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-recapgit 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-recap)<a href="https://agentmods.dev/skills/himself65/finance-skills/earnings-recap"><img src="https://agentmods.dev/badge/skills/himself65/finance-skills/earnings-recap/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-recap"><img src="https://agentmods.dev/badge/skills/himself65/finance-skills/earnings-recap.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 | $0.00186 | $0.01831 |
| Opus 5 | $0.00093 | $0.00915 |
| Sonnet 5 | $0.00037 | $0.00366 |
| Haiku 4.5 | $0.00019 | $0.00183 |
Grade A, and why
earnings-recap 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 11d 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Earnings Recap Skill
Generates a post-earnings analysis using Yahoo Finance data via yfinance. Covers the actual vs estimated numbers, surprise magnitude, stock price reaction, and financial context — a complete picture of what happened.
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 Data
Extract the ticker from the user's request. Fetch all relevant post-earnings data in one script.
import yfinance as yf
import pandas as pd
from datetime import datetime, timedelta
ticker = yf.Ticker("AAPL") # replace with actual ticker
# --- Earnings result ---
earnings_hist = ticker.earnings_history
# --- Financial statements ---
quarterly_income = ticker.quarterly_income_stmt
quarterly_cashflow = ticker.quarterly_cashflow
quarterly_balance = ticker.quarterly_balance_sheet
# --- Price reaction ---
# Get ~30 days of history to capture the reaction window
hist = ticker.history(period="1mo")
# --- Context ---
info = ticker.info
news = ticker.news
recommendations = ticker.recommendations
What to extract
| Data Source | Key Fields | Purpose |
|---|---|---|
earnings_history |
epsEstimate, epsActual, epsDifference, surprisePercent | Beat/miss result |
quarterly_income_stmt |
TotalRevenue, GrossProfit, OperatingIncome, NetIncome, BasicEPS | Actual financials |
history() |
Close prices around earnings date | Stock price reaction |
info |
currentPrice, marketCap, forwardPE | Current context |
news |
Recent headlines | Earnings-related news |
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.
- 11d ago First seen · 190 lines · 186 tokens per session scan A 0cd4f5b93404
earnings-recap is a skill published in the GitHub repository himself65/finance-skills (3,309 stars, last pushed 14d ago), licensed MIT. It adds 186 tokens to every session and 1,831 once invoked, about $0.0009 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.
Other skills, from other repositories
quotient
Prediction-market intelligence for Polymarket agents. Quotient runs a multi-role AI forecasting pipeline over 1,600+ sources and publishes daily trade signals with side, entry prices, conviction tiers, capacity, and convergence reads. Pull forecasts (with what-changed deltas), recent sources (articles + X posts), the…
delu-oracle
Full-cognition token analysis for Base EVM tokens via the deluagent oracle. Pass a CA or cashtag, get back a flat decision header (action, conviction, entry/stop/size, read) plus full cognition report. Tiered x402 pricing — 100M+ DELU free, 50M+ 50k DELU, public 250k DELU. Sequential calls only.
lonestaroracle-data
Live pay-per-call data for crypto and DeFi protocol risk, funding rates, open interest, liquidations, stablecoin health, macro, equities, and on-chain intelligence — settled per query in USDC on Base via x402, no signup or API key.
backtesting-sim
Backtesting and simulation: vectorized backtesting, paper trading simulation, strategy A/B testing, automated strategy building, natural language to strategy, and trading plan generation. USE FOR: backtest, backtesting, paper trading, simulation, strategy builder, A/B test strategies, natural language strategy…
cross-asset-relationships
Cross-asset and quantitative analysis: pair correlations, correlation heatmaps, currency strength, cross-timeframe divergence, intermarket analysis, market breadth, carry trades, swap rates, risk premia, and multi-pair baskets. USE FOR: correlation, currency strength, intermarket, market breadth, carry trade, swap…
freqtrade-bot
Freqtrade — open-source Python crypto trading bot. Backtesting, hyperopt (ML parameter optimization), FreqAI (self-training adaptive strategies), Telegram + WebUI control. Supports Binance, Kraken, Bybit, OKX, Gate.io (spot + futures). SQLite trade h.