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 mphinance/alpha-skills --skill options-strategy-advisorgit clone --depth 1 https://github.com/mphinance/alpha-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/mphinance/alpha-skills/options-strategy-advisor)<a href="https://agentmods.dev/skills/mphinance/alpha-skills/options-strategy-advisor"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/options-strategy-advisor/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/mphinance/alpha-skills/options-strategy-advisor"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/options-strategy-advisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00084 | $0.07918 |
| Opus 5.5 | $0.00034 | $0.03167 |
| Sonnet 5.5 | $0.00017 | $0.01584 |
| Haiku 4.5 | $0.00008 | $0.00792 |
Grade A, and why
options-strategy-advisor scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
94% identical to options-strategy-advisor — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 991 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Options Strategy Advisor
Overview
This skill provides comprehensive options strategy analysis and education using theoretical pricing models. It helps traders understand, analyze, and simulate options strategies without requiring real-time market data subscriptions.
Core Capabilities:
- Black-Scholes Pricing: Theoretical option prices and Greeks calculation
- Strategy Simulation: P/L analysis for major options strategies
- Earnings Strategies: Pre-earnings volatility plays integrated with Earnings Calendar
- Risk Management: Position sizing, Greeks exposure, max loss/profit analysis
- Educational Focus: Detailed explanations of strategies and risk metrics
Data Sources:
- FMP API: Stock prices, historical volatility, dividends, earnings dates
- User Input: Implied volatility (IV), risk-free rate
- Theoretical Models: Black-Scholes for pricing and Greeks
Prerequisites
Required:
- Python 3.8+ with
numpy,scipy,requests
Optional:
- FMP API key (for real-time stock prices and historical volatility)
- Set via
FMP_API_KEYenvironment variable or--api-keyargument - Without API key: Use manual inputs for stock price and volatility
- Set via
Installation:
pip install numpy scipy requests
Quick Start Examples:
# Basic call option pricing (no API key needed)
python3 scripts/black_scholes.py
# With FMP API key for real-time data
python3 scripts/black_scholes.py --ticker AAPL --api-key $FMP_API_KEY
# Custom option parameters
python3 scripts/black_scholes.py --stock-price 180 --strike 185 --days 30 --volatility 0.25
# Put option analysis
python3 scripts/black_scholes.py --stock-price 180 --strike 175 --days 30 --option-type put
When to Use This Skill
Use this skill when:
- User asks about options strategies ("What's a covered call?", "How does an iron condor work?")
- User wants to simulate strategy P/L ("What's my max profit on a bull call spread?")
- User needs Greeks analysis ("What's my delta exposure?")
- User asks about earnings strategies ("Should I buy a straddle before earnings?")
- User wants to compare strategies ("Covered call vs protective put?")
- User needs position sizing guidance ("How many contracts should I trade?")
- User asks about volatility ("Is IV high right now?")
What ships with it
5 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.
- 1mo ago First seen · 991 lines · 84 tokens per session scan A aa1370abcdaf
options-strategy-advisor is a skill published in the GitHub repository mphinance/alpha-skills (28 stars, last pushed 10d ago), licensed MIT. It adds 84 tokens to every session and 7,918 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to options-strategy-advisor, differing in 14 lines, and is treated as a copy.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research through opencli: the user's home timeline, tweet search, trending topics, bookmarks, a user's recent tweets, threads, articles, profiles, followers and following, and notifications. Use this skill whenever the user wants to know what people are saying on Twitter/X or fintwit about…
energy-procurement
Codified expertise for electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management. Informed by energy procurement managers with 15+ years experience at large commercial and industrial consumers. Includes market structure analysis…
airdrop-hunting-guide
Guide to finding and qualifying for crypto airdrops — eligibility criteria, farming strategies, on-chain activity requirements, Sybil detection avoidance, and tracking tools. Use when helping users understand airdrops, check eligibility, or plan participation strategies.
china-portfolio-monitoring
A monitoring framework for tracking the financial results, operating measures, strategic milestones, and risks of companies held by China-focused private-equity funds. It uses measures such as revenue growth, margins, debt, cash flow, and return on equity.