New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Equities

by JoelLewis in JoelLewis/finance_skills

Analyze equity securities, factor models, and equity portfolio construction.

MITAuto-check passedBusiness, Finance & HR

Install Equities

skills CLI
$ npx skills add JoelLewis/finance_skills --skill equities -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install JoelLewis/finance_skills equities --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/JoelLewis/finance_skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/wealth-management/skills/equities .claude/skills/equities && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
equities
GitHub stars
205
Token cost
~2.2k tokens
SKILL.md length
1,032 words
Files
2 (incl. scripts)
Skills in repo
91
Repo updated
First seen
Licence
MIT

At a glance

Analyze equity securities, factor models, and equity portfolio construction.

  • Works in 6 steps: Classify the business — sector (GICS or… → Quality screen — revenue trend, margin… → Earnings basis — pick trailing vs… → …
  • The user asks about stocks
  • SKILL.md covers Core Concepts, Key Formulas, Worked Examples and Common Pitfalls, plus 2 more sections
  • Runs Python scripts from its folder; calls uv, python3 and python

What it does

Equities is an agent skill from JoelLewis/finance_skills. Analyze equity securities, factor models, and equity portfolio construction. Use when the user asks about stocks, equity valuation ratios, index construction methods, or style analysis. Also trigger when users mention 'P/E ratio', 'growth vs value', 'market cap weighting', 'sector allocation', 'GICS classification', 'earnings per share', 'Fama-French factors', 'CAPM', 'dividend yield', 'PEG ratio', 'EV/EBITDA', or ask which factors explain equity returns.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/equities.py`).

It sits in Business, Finance & HR. The repository describes itself as: Claude Code skill plugins for financial services — 81 skills across 7 domain plugins covering investment management, compliance, advisory practice, trading, and operations. The licence is MIT.

When your agent uses it

  • The user asks about stocks
  • Equity valuation ratios
  • Index construction methods
  • Users mention P/E ratio

Example prompts

  • “P/E ratio”
  • “growth vs value”
  • “market cap weighting”
  • “/equities”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Classify the business — sector (GICS or equivalent), cyclical vs defensive, capital intensity, leverage. This determines the valuation…
  2. Quality screen — revenue trend, margin trend, ROIC vs cost of capital, balance-sheet risk (net debt/EBITDA, interest coverage), share…
  3. Earnings basis — pick trailing vs forward EPS, check for one-offs, use diluted share count. For cyclicals, normalize to mid-cycle.
  4. Value with the matched metric — primary multiple from the table, one cross-check multiple, and where dividends are central a…
  5. Factor and style context — regress (or eyeball) exposures to market beta, size, value, momentum, quality. Distinguish stock-specific…
  6. Portfolio fit — marginal effect on sector concentration and factor tilts; total return (price + dividends) is the comparison basis, never…

What it can do on your machine

Read from SKILL.md and the folder at commit 5c498ea. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • uv
    • python3
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Equities loads about 2.2k tokens when it runs. Until then it costs about 117 tokens; SKILL.md has 1,032 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~117
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from JoelLewis/finance_skills at commit 5c498ea, republished under its MIT licence (© JoelLewis). 1,032 words, ~2,204 tokens.

Download SKILL.mdSave it as .claude/skills/equities/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
equities
description
Analyze equity securities, factor models, and equity portfolio construction. Use when the user asks about stocks, equity valuation ratios, index construction methods, or style analysis. Also trigger when users mention 'P/E ratio', 'growth vs value', 'market cap weighting', 'sector allocation', 'GICS classification', 'earnings per share', 'Fama-French factors', 'CAPM', 'dividend yield', 'PEG ratio', 'EV/EBITDA', or ask which factors explain equity returns.

Equities

This skill is a decision procedure: which valuation metric to use for which company, which index methodology fits which mandate, and the order of operations for analyzing a stock. It assumes the user can look up definitions; the value here is choosing the right tool.

Core Concepts

Choosing the Valuation Metric

Match the metric to the sector and capital structure — using the wrong one is the most common equity-analysis error.

