Finance Skills is a collection of agent skills for financial analysis and trading, covering activities such as company valuation, earnings research, market analysis, and options calculations. It is for users who want coding agents to perform structured finance workflows, and the catalogue contains its skills, plugins, instructions, and MCP integration.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add himself65/finance-skills --skill earnings-previewgit clone --depth 1 https://github.com/himself65/finance-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/himself65/finance-skills/earnings-preview)<a href="https://agentmods.dev/skills/himself65/finance-skills/earnings-preview"><img src="https://agentmods.dev/badge/skills/himself65/finance-skills/earnings-preview/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/himself65/finance-skills/earnings-preview"><img src="https://agentmods.dev/badge/skills/himself65/finance-skills/earnings-preview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00221 | $0.01644 |
| Opus 5 | $0.00111 | $0.00822 |
| Sonnet 5 | $0.00044 | $0.00329 |
| Haiku 4.5 | $0.00022 | $0.00164 |
Grade A, and why
earnings-preview scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"]) How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Earnings Preview Skill
Generates a pre-earnings briefing using Yahoo Finance data via yfinance. Pulls together upcoming earnings date, consensus estimates, historical accuracy, analyst sentiment, and key financial context — everything you need before an earnings call.
Important: Data is for research and educational purposes only. Not financial advice. yfinance is not affiliated with Yahoo, Inc.
Step 1: Ensure yfinance Is Available
Current environment status:
!`python3 -c "exec('try:\n import yfinance\n print(\'yfinance \' + yfinance.__version__ + \' installed\')\nexcept Exception:\n print(\'YFINANCE_NOT_INSTALLED\')')"`
If YFINANCE_NOT_INSTALLED, install it:
import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance"])
If already installed, skip to the next step.
Step 2: Identify the Ticker and Gather All Data
Extract the ticker symbol from the user's request. If they mention a company name without a ticker, look it up. Then fetch all relevant data in one script to minimize API calls.
import yfinance as yf
import pandas as pd
from datetime import datetime
ticker = yf.Ticker("AAPL") # replace with actual ticker
# --- Core data ---
info = ticker.info
calendar = ticker.calendar
# --- Estimates ---
earnings_est = ticker.earnings_estimate
revenue_est = ticker.revenue_estimate
# --- Historical track record ---
earnings_hist = ticker.earnings_history
# --- Analyst sentiment ---
price_targets = ticker.analyst_price_targets
recommendations = ticker.recommendations
# --- Recent financials for context ---
quarterly_income = ticker.quarterly_income_stmt
quarterly_cashflow = ticker.quarterly_cashflow
What to extract from each source
| Data Source | Key Fields | Purpose |
|---|---|---|
calendar |
Earnings Date, Ex-Dividend Date | When earnings are and key dates |
earnings_estimate |
avg, low, high, numberOfAnalysts, yearAgoEps, growth (for 0q, +1q, 0y, +1y) | Consensus EPS expectations |
revenue_estimate |
avg, low, high, numberOfAnalysts, yearAgoRevenue, growth | Revenue expectations |
earnings_history |
epsEstimate, epsActual, epsDifference, surprisePercent | Beat/miss track record |
analyst_price_targets |
current, low, high, mean, median | Street price targets |
recommendations |
Buy/Hold/Sell counts | Sentiment distribution |
quarterly_income_stmt |
TotalRevenue, NetIncome, BasicEPS | Recent trajectory |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 170 lines · 221 tokens per session scan A d70f380dc495
earnings-preview is a skill published in the GitHub repository himself65/finance-skills (3,302 stars, last pushed 13d ago), licensed MIT. It adds 221 tokens to every session and 1,644 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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