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    Serenity-Style Bottleneck Alpha Research Pro

    1

    Map supply chains, identify overlooked upstream chokepoints, score candidate quality, audit evidence, and generate research reports.

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    See it in action

    You say

    Analyze the bottleneck for AI data center cooling. Focus on liquid cooling components and identify public candidates with the strongest evidence of customer design-in.

    Your agent does

    ## Candidate Leaderboard | Rank | Candidate | Layer | Bottleneck Score | Research Priority | Label | Key Trigger | |---:|---|---|---:|---:|---|---| | 1 | Vertiv (VRT) | L2 Subsystem | 88 | 92 | High Priority | Power/thermal backlog growth | | 2 | Boyd Corp | L2 Subsystem | 82 | Watchlist | Private | Potential IPO/Acquisition target | | 3 | nVent (NVT) | L3 Component | 74 | 78 | Watchlist | Q3 margin expansion on CDA units | **Conclusion:** VRT remains the cleanest public pure-play for thermal management subsystems due to L2 deployment lock. Evidence quality is high (Verified via 10-K). Risk: Valuation at 45x forward P/E.

    What you get

    Map the value chain of a capex-driven theme to find structural chokepoints.Rank public candidates based on bottleneck strength and research priority.Build an evidence ledger to separate management claims from verified filings.Generate professional-grade HTML research dashboards for investment committees.

    About this skill

    Serenity-Style Bottleneck Alpha Research

    Serenity-Style Bottleneck Alpha Research Pro is a structured skill for turning a public-market theme into a supply-chain map, upstream chokepoint inventory, candidate watchlist, evidence ledger, and research memo. It is built for thematic equity research, market mapping, competitor diligence, account teardown, and idea generation.

    What it does

    • Maps demand chains, capex chains, component chains, service chains, and substitution paths.
    • Identifies bottlenecks caused by capacity limits, qualification cycles, regulation, lead time, IP, tooling, or supplier concentration.
    • Separates public companies, private players, acquired assets, and non-investable nodes.
    • Scores candidates with Bottleneck Strength and Research Priority so users can compare ideas instead of chasing narratives.
    • Labels evidence as verified, management claim, inferred, or unverified, then writes the bear case before the bullish case.
    • Outputs Markdown research reports by default, with an optional local HTML dashboard only when requested.

    Best for

    Use it for AI infrastructure, data centers, robotics, EVs, GLP-1, defense, nuclear, packaging, semiconductors, industrial automation, or any theme where the obvious winner may not capture the most value.

    Important

    This is an educational research workflow. It does not provide personalized financial advice, buy or sell instructions, guaranteed returns, or official recommendations. Users should verify data with primary sources before making decisions.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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