SituationUseAvoidWhy
Financials (banks, insurers)P/B, P/TBV, ROE vs P/BEV/EBITDADebt is raw material, not financing — EV and EBITDA are meaningless; book value is marked closer to fair value
Capital-intensive (industrials, telecom, energy)EV/EBITDA, EV/EBITP/E aloneNeutralizes depreciation policy and leverage differences across peers
Mature dividend payers (utilities, staples)Dividend yield + payout sustainability, P/EPEGGrowth is low and stable; income and coverage matter most
High-growth, low/no earningsEV/Sales, PEG (if earnings exist), unit economicsP/E, P/BEarnings are depressed by reinvestment; book value is mostly intangibles
Cyclicals (autos, semis, materials)Mid-cycle or normalized P/E, P/B at troughSpot P/EP/E is lowest at the cycle peak and highest at the trough — spot P/E inverts the buy/sell signal
Negative earnings, positive cash flowEV/EBITDA, P/FCFP/E, earnings yieldRatio is undefined or misleading with negative denominator
REITs and listed real estateP/FFO, P/AFFO, NAVP/EGAAP depreciation distorts earnings for property — handled in detail by the real-assets skill
Cross-border / different leverageEV-based multiplesEquity multiplesEnterprise value normalizes for capital structure

Cross-checks that apply everywhere:

  • Use forward (next-12-month) estimates for the numerator decision when the business is changing; trailing figures when estimate quality is poor.
  • Compare against the company's own history and a true peer set, not the whole market.
  • Translate any multiple into its implied assumptions (growth, margin, required return) before declaring cheap/expensive — a low multiple usually encodes a real problem.
Choosing the Index Methodology
MandateMethodologyTrade-off to flag
Cheap, tax-efficient market exposureCap-weighted (S&P 500, total market)Momentum-chasing by construction; concentration in mega-caps — a single sector can exceed 30%
Reduce concentration / small-cap tiltEqual-weightedHigher turnover and rebalancing cost; structural size and contrarian tilt
Break the price-weight linkFundamental-weighted (revenue, earnings, book)Effectively a value tilt with extra steps; compare cost vs an explicit value fund
Explicit factor exposureFactor/style index (value, momentum, quality, low vol)Verify the factor definition and rebalance rules; factor timing rarely works
AvoidPrice-weighted (DJIA-style)Weight proportional to share price is economically arbitrary — legacy only

Selection rules: default to cap-weighted for core beta; add equal- or fundamental-weighted only when the user explicitly wants the embedded tilt and accepts the turnover; treat any "smart beta" product as a factor portfolio and evaluate its factor loadings, not its marketing name.

Security Analysis Sequence
  1. Classify the business — sector (GICS or equivalent), cyclical vs defensive, capital intensity, leverage. This determines the valuation toolkit (table above).
  2. Quality screen — revenue trend, margin trend, ROIC vs cost of capital, balance-sheet risk (net debt/EBITDA, interest coverage), share count trajectory (dilution vs buybacks).
  3. Earnings basis — pick trailing vs forward EPS, check for one-offs, use diluted share count. For cyclicals, normalize to mid-cycle.
  4. Value with the matched metric — primary multiple from the table, one cross-check multiple, and where dividends are central a dividend-based check (Gordon growth: P = D1 / (r - g), valid only when g < r).
  5. Factor and style context — regress (or eyeball) exposures to market beta, size, value, momentum, quality. Distinguish stock-specific thesis from a factor bet you could buy more cheaply via an index.
  6. Portfolio fit — marginal effect on sector concentration and factor tilts; total return (price + dividends) is the comparison basis, never price return alone.
Show full SKILL.md (426 more words)Show less

Key Formulas

FormulaExpressionUse Case
EV/EBITDA(Market Cap + Debt - Cash) / EBITDACapital-structure-neutral valuation
Earnings YieldEPS / PriceCompare equity vs bond yields
PEG(P/E) / Earnings Growth Rate (in %)Growth-adjusted valuation
Gordon GrowthP = D1 / (r - g)Dividend-based intrinsic value
CAPME(R) = R_f + beta × (E(R_m) - R_f)Required return input for valuation
Total ReturnPrice Return + Dividend ReturnPerformance comparison basis

Worked Examples

Metric Selection and Valuation

Given: An industrial company with market cap $500M, total debt $100M, cash $50M, EBITDA $75M, EPS $7.50, price $150. Decide and calculate:

  1. Capital-intensive industrial → primary metric is EV/EBITDA (table above), with P/E as cross-check.
  2. EV = $500M + $100M - $50M = $550M. EV/EBITDA = $550M / $75M = 7.33x.
  3. Cross-check: P/E = $150 / $7.50 = 20.0x; earnings yield = 7.50 / 150 = 5.0%.
  4. Interpretation: 7.33x EV/EBITDA is modest for an industrial if margins are stable — compare against the peer set and the company's own 5-10 year range. The 20x P/E looks richer than the EV multiple because the company carries little net debt; the EV multiple is the better cross-peer comparison.

