alphagbm-chokepoint

alphagbm-chokepoint is an Investment analysis & markets skill for AI agents, published by AlphaGBM in the skills repository on GitHub. Skillspace rates alphagbm-chokepoint D (56/100) and it passed the safety scan; the rating is based on static analysis only.

Serenity-style "Chokepoint Theory" applied to AI supply chains. Identifies physically irreplaceable bottleneck suppliers — small-cap near-monopolies buried 4–7 layers deep — whose capacity constraints force violent repricing when demand outgrows supply. Uses a 5-factor scoring model (Concentration, Irreplaceability, Qualification Gate, Discovery Gap, Demand Tension) to screen and rank candidates. This is AlphaGBM's independent reading of Serenity (@aleabitoreddit)'s publicly shared methodology — NOT affiliated with or endorsed by Serenity. Triggers: "chokepoint analysis", "AI supply chain bottleneck", "find the shiso leaf", "Serenity-style screen", "which small-caps own the bottleneck", "InP substrate play", "co-packaged optics chokepoint", "irreplaceable supplier in AI buildout", "supply chain concentration risk"

Grade

D

Quality

56 / 100

Safety

Passed

GitHub stars

1,381

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