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-iv-rankgit 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-iv-rank)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/alphagbm-iv-rank"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-iv-rank/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-iv-rank"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-iv-rank.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 | $0.00132 | $0.01528 |
| Opus 5 | $0.00066 | $0.00764 |
| Sonnet 5 | $0.00026 | $0.00306 |
| Haiku 4.5 | $0.00013 | $0.00153 |
Grade A, and why
alphagbm-iv-rank 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-iv-rank — 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaGBM IV Rank
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
Calculates IV Rank and IV Percentile for any ticker, placing current implied volatility in historical context. Answers the key question: "Is IV high or low right now, and what should I do about it?"
Key Metrics
| Metric | Formula | What It Means |
|---|---|---|
| IV Rank | (Current IV - 52w Low) / (52w High - 52w Low) x 100 | Where IV sits in its annual range. 0 = at the low, 100 = at the high |
| IV Percentile | % of days in past year where IV was lower than today | What % of the time IV was cheaper than now. 80 = IV was lower 80% of the time |
| Current IV | 30-day ATM implied volatility | The market's current expectation of annualized movement |
| IV 52w High | Highest 30-day IV in past 252 trading days | Peak IV -- usually during selloffs or events |
| IV 52w Low | Lowest 30-day IV in past 252 trading days | Trough IV -- usually during calm, grinding markets |
| HV/IV Ratio | Historical Volatility / Implied Volatility | >1 means realized vol exceeds implied (IV may be cheap) |
IV Zones and Trading Signals
| IV Rank | Zone | What It Means | Suggested Action |
|---|---|---|---|
| 80-100 | Very High | IV is near its annual peak -- options are expensive | Sell premium: short strangles, iron condors, credit spreads |
| 60-80 | High | IV is elevated -- above-average option prices | Lean toward selling, but selective; good for covered calls |
| 40-60 | Moderate | IV is in the middle -- neither cheap nor expensive | Strategy-neutral; use directional view to decide |
| 20-40 | Low | IV is depressed -- options are cheap | Lean toward buying; good for debit spreads, long straddles |
| 0-20 | Very Low | IV is near its annual trough -- options are very cheap | Buy premium: long straddles, debit spreads, calendars (sell back month) |
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 446 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 · 131 lines · 132 tokens per session scan A 8ae181659b34
alphagbm-iv-rank is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed today), licensed MIT. It adds 132 tokens to every session and 1,528 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-iv-rank, differing in 0 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 using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
furusato
A Japanese hometown-tax donation manager for furusato nozei, a system where donations to municipalities can qualify for an income-tax or local-tax deduction. It reads donation receipts, stores donation records, and calculates deduction limits.
reading-receipt
An image-reading workflow for extracting structured information from receipts, invoices, and hometown-tax donation certificates. It can first extract text from PDFs and otherwise read their images.