Common Pitfalls

  • Applying EV/EBITDA to banks or P/E to REITs — metric/sector mismatch is the dominant error this skill exists to prevent
  • Buying cyclicals on low trailing P/E at the cycle peak (the "value trap" inversion)
  • Treating a fundamental-weighted or smart-beta index as alpha rather than a packaged factor tilt
  • Confusing price return with total return — dividends compound to a large share of long-run equity returns
  • Survivorship bias in backtested factor or screen results

Cross-References

  • historical-risk (wealth-management plugin): volatility and drawdown measurement for equity return series
  • statistics-fundamentals (core plugin): beta estimation via CAPM regression
  • performance-metrics (wealth-management plugin): Sharpe ratio and related risk-adjusted return measures
  • fund-vehicles (wealth-management plugin): equity fund selection (ETFs, mutual funds, SMAs)
  • currencies-and-fx (wealth-management plugin): international equity currency effects
  • asset-allocation (wealth-management plugin): equity allocation within multi-asset portfolios
  • real-assets (wealth-management plugin): REIT valuation (P/FFO, NAV) is owned by that skill
  • qualitative-valuation (wealth-management plugin) and quantitative-valuation (wealth-management plugin): deeper single-company valuation workflows
  • financial-statements (wealth-management plugin): EBITDA, free cash flow, ROIC, and margin analysis underpinning fundamental stock selection
  • equity-compensation (wealth-management plugin): employer stock acquired through RSUs, options, and ESPPs carries equity risk plus tax and insider-trading constraints
  • factor-investing (wealth-management plugin): the factor-loading evaluation of smart-beta and style products prescribed above lives in that skill

Running the Script

bash
uv run scripts/equities.py            # run the demo (uses PEP 723 inline deps)
uv run scripts/equities.py --verify   # check demo outputs against the worked example (exit 1 on mismatch)
python3 scripts/equities.py            # alternative (requires: pip install numpy)

The demo prints valuation metrics (including the worked example's EV/EBITDA and earnings yield), a factor regression on synthetic data, and sector concentration analysis. Run --help for a list of the classes and functions. For programmatic use, import the module rather than running it — the demo only executes under python equities.py.

© JoelLewis, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (scripts) in plugins/wealth-management/skills/equities of JoelLewis/finance_skills.

  • SKILL.md
  • scripts/equities.py

Open the folder on GitHubat commit 5c498ea

Compare with similar skills

Equities next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Equities compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Equities this skillJoelLewis/finance_skills205—~2.2kAutomated safety check: PassMIT
Technical Analysttradermonty/claude-trading-skills3k4 repos~4.6kAutomated safety check: PassMIT
Theme Detectortradermonty/claude-trading-skills3k2 repos~4.9kAutomated safety check: PassMIT
Creating Financial ModelsChen-zexi/open-ptc-agent7293 repos~1.3kAutomated safety check: PassMIT
Stock APIzhangxiangliang/stock-api2k—~507Automated safety check: PassMIT
Itr Walakaranb192/itr-wala871—~3.6kAutomated safety check: PassMIT

Similar skills

  • Technical Analyst

    tradermonty/claude-trading-skills

    This skill should be used when analyzing weekly price charts for stocks, stock indices, cryptocurrencies, or forex pairs.

    3k GitHub starsUsed in 4 repos~4.6k tokens
    Business, Finance & HRAuto-check passed

  • Theme Detector

    tradermonty/claude-trading-skills

    Detect and analyze trending market themes across sectors. An agent skill from tradermonty/claude-trading-skills.

