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-take-profitgit 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-take-profit)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/alphagbm-take-profit"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-take-profit/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-take-profit"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-take-profit.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 | $0.00175 | $0.01730 |
| Opus 5 | $0.00088 | $0.00865 |
| Sonnet 5 | $0.00035 | $0.00346 |
| Haiku 4.5 | $0.00017 | $0.00173 |
Grade A, and why
alphagbm-take-profit 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-take-profit — 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaGBM Take-Profit Strategy Lab
Answer one question mechanically for any ticker: Can you just hold it, or do you need to actively take profits? Most retail losses come from poor exits, not poor entries. This skill quantifies the exit decision with 10 years of daily data.
The Core Metric: Rollercoaster Rate
A "rollercoaster event" happens when an entry's paper profit exceeds +50% and then falls more than 50% from that peak before exit. Example: enter at 100, peak at 190, fall back to 90 — you didn't lose money, but the 90 of peak profit you "touched" evaporated, and the journey was brutal.
Rollercoaster rate varies up to 97 percentage points across instruments:
- Broad-index ETFs (SPY, VTI): 0% — hold forever
- Blue chips (AAPL, MSFT): 0% — hold forever
- Sector ETFs (SOXX, XLK): 0% — hold forever
- Large-cap mega-caps (META, AMZN): ~47% — tiered exit preferred
- HK tech (腾讯, 阿里): ~49% — tiered exit preferred
- High growth (NVDA, TSLA, AMD): ~85% — tiered exit mandatory
- Crypto-related (COIN, MSTR): ~90% — tiered exit mandatory
- Leveraged ETFs (TQQQ, SOXL): ~97% — structurally un-holdable
Whether you can hold is an instrument property, not an attitude problem.
Strategy Universe (15 total)
- A family (sell all at trigger): A_+50%, A_+100%, A_+200%
- B family (tiered): B_50/100/200 (default), B_30/60/100, B2_20/40/80, B3_40/80/150, B5 back-weighted, B6 front-weighted
- C_10x (conviction hold)
- D (-20% / -30% trailing stop) — loses to hold on every tested ticker
- E (never sell / long-hold)
- F (peak-pullback after +50% activation)
- G (HV-aware: picks A_+100% or A_+200% based on entry-day vol)
How to Use
Input:
ticker(required) — any US / HK / CN stock, ETF, or leveraged ETF
Output:
- Profile:
color(green/amber/red) +special_flag(no_holdfor leveraged ETFs,reverse_alphafor declining stocks where active selling beats hold) - Headline numbers:
rollercoaster_rate,max_drawdown,hold_cagr strategy_results: 15 strategies, each with{cagr, rc, mdd}medians- Provenance:
sample_size(typically ~120 entry points),period,computed_at
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 450 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 · 151 lines · 175 tokens per session scan A fbd57acffe29
alphagbm-take-profit is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed today), licensed MIT. It adds 175 tokens to every session and 1,730 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to alphagbm-take-profit, differing in 0 lines, and is treated as a copy.
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