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-fear-scoregit 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-fear-score)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/alphagbm-fear-score"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-fear-score/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-fear-score"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-fear-score.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 | $0.00162 | $0.01319 |
| Opus 5 | $0.00081 | $0.00660 |
| Sonnet 5 | $0.00032 | $0.00264 |
| Haiku 4.5 | $0.00016 | $0.00132 |
Grade A, and why
alphagbm-fear-score 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-fear-score — 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaGBM FearScore
A weighted composite panic gauge, per ticker. Reconstructs the FearDesk framework in one API call: six orthogonal fear signals, each scored 0–100, then combined with fixed weights into a single number. Score ≥ 60 is the historical trigger for Bull Put Spread entries.
Scoring Weights
| Indicator | Weight | Source |
|---|---|---|
| VIX level | 20% | Global fear floor (market-wide) |
| IV Rank | 25% | Per-ticker option premium expensiveness |
| RSI-14 | 15% | Oversold intensity |
| Volume anomaly | 15% | Options or stock volume spike vs 5-day avg |
| Put/Call ratio | 15% | Bearish positioning skew |
| Consecutive down days | 10% | Selloff persistence |
Each indicator has its own 0–100 sub-score with thresholds tuned so extreme readings
contribute most. Missing inputs fall back to neutral values (and are flagged in
components.*.fallback), so the endpoint never 500s on partial data.
Why It Exists
Most fear gauges are either VIX-only (miss per-ticker divergence) or opaque ("sentiment index: 72"). This breaks down exactly what drove the score so you can decide whether to trust it.
Backtest evidence: Across 146 live Bull Put Spread trades, entries at FearScore ≥ 60 delivered ~10.8% annualized ROC vs ~3.5% for unconditional entries — roughly 3× the alpha from a single filter. Use this as the market-timing layer on any premium-selling strategy.
How to Use
Input: A ticker symbol.
Output:
fear_score— weighted total 0-100signal— boolean, true whenfear_score ≥ threshold(default 60)threshold— current trigger valueconfidence— 0-1, fraction of the 6 indicators that used real (non-fallback) datacomponents.{vix,iv_rank,rsi,volume_anomaly,pc_ratio,consecutive_down}:value— raw inputscore— 0-100 per-indicator scoreweight— contribution weightfallback— true if neutral default was used
Example Queries:
fear score QQQ— Full breakdown of the 6 indicators for QQQis NVDA oversold right now— RSI + FearScore compositeBPS signal SPY— Check if entry threshold is hitwhen should I sell put AAPL— Timing via FearScore ≥ 60 rulehow panicked is TSLA today— Per-ticker panic index with component breakdownwhy is QQQ fear score low— Component-by-component explanation
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 449 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 · 120 lines · 162 tokens per session scan A 2fc07439ff1e
alphagbm-fear-score is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed today), licensed MIT. It adds 162 tokens to every session and 1,319 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to alphagbm-fear-score, differing in 0 lines, and is treated as a copy.
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