    3k GitHub starsUsed in 2 repos~4.9k tokens
    Business, Finance & HRAuto-check passed

  • Creating Financial Models

    Chen-zexi/open-ptc-agent

    This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions

    729 GitHub starsUsed in 3 repos~1.3k tokens
    Business, Finance & HRAuto-check passed

  • Stock API

    zhangxiangliang/stock-api

    Fetch real-time stock quotes, K-line (candlestick) history, and search symbols for China A-shares, Hong Kong, and US markets.

    2k GitHub stars~507 tokensUpdated today
    Business, Finance & HRAuto-check passed

  • Itr Wala

    karanb192/itr-wala

    File Indian income tax returns (ITR) for FY 2025-26 / AY 2026-27.

    871 GitHub stars~3.6k tokensUpdated 5 days ago
    Business, Finance & HRAuto-check passed

  • Tushare Data

    zillionare/zillionare

    面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求，转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。

    321 GitHub starsUsed in 2 repos~2.3k tokens
    Business, Finance & HRAuto-check passed

More from JoelLewis/finance_skills

All 91 skills in this repo

  • Asset Allocation

    JoelLewis/finance_skills

    Determine how to distribute capital across asset classes using strategic and tactical allocation frameworks.

    205 GitHub stars~2.5k tokensUpdated 2 mo ago
    Auto-check passed

  • Bet Sizing

    JoelLewis/finance_skills

    Determine how much capital to allocate to individual positions within a portfolio.

    205 GitHub stars~2.5k tokensUpdated 2 mo ago
    Auto-check passed

  • Commodities

    JoelLewis/finance_skills

    Analyze commodity markets including futures curve dynamics, roll yield, and supply/demand fundamentals.

    205 GitHub stars~1.9k tokensUpdated 2 mo ago
    Auto-check passed

  • Currencies And Fx

    JoelLewis/finance_skills

    Analyze currency markets, exchange rate mechanics, and FX risk management for international portfolios.

    205 GitHub stars~1.9k tokensUpdated 2 mo ago
    Auto-check passed

  • Debt Management

    JoelLewis/finance_skills

    Provide frameworks for managing and paying off personal debt effectively.

    205 GitHub stars~2.5k tokensUpdated 2 mo ago
    Auto-check passed

  • Diversification

    JoelLewis/finance_skills

    Build diversified portfolios using correlation analysis, efficient frontier construction, and factor-based diversification.

    205 GitHub stars~2.3k tokensUpdated 2 mo ago
    Auto-check passed

Categories

Questions about Equities

What does Equities do?

Analyze equity securities, factor models, and equity portfolio construction. Equities is an agent skill from JoelLewis/finance_skills. Analyze equity securities, factor models, and equity portfolio construction.

When should I use Equities?

Equities fits situations like: the user asks about stocks; equity valuation ratios; index construction methods; users mention P/E ratio.

How do I install Equities in Claude Code?

Run `npx skills add JoelLewis/finance_skills --skill equities -a claude-code`. Or copy the skill folder (plugins/wealth-management/skills/equities in JoelLewis/finance_skills) into .claude/skills/equities in your project. Claude Code loads it when a task matches its description.

How do I install Equities in Codex?

Run `npx skills add JoelLewis/finance_skills --skill equities -a codex`. Or copy the skill folder (plugins/wealth-management/skills/equities in JoelLewis/finance_skills) into .agents/skills/equities in your project. Codex loads it when a task matches its description.

Can I use Equities in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add JoelLewis/finance_skills --skill equities -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/equities, .gemini/skills/equities, .github/skills/equities and .opencode/skills/equities in your project.

What does Equities need to run?

Going by SKILL.md and its folder, Equities needs Python for the scripts in its folder and the command-line tools its instructions call (uv, python3 and python). Our summary lists: Python 3.

Does Equities access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Equities safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Equities use?

Equities is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Equities use?

About 2.2k tokens (SKILL.md is roughly 8.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Equities?

Skills that share tags, products or a category with Equities: Technical Analyst (tradermonty/claude-trading-skills, 3k stars), Theme Detector (tradermonty/claude-trading-skills, 3k stars), Creating Financial Models (Chen-zexi/open-ptc-agent, 729 stars) and Stock API (zhangxiangliang/stock-api, 2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Equities?

JoelLewis (a GitHub user) maintains it in JoelLewis/finance_skills, which has 205 GitHub stars. The repository holds 91 skills in this directory. The repository was last updated on July 18, 2026.

Source: JoelLewis/finance_skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.