflow
04Present standardized tables and an investment thesis

Use cases

01Prepare a structured company research snapshot
02Compare consensus expectations with fundamentals
03Assess price performance alongside macro context

MCP support

The skill uses MCP tools for analyst estimates, company fundamentals, equity prices, pricing summaries, and macroeconomic indicators. It specifies how to chain these tools into an equity research note.

Install

Copy the command and run it in a terminal.

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

After installing, type/equity-researchin Claude Code to confirm it loaded.

Details

From the project README

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

Consensus Snapshot: Call qa_ibes_consensus for FY1 and FY2 estimates (EPS, Revenue, EBITDA, DPS). Note analyst count and dispersion.
Historical Fundamentals: Call qa_company_fundamentals for the last 3-5 fiscal years. Extract revenue growth, margins, leverage, returns (ROE, ROIC).
Price Performance: Call qa_historical_equity_price for 1Y history. Compute YTD return, 1Y return, 52-week range position, beta.
Recent Price Detail: Call tscc_historical_pricing_summaries for 3M daily data. Assess volume trends and recent momentum.
Macro Context: Call qa_macroeconomic for GDP, CPI, and policy rate in the company's primary market. Summarize whether macro is tailwind or headwind.
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).

Comments

Markdown supported

Details

Author: anthropics
Repository: anthropics/financial-services
Category: Data & databases
Installs: 3,201
GitHub stars: 40,886

Report

More skills

All skills

hyperframes-core

github.com/heygen-com

930.6k

The HyperFrames composition contract — build one renderable project. Use for composition structure, the `data-*` timing attributes, `class="clip"`, tracks, sub-compositions, variables, framework-owned media playback, deterministic-render rules, and validation. Also covers Tailwind projects and the STORYBOARD.md / SCRIPT.md plan formats. Read before writing composition HTML.

lark-base

open.feishu.cn

823.2k

Operate on Feishu Base: create tables, fields, records, and views; handle statistics, formulas/lookups, forms, dashboards, app mode (BaseApp/AppMode pages and components), Workspace directories, workflows, role permissions, and the template center (Base template categories, lists, and search). Use when encountering Base/multidimensional tables/bitable, BaseApp/AppMode, or /base/ or /app/ links. Do not use lark-apps for BaseApp; route file import/export to lark-drive and authentication/authorization to lark-shared.

lark-sheets

open.feishu.cn

821.8k

Feishu Spreadsheets: create and operate spreadsheets. Supports worksheets and row/column structures (add/delete/merge/size/hide/freeze/group), cell read/write (values/formulas/styles/comments/cell images), range copy/move/sort/fill, find and replace, bulk updates, and creating and maintaining objects such as charts, pivot tables, conditional formatting, filters and filter views, dropdown lists, sparklines, and floating images. Also supports formula validation, restoring historical versions, and importing/exporting local Excel/CSV files and Feishu spreadsheets. Use when users need to create or edit spreadsheets, summarize and visualize data, format spreadsheets, calculate formulas (including migrating Excel formulas), or build financial models (DCF, three statements, budgets, Sensitivity, etc.). For multidimensional tables (Base/bitable), use lark-base. If users want to search for spreadsheet files in cloud storage (Drive/cloud storage) by name or keyword, use lark-drive's drive +search to locate resources first. When users provide a doubao.com /sheets/ URL/token, use this skill directly; do not fall back to WebFetch because the domain is not Feishu. Routing is based on the URL path pattern and token, not the domain.

remotion-best-practices

github.com/remotion-dev

701.4k

Best practices and domain knowledge for building videos programmatically with Remotion (videos in React/TypeScript, rendered to MP4). Use whenever writing or editing Remotion code — scaffolding a video project, animating with interpolate/spring over useCurrentFrame(), sequencing scenes and transitions, captions/subtitles, audio, GIFs/Lottie/3D, charts, visual effects, dynamic duration via calculateMetadata, or rendering. Consult before reaching for a Remotion API, since its components and props evolve (e.g. @remotion/media).