Agent SkillsAlphaGBM/skills › alphagbm-chokepoint

alphagbm-chokepoint

GitHub

应用瓶颈理论分析AI供应链,通过五因子模型筛选物理上不可替代的小盘垄断供应商,识别需求超出供给时的重定价机会。

skills/alphagbm-chokepoint/SKILL.md AlphaGBM/skills

Trigger Scenarios

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

Install

npx skills add AlphaGBM/skills --skill alphagbm-chokepoint -g -y
More Options

Use without installing

npx skills use AlphaGBM/skills@alphagbm-chokepoint

指定 Agent (Claude Code)

npx skills add AlphaGBM/skills --skill alphagbm-chokepoint -a claude-code -g -y

安装 repo 全部 skill

npx skills add AlphaGBM/skills --all -g -y

预览 repo 内 skill

npx skills add AlphaGBM/skills --list

SKILL.md

Frontmatter
{
    "name": "alphagbm-chokepoint",
    "description": "Serenity-style \"Chokepoint Theory\" applied to AI supply chains. Identifies\nphysically irreplaceable bottleneck suppliers — small-cap near-monopolies\nburied 4–7 layers deep — whose capacity constraints force violent repricing\nwhen demand outgrows supply. Uses a 5-factor scoring model (Concentration,\nIrreplaceability, Qualification Gate, Discovery Gap, Demand Tension) to\nscreen and rank candidates. This is AlphaGBM's independent reading of\nSerenity (@aleabitoreddit)'s publicly shared methodology — NOT affiliated\nwith or endorsed by Serenity.\nTriggers: \"chokepoint analysis\", \"AI supply chain bottleneck\", \"find the\nshiso leaf\", \"Serenity-style screen\", \"which small-caps own the bottleneck\",\n\"InP substrate play\", \"co-packaged optics chokepoint\", \"irreplaceable\nsupplier in AI buildout\", \"supply chain concentration risk\""
}

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

  • ≥ 80CORE — highest-conviction chokepoint, full position
  • 60–79BUILD — strong candidate, scale in on confirmation
  • 40–59STARTER — early signal, small position, monitor closely
  • < 40PASS — does not meet chokepoint criteria

The Logic: Why Chokepoints Reprice

When demand grows at 50–100% CAGR but the chokepoint physically cannot expand capacity at the same rate (constrained by physics, materials, clean-room build time, or qualification cycles), the screw gets repriced violently upward.

The framework is not about:

  • Betting on earnings beats
  • Momentum / technical analysis
  • Macro timing

It is about:

  • Mapping the physical supply chain end-to-end
  • Finding the narrowest point where supply is inelastic
  • Entering before the market prices in the constraint

Canonical Example: AXTI (AXT Inc.)

The AXTI thesis illustrates the framework in action:

  • What they make: Indium Phosphide (InP) substrates — the base wafer for photonic integrated circuits (PICs) used in co-packaged optics
  • Concentration: AXTI + 2 others control ~85% of global InP substrate supply
  • Irreplaceability: InP is the only material that works for 800G+ optical transceivers; GaAs and Si cannot substitute at these wavelengths
  • Qualification Gate: 18-month qualification cycle with each foundry customer
  • Discovery Gap: Was a $200M market cap, <5 analyst coverage when the thesis was formed
  • Demand Tension: Co-packaged optics demand growing at ~80% CAGR; substrate capacity expansion takes 2+ years

Result: the stock repriced ~30x as the market recognized the bottleneck.

How to Use This Skill

This is a methodology skill — it provides the analytical framework for an AI agent to evaluate whether a given company or supply-chain node qualifies as a chokepoint.

Input

Provide one of:

  • A ticker to evaluate against the 5-factor test
  • A supply-chain segment (e.g., "InP substrates", "HBM packaging", "advanced substrates for AI servers") to map and identify chokepoint candidates
  • A thesis to stress-test (e.g., "AXTI is a chokepoint in co-packaged optics")

Output

The agent should return:

  1. Supply-chain map — where the company sits in the value chain
  2. 5-factor scorecard — each factor scored 0–100 with evidence
  3. Overall Chokepoint Score — weighted composite + tier (CORE/BUILD/STARTER/PASS)
  4. Key risks — what could break the thesis (second source emerging, demand destruction, technology shift)
  5. Comparable chokepoints — other names in the same supply chain that may also qualify

Example Queries

  • Is AXTI a chokepoint in co-packaged optics?
  • Map the HBM supply chain and find the bottleneck
  • Which InP substrate makers qualify as chokepoints?
  • Evaluate CEVA as a chokepoint in sensor fusion IP
  • Find the shiso leaf in the AI server power delivery chain

Key Supply-Chain Domains to Watch

Domain Why It Matters Example Chokepoints
Co-packaged Optics 800G→1.6T transceiver migration InP substrates, EEL lasers
Advanced Packaging HBM + chiplet integration CoWoS capacity, bonding equipment
AI Power Delivery 1MW+ per rack power density GaN/SiC power semis, busbar/PDU
Specialty Materials Enabling substrates & gases InP wafers, ultra-high-purity gases
Cooling Liquid cooling for AI clusters CDU units, cold plate connectors

Risk Factors

Every chokepoint thesis has kill conditions. The agent must surface these:

  1. Second source qualification — a new supplier completing qual breaks the monopoly
  2. Technology substitution — a different material or architecture bypasses the bottleneck
  3. Demand destruction — AI capex slowdown reduces urgency
  4. Customer vertical integration — hyperscaler builds in-house
  5. Geopolitical risk — export controls or sanctions disrupt supply chain

Related Skills

Skill Relevance
alphagbm-stock-analysis Complement with G=B+M scoring for overall stock quality
alphagbm-company-profile Deep fundamental profile for chokepoint candidates
alphagbm-theme-research Map broader AI themes before drilling into chokepoints
alphagbm-investment-thesis Convert chokepoint finding into a trackable thesis
alphagbm-unusual-activity Detect institutional accumulation in chokepoint names

Powered by AlphaGBM — Real-data options & research intelligence.

Version History

  • 6ecea74 Current 2026-07-23 06:21

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