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Alphagbm Vol Surface

CommunityPopular
AlphaGBM
alphagbm-vol-surface

3D volatility surface analysis mapping implied volatility across strikes (moneyness) and expirations. Shows whether options are cheap or expensive at every point on the surface. Returns surface grid data, ATM term structure, skew by expiry, and surface anomalies. Use when: checking if IV is expensive, analyzing term structure, finding mispriced options, understanding volatility dynamics. Triggers on: "vol surface AAPL", "is NVDA IV expensive", "volatility term structure SPY", "surface analysis TSLA", "IV surface META", "show me the vol surface".

Overview

PublisherAlphaGBM
Repositoryskills
Skill namealphagbm-vol-surface
Stars
2.7K
Forks
290
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by AlphaGBM on GitHub. Read the source before you install it.

Installation

Install the Alphagbm Vol Surface AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/AlphaGBM/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/alphagbm-vol-surface .claude/skills/alphagbm-vol-surface
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Alphagbm Vol Surface in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Alphagbm Vol Surface on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Alphagbm Vol Surface is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AlphaGBM Volatility Surface

Prerequisites

  • API Key: Set env ALPHAGBM_API_KEY (format agbm_xxxx...).
  • Base URL: Default https://alphagbm.zeabur.app. Override with env ALPHAGBM_BASE_URL.

What This Skill Does

Builds a 3D volatility surface for any optionable ticker, mapping implied volatility across two dimensions -- strike price (moneyness) and time to expiration. Identifies where options are cheap, expensive, or anomalous relative to the surface.

Key Outputs

OutputWhat It Shows
Surface GridIV at each (strike, expiry) coordinate -- the full 3D map
ATM Term StructureHow at-the-money IV changes across expirations (front-month vs. back-month)
Skew by ExpiryPut-call IV differential at each expiration -- measures fear/complacency
Surface AnomaliesPoints where IV deviates significantly from the fitted surface -- potential mispricings
Surface ShapeClassification: contango, backwardation, flat, inverted, event-driven

What the Surface Tells You

  • Contango (front IV < back IV): Normal market, no near-term fear
  • Backwardation (front IV > back IV): Near-term event expected (earnings, FDA, etc.)
  • Steep skew: Market pricing tail risk in puts -- hedging demand is high
  • Flat skew: Balanced sentiment -- no strong directional fear
  • Anomaly detected: A specific contract is mispriced vs. neighbors -- potential opportunity

Volatility Risk Premium (VRP)

VRP = Implied Vol - Historical Vol
VRP LevelSellerBuyer
very_high (>=15%)Very favorableUnfavorable
high (5-15%)FavorableSlightly unfavorable
normal (+/-5%)NeutralNeutral
low (-15% to -5%)UnfavorableFavorable
very_low (<-15%)Very unfavorableVery favorable

API Endpoints

Volatility Surface (3D)

GET /api/options/tools/vol-surface/<SYMBOL>

Returns the full 3D volatility surface with moneyness axis, expiry axis, and IV grid.

IV Snapshot (quick check, no quota cost)

For a fast ATM IV / IV Rank / HV / VRP check without pulling the full surface:

GET /api/options/snapshot/<SYMBOL>

Returns: ATM IV, IV Rank, HV 30d, VRP, VRP level.

How to Use

Input

  • Required: Ticker symbol
  • Optional: Moneyness range (e.g., 0.8-1.2), expiration range (e.g., 7-90 days)

Output Structure

json
{
  "ticker": "AAPL",
  "price": 218.45,
  "surface": {
    "moneyness_axis": [0.85, 0.90, 0.95, 1.00, 1.05, 1.10, 1.15],
    "expiry_axis": ["2026-04-04", "2026-04-18", "2026-05-16", "2026-06-20"],
    "iv_grid": [
      [38.2, 34.5, 31.0, 28.5, 30.2, 33.1, 36.8],
      [36.1, 33.0, 29.8, 27.2, 28.9, 31.5, 34.9],
      [34.5, 31.8, 28.5, 26.0, 27.5, 30.0, 33.2],
      [33.0, 30.5, 27.8, 25.5, 26.8, 29.0, 31.8]
    ]
  },
  "atm_term_structure": {
    "2026-04-04": 28.5,
    "2026-04-18": 27.2,
    "2026-05-16": 26.0,
    "2026-06-20": 25.5
  },
  "skew": {
    "2026-04-18": {"25d_put_iv": 33.0, "25d_call_iv": 28.9, "skew": -4.1}
  },
  "shape": "contango",
  "anomalies": [
    {
      "strike": 200,
      "expiry": "2026-04-18",
      "iv": 38.5,
      "expected_iv": 34.2,
      "deviation_sigma": 2.3,
      "signal": "overpriced"
    }
  ]
}

Example Queries

User SaysWhat Happens
"Vol surface AAPL"Full 3D surface with term structure, skew, anomalies
"Is NVDA IV expensive?"Compares current surface to 30-day historical average
"Volatility term structure SPY"ATM IV across all expirations with shape classification
"Surface analysis TSLA"Full surface + anomaly detection for mispriced contracts
"Front-month vs back-month IV for META"Term structure with contango/backwardation call

Mock Data

Demo tickers available without API key: AAPL, NVDA, SPY, TSLA, META. Surface data uses realistic IV snapshots from mock-data/.

Related Skills

  • alphagbm-vol-smile -- Zoom into a single expiration's smile/skew curve
  • alphagbm-iv-rank -- Is IV high or low vs. its own history?
  • alphagbm-options-score -- Use surface insights to find the best-scored contracts
  • alphagbm-options-strategy -- High IV surface suggests selling premium; low IV suggests buying

Powered by AlphaGBM -- Real-data options & research intelligence for traders and AI agents. 10K+ users.

Frequently asked questions

What does the Alphagbm Vol Surface AI skill do?

3D volatility surface analysis mapping implied volatility across strikes (moneyness) and expirations. Shows whether options are cheap or expensive at every point on the surface. Returns surface grid data, ATM term structure, skew by expiry, and surface anomalies. Use when: checking if IV is expensive, analyzing term structure, finding mispriced options, understanding volatility dynamics. Triggers on: "vol surface AAPL", "is NVDA IV expensive", "volatility term structure SPY", "surface analysis TSLA", "IV surface META", "show me the vol surface".

Why use Alphagbm Vol Surface on TypingMind?

Because you install it once and use it with any model. Alphagbm Vol Surface is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Alphagbm Vol Surface in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AlphaGBM/skills/tree/main/skills/alphagbm-vol-surface. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Alphagbm Vol Surface?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Alphagbm Vol Surface?

As many as you like. As long as a model supports skills, you can use Alphagbm Vol Surface with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Alphagbm Vol Surface AI skill free?

Yes. It is published on GitHub by AlphaGBM under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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