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-chokepointgit 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-chokepoint)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/alphagbm-chokepoint"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-chokepoint/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-chokepoint"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/alphagbm-chokepoint.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 | $0.00203 | $0.01865 |
| Opus 5 | $0.00102 | $0.00932 |
| Sonnet 5 | $0.00041 | $0.00373 |
| Haiku 4.5 | $0.00020 | $0.00186 |
Grade A, and why
alphagbm-chokepoint 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 11d 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-chokepoint — 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaGBM Chokepoint Analysis (Serenity-style)
In a piece of sushi, the tuna belly is the expensive part — but the shiso leaf is the one thing you cannot skip.
Everyone owns the "tuna": NVIDIA, TSMC, the hyperscalers. The alpha hides in the "shiso leaf" — the tiny, overlooked, near-monopoly suppliers buried 4–7 layers deep in the AI supply chain, whose failure would halt the entire buildout.
This skill codifies the Chokepoint Theory as publicly described by Serenity (@aleabitoreddit), one of the most discussed retail AI-supply-chain analysts.
⚠️ Disclaimer: This is AlphaGBM's independent interpretation of publicly available ideas. Not affiliated with, endorsed by, or connected to Serenity. Nothing here is financial advice. These are typically small-cap, illiquid, highly volatile names — you can lose everything.
The 5-Factor Chokepoint Test
A true chokepoint is a supply-chain node that satisfies all five criteria simultaneously. Each factor is scored 0–100; the overall Chokepoint Score is the weighted composite.
| # | Factor | Weight | What It Measures | Strong Signal |
|---|---|---|---|---|
| 1 | Concentration | 25% | Top 1–3 suppliers hold ≥ 70% market share | HHI > 2500, CR3 ≥ 70% |
| 2 | Irreplaceability | 25% | Material-science or physics moat; no viable second source | No drop-in substitute exists |
| 3 | Qualification Gate | 20% | Design-in / qualification cycle ≥ 12 months | 12–24 month cycle, customer switching cost |
| 4 | Discovery Gap | 15% | Under-owned, under-covered by institutions | Institutional ownership < 40%, analyst coverage ≤ 3 |
| 5 | Demand Tension | 15% | Downstream demand growing ≥ 50% CAGR vs flat/constrained supply | Demand CAGR ≥ 50%, capacity utilization > 85% |
Scoring Thresholds
- ≥ 80 → CORE — highest-conviction chokepoint, full position
- 60–79 → BUILD — strong candidate, scale in on confirmation
- 40–59 → STARTER — early signal, small position, monitor closely
- < 40 → PASS — does not meet chokepoint criteria
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.
- 11d ago First seen · 155 lines · 203 tokens per session scan A bc03834dd782
alphagbm-chokepoint is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed 20d ago), licensed MIT. It adds 203 tokens to every session and 1,865 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to alphagbm-chokepoint, differing in 0 lines, and is treated as a copy.
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