Generates a fully interactive HTML widget (via visualize:show_widget) showing:
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionoptions-payoffExecute the skills CLI command in your project's root directory to begin installation:
Fetches options-payoff from himself65/finance-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate options-payoff. Access via /options-payoff in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Generates a fully interactive HTML widget (via visualize:show_widget) showing:
When the user provides a screenshot or text, extract:
| Field | Where to find it | Default if missing |
|---|---|---|
| Strategy type | Title bar / leg description | "custom" |
| Underlying | Ticker symbol | SPX |
| Strike(s) | K1, K2, K3... in title or leg table | nearest round number |
| Premium paid/received | Filled price or avg price | 5.00 |
| Quantity | Position size | 1 |
| Multiplier | 100 for equity options, 100 for SPX | 100 |
| Expiry | Date in title | 30 DTE |
| Spot price | Current underlying price (NOT strike) | middle strike |
| IV | Shown in greeks panel, or estimate from vega | 20% |
| Risk-free rate | — | 4.3% |
Critical for screenshots: The spot price is the CURRENT price of the underlying index/stock, NOT the strikes. Never default spot to a strike price value.
Current SPX reference price:
!`python3 -c "import yfinance as yf; print(f'SPX ≈ {yf.Ticker(\"^GSPC\").fast_info[\"lastPrice\"]:.0f}')" 2>/dev/null || echo "SPX price unavailable — check market data"`
Match to one of the supported strategies below, then read the corresponding section in references/strategies.md.
| Strategy | Legs | Key Identifiers |
|---|---|---|
| butterfly | Buy K1, Sell 2×K2, Buy K3 | 3 strikes, "Butterfly" in title |
| vertical_spread | Buy K1, Sell K2 (same expiry) | 2 strikes, debit or credit |
| calendar_spread | Buy far-expiry K, Sell near-expiry K | Same strike, 2 expiries |
| iron_condor | Sell K2/K3, Buy K1/K4 wings | 4 strikes, 2 spreads |
| straddle | Buy Call K + Buy Put K | Same strike, both types |
| strangle | Buy OTM Call + Buy OTM Put | 2 strikes, both OTM |
| covered_call | Long 100 shares + Sell Call K | Stock + short call |
| naked_put | Sell Put K | Single leg |
| ratio_spread | Buy 1×K1, Sell N×K2 | Unequal quantities |
For strategies not listed, use custom mode: decompose into individual legs and sum their P&Ls.
d1 = (ln(S/K) + (r + σ²/2)·T) / (σ·√T)
d2 = d1 - σ·√T
put = K·e^(-rT)·N(-d2) - S·N(-d1)
call = put + S - K·e^(-rT)
if S >= K3: 0
if S >= K2: K3 - S
if S >= K1: S - K1
else: 0
Net P&L per share = payoff − premium_paid
long_call = max(S - K1, 0)
short_call = max(S - K2, 0)
payoff = long_call - short_call - net_debit
Calendar cannot be expressed as a simple expiry function — always use BS pricing for both legs:
value = BS(S, K, T_far, r, IV_far) - BS(S, K, T_near, r, IV_near)
For expiry curve of calendar: near leg expires worthless, far leg = BS with remaining T.
put_spread = max(K2-S, 0) - max(K1-S, 0) // short put spread
call_spread = max(S-K3, 0) - max(S-K4, 0) // short call spread
payoff = credit_received - put_spread - call_spread
Use visualize:read_me with modules ["chart", "interactive"] before building.
Structure section:
Pricing variables section:
Spot price:
Use this JS structure inside the widget, adapting pnlExpiry() and bfTheory() per strategy:
// Black-Scholes helpers (always include)
function normCDF(x) { /* Horner approximation */ }
function bsCall(S,K,T,r,sig) { /* standard BS call */ }
function bsPut(S,K,T,r,sig) { /* standard BS put */ }
// Strategy-specific expiry payoff (returns per-share value BEFORE premium)
function expiryValue(S, ...strikes) { ... }
// Strategy-specific theoretical value using BS
function theoreticalValue(S, ...strikes, T, r, iv) { ... }
// Main update() reads all sliders, computes arrays, destroys+recreates Chart.js instance
function update() { ... }
// Attach listeners
['k1','k2',...,'iv','dte','rate','spot'].forEach(id => {
document.getElementById(id).addEventListener('input', update);
});
update();
After rendering the widget, briefly explain:
Keep it concise — the chart speaks for itself.
references/strategies.md — Detailed payoff formulas and edge cases for each strategy typereferences/bs_code.md — Copy-paste ready Black-Scholes JS implementation with normCDFRead the relevant reference file if you're unsure about payoff formula edge cases for a given strategy.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Solid pick for teams standardizing on skills: options-payoff is focused, and the summary matches what you get after install.
I recommend options-payoff for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added options-payoff from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
options-payoff fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend options-payoff for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in options-payoff — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
options-payoff reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: options-payoff is the kind of skill you can hand to a new teammate without a long onboarding doc.
options-payoff is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: options-payoff is the kind of skill you can hand to a new teammate without a long onboarding doc.
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