Individual skill
bottleneck-hunter
Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar listed companies sitting on each bottleneck. Scores each link on 6 scarcity criteria, applies mandatory valuation gates (PS/PE/safety-margin) via the financial_rigor tool, and Munger-style reverse-validates. Outputs a ranked bottleneck opportunity board. Use when the user wants hidden beneficiaries of a structural trend rather than already-priced leaders.
agent/src/skills/bottleneck-hunter/SKILL.md
Source commit a79afbdb048f873433fdb651a667e43dd0bb735d
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- backtesting Repository topic
- multi-agent Repository topic
- quantitative-finance Repository topic
- trading Repository topic
- ai-agent Repository topic
- algorithmic-trading Repository topic
- fintech Repository topic
- llm Repository topic
- mcp Repository topic
- python Repository topic
- finance Derived category
- operations Derived category
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# Supply-Chain Bottleneck Hunter
Decompose a super-trend (user-specified, e.g. "AI infrastructure", "energy transition") into its physical supply chain and hunt for bottleneck-arbitrage opportunities.
## Core Idea
Don't ask "which AI stock to buy" — ask "if this trend keeps expanding, which link runs out first?"
Traditional research chases leaders and known tracks. This skill inverts: **start from the choke points of the physical supply chain and find companies nobody notices but that the whole industry must wait on when they run short.**
The edge: Layer-1 bottlenecks (GPU, HBM, power) are already priced in. The real alpha is in **Layer 2 and Layer 3** — optical modules, lasers, InP substrates, SOI wafers, epitaxy equipment, wafer-level test, IC substrates, specialty fiberglass.
## Step 1: Super-Trend Confirmation
