Example of storm-research skill (applied to investment research)

I tested Nate's storm-research skill (he shared earlier today here https://www.skool.com/ai-automation-society/new-video-stanfords-method-turns-claude-into-a-phd-level-research-team?p=7000fecf) with an genuine use-case: I am trying to decide whether/how to add drone exposure to my portfolio where my leading candidates are two public companies (RCAT, ONDS).

I suppose the general prompt could have been something like "what will be the size of the drone market in 5 years and which of the current players are likely to be leaders?" but I gave it a more detailed, specific prompt asking to feature the two candidate companies.

Here's the output (you'll see in the video that Nate provided also the report template, which renders the neat green/red supported/challenged tags)

https://bionicturtle.github.io/storm-research-reports/storm-reports/drone-industry-rcat-onds-briefing.html

My initial, quick impressions:

Excellent product: I am so delighted by this council-of-experts approach, especially for purposes of investment decision-making; e.g., I'm current on all of SeekingAlpha's latest articles on RCAT, but this document is next-level skepticism
The built-in STORM expert panel is Practitioner, Academic, Skeptic, Economist, Historian. For my investment decision-making purpose, next I would REPLACE the Academic with CFA/Forensic Accountant/etc. But in the GENERAL, I think you'd consider swapping an expert(s) depending on the purpose (!).
Maybe because my prompt was detailed (not sure?), I used 75% of my session tokens. I had to interrupt and ask Claude to hurry up and finish. Claude is a revelation every day but now I'm becoming very aware of token usage ;)

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Example of storm-research skill (applied to investment research)