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 leecyno1/boutique-skills --skill alphagbm-options-strategygit clone --depth 1 https://github.com/leecyno1/boutique-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/leecyno1/boutique-skills/alphagbm-options-strategy)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/alphagbm-options-strategy"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-options-strategy/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/leecyno1/boutique-skills/alphagbm-options-strategy"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-options-strategy.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 | $0.00147 | $0.01584 |
| Opus 5 | $0.00073 | $0.00792 |
| Sonnet 5 | $0.00029 | $0.00317 |
| Haiku 4.5 | $0.00015 | $0.00158 |
Grade A, and why
alphagbm-options-strategy 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 12d 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
100% identical to alphagbm-options-strategy — 0 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaGBM Options Strategy
Prerequisites
- API Key: Set env
ALPHAGBM_API_KEY(formatagbm_xxxx...). - Base URL: Default
https://alphagbm.zeabur.app. Override with envALPHAGBM_BASE_URL.
What This Skill Does
Given a market view and a ticker, recommends the best multi-leg option strategies ranked by risk/reward profile. Selects optimal strikes and expirations automatically using AlphaGBM's scoring engine.
Four Core Strategies and Trend Alignment
| Strategy | Ideal Trend | Max Profit | Max Loss |
|---|---|---|---|
| Sell Put | Neutral / Bullish | Premium received | Strike - Premium (assignment risk) |
| Sell Call | Neutral / Bearish | Premium received | Unlimited (uncovered) |
| Buy Call | Bullish | Unlimited | Premium paid |
| Buy Put | Bearish | Strike - Premium | Premium paid |
Trend alignment scoring: The scoring model rewards contracts that match the prevailing trend. For Sell Put, a downtrend scores 100 (counter-intuitive: you want to sell puts into weakness for higher premium), while an uptrend scores 30. For Buy Call, bullish momentum is weighted at 25%.
Supported Strategy Templates (15+)
| Category | Strategies |
|---|---|
| Bullish | Bull Call Spread, Bull Put Spread, Long Call, Covered Call, Synthetic Long |
| Bearish | Bear Put Spread, Bear Call Spread, Long Put, Synthetic Short |
| Neutral | Iron Condor, Iron Butterfly, Short Straddle, Short Strangle, Calendar Spread |
| Volatile | Long Straddle, Long Strangle, Butterfly Spread, Reverse Iron Condor |
| Income | Covered Call, Cash-Secured Put, Collar, Jade Lizard |
Risk-Return Profiles
| Style | Typical Win Rate | Typical Return |
|---|---|---|
| steady_income | 65-80% | 1-5%/month |
| balanced | 40-55% | 50-200% |
| high_risk_high_reward | 20-40% | 2-10x |
| hedge | 30-50% | 0-1x |
Strategy Selection Logic
- Match user's market view to candidate strategies
- Filter by IV environment (high IV favors selling premium; low IV favors buying)
- Score each candidate using risk/reward, probability of profit, and capital efficiency
- Rank and return the top 3 recommendations with full details
What ships with it
17 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.
- LICENSE 1.0 KB
- mock-data/AAPL.json 6.7 KB
- mock-data/buffett-analysis/example-ko.json 1.9 KB
- mock-data/fear-score/example-calm.json 661 B
- mock-data/fear-score/example-signal-triggered.json 663 B
- mock-data/hedge-advisor/example-gain-protection.json 1.5 KB
- mock-data/marks-cycle/example-neutral.json 366 B
- mock-data/META.json 8.8 KB
- mock-data/NVDA.json 8.4 KB
- mock-data/SPY.json 7.5 KB
- mock-data/take-profit/example-leveraged-etf.json 1.1 KB
- mock-data/tepper-signal/example-armed.json 589 B
- mock-data/tepper-signal/example-cold.json 488 B
- mock-data/TSLA.json 9.4 KB
- mock-data/vix-status/example-extreme-fear.json 528 B
- mock-data/vix-status/example-sweet-spot.json 506 B
- SOURCE.txt 455 B
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.
- 12d ago First seen · 171 lines · 147 tokens per session scan A f69f3da48a02
alphagbm-options-strategy is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed today), licensed MIT. It adds 147 tokens to every session and 1,584 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to alphagbm-options-strategy, differing in 0 lines, and is treated as a copy.
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