Fundamentals Agent Skill

Get fundamental financial data including financials, earnings, and key metrics. Use when user asks about financials, earnings, revenue, profit, balance sheet, income statement, or company fundamentals.

1k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

308

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/staskh/trading_skills --skill fundamentals

The instruction itself

14 sections, as written by the author

Fundamentals

Fetch fundamental financial data from Yahoo Finance.

Instructions

> Note: If uv is not installed or pyproject.toml is not found, replace uv run python with python in all commands below.

uv run python scripts/fundamentals.py SYMBOL [--type TYPE]

Arguments

SYMBOL - Ticker symbol
--type - Data type: all, financials, earnings, info (default: all)

Output

Returns JSON with:

info - Key metrics (market cap, PE, EPS, dividend, etc.)
financials - Recent quarterly/annual income statement data
earnings - Historical and estimated earnings

Present key metrics clearly. Compare actual vs estimated earnings if relevant.

Piotroski F-Score

Calculate Piotroski's F-Score to evaluate a company's financial strength using 9 fundamental criteria.

Instructions

uv run python scripts/piotroski.py SYMBOL

What is Piotroski F-Score?

Piotroski's F-Score is a fundamental analysis tool developed by Joseph Piotroski that evaluates a company's financial strength using 9 criteria. Each criterion scores 1 point if passed, 0 if failed, for a maximum score of 9.

The 9 Criteria

Positive Net Income - Company is profitable
Positive ROA - Assets are generating returns
Positive Operating Cash Flow - Company generates cash from operations
Cash Flow > Net Income - High-quality earnings (cash exceeds accounting profit)
Lower Long-Term Debt - Decreasing leverage (improving financial position)
Higher Current Ratio - Improving liquidity
No New Shares Issued - No dilution (or share buybacks)
Higher Gross Margin - Improving profitability efficiency
Higher Asset Turnover - More efficient use of assets

Score Interpretation

8-9: Excellent - Very strong financial health
6-7: Good - Strong financial health
4-5: Fair - Moderate financial health
0-3: Poor - Weak financial health

Output

Returns JSON with:

score - F-Score (0-9)
max_score - Maximum possible score (9)
criteria - Detailed breakdown of each criterion with pass/fail status and values
interpretation - Text description of financial health level
data_available - Boolean indicating if year-over-year comparison data is available for criteria 5-9

Implementation Details

Criteria 1-4 use quarterly financial data (most recent year)
Criteria 5-9 use annual financial data for year-over-year comparisons
Compares most recent fiscal year vs previous fiscal year

Use Cases

Use Piotroski F-Score when:

Evaluating fundamental financial strength
Screening for value stocks with improving fundamentals
Assessing financial health trends
Comparing financial strength across companies
Identifying companies with strong fundamentals but undervalued prices

Dependencies

pandas
yfinance

Timezone

All timestamps and time-based calculations must use the America/New_York timezone. All JSON output must include generated_at (NY time string) and data_delay fields.

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825 tokens

How to use it

Copy the folder

Take staskh/fundamentals from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.