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-market-sentimentgit 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-market-sentiment)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/alphagbm-market-sentiment"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-market-sentiment/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-market-sentiment"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-market-sentiment.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 | $0.00105 | $0.00810 |
| Opus 5 | $0.00053 | $0.00405 |
| Sonnet 5 | $0.00021 | $0.00162 |
| Haiku 4.5 | $0.00011 | $0.00081 |
Grade A, and why
alphagbm-market-sentiment 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-market-sentiment — 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaGBM Market Sentiment Dashboard
Aggregates market-wide sentiment indicators into a single dashboard, classifying the current regime to guide your trading stance.
What This Skill Does
| Indicator | Description |
|---|---|
| VIX Level + Percentile | Current VIX value and its rank over the past year (e.g., 85th percentile = elevated fear) |
| Put/Call Ratio | Equity and index P/C ratios — high values signal fear, low values signal complacency |
| Fear & Greed Index | Composite score (0-100) combining multiple sentiment inputs |
| Market Breadth | Advance/decline ratio and new highs vs new lows — measures participation |
| Sector Rotation Stage | Which sectors are leading/lagging, mapped to the economic cycle |
| Regime Classification | Overall assessment: risk-on, risk-off, or neutral with confidence level |
How to Use
Input: A market sentiment query (no ticker required, or specify VIX/SPX for focused analysis).
Output:
- Sentiment dashboard with all indicators and their current readings
- Historical context: where each indicator sits relative to the past 1 year
- Current regime classification (risk-on / risk-off / neutral) with confidence %
- Sector rotation map: early cycle, mid cycle, late cycle, or recession positioning
- Actionable interpretation: what the current sentiment means for options trading
Example Queries:
market sentiment— Full dashboard with all indicatorsis the market fearful— Quick fear/greed assessmentVIX analysis— Deep dive on VIX level, term structure, and percentileput call ratio— Equity and index put/call with historical contextmarket breadth— Advance/decline, new highs/lows, participation analysissector rotation— Which sectors are leading and what cycle stage we are in
Mock Data
Mock data files are located in mock-data/market-sentiment/ and include:
sentiment-dashboard.json— Full dashboard snapshot with all indicatorsvix-history.json— VIX time series with percentile rankssector-rotation.json— Sector performance and cycle classification
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 · 76 lines · 105 tokens per session scan A f5e1c3dfb457
alphagbm-market-sentiment is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed today), licensed MIT. It adds 105 tokens to every session and 810 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to alphagbm-market-sentiment, differing in 0 lines, and is treated as a copy.
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