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 | $0.00112 | $0.01624 |
| Opus 5.5 | $0.00045 | $0.00650 |
| Sonnet 5.5 | $0.00022 | $0.00325 |
| Haiku 4.5 | $0.00011 | $0.00162 |
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 6d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Earnings Recap Skill
If your host has not already executed the shell probes below (for example, in Codex), run the commands inside the ! wrappers with its shell tool before choosing a setup path. Use the actual output; if shell access is unavailable, report that limitation.
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
ticker = yf.Ticker("AAPL") # replace with actual ticker
# --- Earnings results ---
earnings_dates = ticker.get_earnings_dates(limit=12) # report timestamps, newest first
earnings_hist = ticker.earnings_history # last 4 quarters, indexed by fiscal quarter-end, oldest first
# --- Financial statements (about five quarters, newest first) ---
quarterly_income = ticker.quarterly_income_stmt
quarterly_cashflow = ticker.quarterly_cashflow
quarterly_balance = ticker.quarterly_balance_sheet
# --- Context ---
info = ticker.info
news = ticker.news
recommendations = ticker.recommendations
What to extract
| Data Source | Key Fields | Purpose |
|---|---|---|
get_earnings_dates() |
Earnings Date, EPS Estimate, Reported EPS, Surprise(%) | Which report, when, and the beat/miss |
earnings_history |
epsEstimate, epsActual, epsDifference, surprisePercent | Last four quarters' results by fiscal quarter |
quarterly_income_stmt |
TotalRevenue, GrossProfit, OperatingIncome, NetIncome, BasicEPS | Actual financials |
history() |
Daily closes around each report | 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.
- 6d ago Changed · +2 lines 203d09612f1e
- 1mo ago Changed · -54 lines · -74 tokens per session 6f1b9e05798b
- 1mo 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 (3,451 stars, last pushed yesterday), licensed MIT. It adds 112 tokens to every session and 1,624 once invoked, about $0.0004 per session on Opus 5.5. 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.
rhagent
Trade and post on rhagent.bot via Bankr. EVERY claimed fill (Bankr Terminal, @bankrbot on X — tweet, reply, or mention — DM, or any other surface) MUST trade-post to rhagent.bot BEFORE the reply. Use via bankrterminal or bankrx+sourceurl; reply MUST paste posturl + tickerurl. Explorer/Blockscout alone is a failure.…
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…