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Alphagbm Options Score

CommunityPopular
AlphaGBM
alphagbm-options-score

Score and rank options contracts for any ticker using AlphaGBM's multi-factor scoring model (liquidity, IV attractiveness, Greeks balance, risk/reward). Returns scored option chains with the best contracts highlighted. Use when: evaluating which option to trade, finding the best strike/expiry, ranking options by quality. Triggers on: "score AAPL options", "best options for NVDA", "which TSLA call should I buy", "option chain for SPY", "rank META puts".

Overview

PublisherAlphaGBM
Repositoryskills
Skill namealphagbm-options-score
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 Options Score 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-options-score .claude/skills/alphagbm-options-score
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Alphagbm Options Score 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 Options Score 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 Options Score 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 Options Score

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

Scores every option contract in a chain using a multi-factor model across 4 strategy types, so you instantly know which contracts have the best risk/reward profile.

Strategy Scoring Models

Sell Put Weights
FactorWeightDescription
premium_yield20%Annualized return from premium
support_strength20%Proximity to key support levels
safety_margin15%ATR-adjusted OTM buffer
trend_alignment15%Downtrend = 100, Uptrend = 30
probability_profit15%Black-Scholes prob of expiring OTM
liquidity10%Volume + OI + spread
time_decay5%20-45 DTE optimal
Sell Call Weights
FactorWeight
premium_yield20%
resistance_strength20%
trend_alignment15%
upside_buffer15%
liquidity10%
is_covered10%
time_decay5%
overvaluation5%
Buy Call Weights
FactorWeight
bullish_momentum25%
breakout_potential20%
value_efficiency20%
volatility_timing15%
liquidity10%
time_optimization10%
Buy Put Weights
FactorWeight
bearish_momentum25%
support_break20%
value_efficiency20%
volatility_expansion15%
liquidity10%
time_value10%

Score Scale

  • 80-100: Exceptional — top-tier opportunity
  • 60-79: Strong — good trade candidate
  • 40-59: Average — proceed with caution
  • 0-39: Poor — avoid unless hedging

Risk-Return Profiles

StyleTypical Win RateTypical Return
steady_income65-80%1-5%/month
balanced40-55%50-200%
high_risk_high_reward20-40%2-10x
hedge30-50%0-1x

API Endpoints

Canonical Options Score

Use this endpoint for the normal "score options" request. It selects an expiry when one is not supplied, applies the requested strategy, and returns ranked recommendations with the trend context and score breakdown.

POST /api/v1/options/score
Authorization: Bearer $ALPHAGBM_API_KEY
Content-Type: application/json

{"ticker": "AAPL", "strategy": "sell_put", "expiry_date": "2026-04-17", "top_n": 5}

strategy accepts sell_put, sell_call, buy_call, buy_put, or all. expiry_date and top_n are optional; top_n is capped at 10. A successful response contains ticker, strategy, current_price, expiry_date, trend, and either recommendations or a strategies object when strategy is all.

Get Option Expirations

GET /api/options/expirations/<SYMBOL>

Option Chain Analysis -- Synchronous

POST /api/options/chain-sync
Content-Type: application/json

{"symbol": "AAPL", "expiry_date": "2026-04-17"}

Add ?compact=true for condensed response.

Response includes for each of 4 strategies (Sell Put, Sell Call, Buy Call, Buy Put):

  • Top 10 recommendations sorted by score (0-100)
  • Score breakdown: premium_yield, support/resistance_strength, safety_margin, trend_alignment, probability_profit, liquidity, time_decay
  • ATR safety info (safety_ratio, atr_multiples, is_safe)
  • Risk-return profile: style, risk_level, win_probability
  • Trend analysis: direction, strength, alignment score

Option Chain Analysis -- Async

POST /api/options/chain-async
Content-Type: application/json

{"symbol": "TSLA", "expiry_date": "2026-04-17"}

Returns {"task_id": "uuid"}. Poll with: GET /api/tasks/<task_id>.

Enhanced Single-Option Analysis -- Sync

POST /api/options/enhanced-sync
Content-Type: application/json

{"symbol": "AAPL", "option_identifier": "AAPL260417C00190000"}

Enhanced Single-Option Analysis -- Async

POST /api/options/enhanced-async
Content-Type: application/json

{"symbol": "AAPL", "option_identifier": "AAPL260417C00190000"}

Reverse Score

Score a specific contract from known parameters:

POST /api/options/reverse-score
Content-Type: application/json

{"symbol": "AAPL", "option_type": "CALL", "strike": 190, "expiry_date": "2026-02-16", "option_price": 2.50, "implied_volatility": 28}

Batch Chain Analysis

POST /api/options/chain/batch
Content-Type: application/json

{"symbols": ["AAPL", "NVDA"], "expiries": ["2026-04-17", "2026-05-15"]}

Max 3 symbols x 2 expiries per request.

IV Snapshot (instant, no analysis-credit cost)

GET /api/options/snapshot/<SYMBOL>
Authorization: Bearer $ALPHAGBM_API_KEY

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

Daily Recommendations (no auth required)

GET /api/options/recommendations?count=5

Typical Workflow

  1. Score directly: POST /api/v1/options/score with ticker + strategy
  2. Quick IV check: GET /api/options/snapshot/AAPL (authenticated, no analysis-credit deduction)
  3. Inspect expirations: GET /api/options/expirations/AAPL when the user specifies a date
  4. Drill into a specific contract: POST /api/options/enhanced-sync with option_identifier
  5. Compare across tickers: POST /api/options/chain/batch for multi-symbol analysis

Use the lower-level chain endpoints only when the user asks for raw chain or enhanced analysis. Do not substitute them for the canonical score endpoint.

Quota

  • Free account: uses the current account-level daily free allowance; do not assume a per-Skill allowance
  • Plus: 1,000/month
  • Pro: 5,000/month
  • Snapshot does not consume analysis credits but still requires authentication. Recommendations are a public summary endpoint.

Output Formatting Tips

  • Scores are 0-100; present top picks in a table sorted by score descending.
  • Always show the score breakdown factors so users understand why a contract scored well.
  • Highlight ATR safety info (is_safe flag) prominently for sell strategies.
  • Include the risk-return style label (steady_income, balanced, etc.) for quick context.

Example Queries

User SaysWhat Happens
"Score AAPL options"Full chain with scores, top picks highlighted
"Best NVDA call to buy"Filtered to calls, sorted by score descending
"TSLA puts for next Friday"Filtered by expiry + type
"Which SPY option has the best risk/reward?"Sorted by risk_reward factor

Mock Data

Offline demo tickers are available without an API key: AAPL, NVDA, SPY, TSLA, META. They use bundled sample data from mock-data/; they are not live API access.

Related Skills

  • alphagbm-stock-analysis -- Analyze the underlying stock first
  • alphagbm-options-strategy -- Build multi-leg strategies with top-scored contracts
  • alphagbm-greeks -- Deep-dive into Greeks for a specific contract
  • alphagbm-vol-surface -- See if IV is cheap or expensive across strikes

Powered by AlphaGBM -- Real-data options & research intelligence. 10K+ users.

Frequently asked questions

What does the Alphagbm Options Score AI skill do?

Score and rank options contracts for any ticker using AlphaGBM's multi-factor scoring model (liquidity, IV attractiveness, Greeks balance, risk/reward). Returns scored option chains with the best contracts highlighted. Use when: evaluating which option to trade, finding the best strike/expiry, ranking options by quality. Triggers on: "score AAPL options", "best options for NVDA", "which TSLA call should I buy", "option chain for SPY", "rank META puts".

Why use Alphagbm Options Score on TypingMind?

Because you install it once and use it with any model. Alphagbm Options Score 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 Options Score in TypingMind?

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

Which AI models can use Alphagbm Options Score?

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 Options Score?

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

Is the Alphagbm Options Score 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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