How to score any stock with Claude in 60 seconds

AI isn’t just for writing code or blog posts anymore. With the right prompt, you can turn Claude into a mini equity research engine that pulls real financials, scores a stock across key risk dimensions, and gives you a single, easy-to-read risk score out of 100 in under a minute.

In this guide, you’ll see how that framework works and what it reveals about one of the most talked‑about stocks in the world: Tesla, which comes out with a surprisingly low 27/100 score.

Why use Claude to score stocks?

Most investors either drown in data or rely on vibes. AI gives you a third option: a structured, repeatable way to turn raw financials into a clear risk score.

Instead of manually digging through filings or paying for expensive terminals, you can use Claude to:

• Pull up-to-date financial data for a company
• Evaluate it across a few well-defined categories
• Combine everything into a single risk score you can compare across stocks

The key is not to ask vague questions like “Is Tesla a good stock?” but to design a precise prompt that tells Claude exactly what to fetch, how to measure it, and how to present the results.

If you like building practical AI workflows like this, you may also enjoy learning how to build a self‑improving AI second brain in Claude.

The 3-part stock risk framework

The scoring system is built around three categories that cover the main ways a stock can be risky: valuation, financial health, and growth. Each category is scored out of 100, then combined into a single overall risk score.

1. Valuation (35% weight)

Question: Is the stock cheap or expensive relative to what the business is currently earning and how fast it’s growing?

Claude looks at metrics like:

• Price-to-earnings (P/E) ratio
• Enterprise value to EBITDA (EV/EBITDA)
• How those compare to market and industry averages

A low score here means the stock looks very expensive relative to its fundamentals and peers, which increases risk if expectations don’t get met.

2. Financial health (35% weight)

Question: Can this company survive a downturn without blowing up its balance sheet?

Claude evaluates:

• Cash and short-term investments
• Total debt levels
• Liquidity ratios like the current ratio (ability to pay short-term bills)

A strong balance sheet can offset other risks. Even if valuation is stretched, a cash-rich, low-debt company has more room to navigate tough conditions.

3. Growth and earnings quality (30% weight)

Question: Is the business actually growing in a healthy, sustainable way?

Here, Claude checks:

• Whether the company is beating or missing estimates (e.g., deliveries, revenue, segment performance)
• Revenue growth trends – accelerating or slowing?
• Margins – holding up or being squeezed?

This is where hype meets reality. If a stock is priced for perfection but growth is slowing or margins are shrinking, risk shoots up.

Designing the Claude prompt

The magic isn’t that Claude “knows” every stock by default. The magic is in the prompt you give it.

A good prompt for this kind of analysis will:

• Specify the company and ticker (e.g., “Tesla, TSLA”)
• Define each risk category and its weight
• Tell Claude exactly which metrics to pull (P/E, EV/EBITDA, cash, debt, margins, growth, etc.)
• Explain how to score each metric and how to combine them into a 0–100 score
• Ask for a clear, structured report with sections and a final verdict

In other words, you’re not just asking Claude for an opinion. You’re giving it a framework and asking it to run the numbers through that framework.

For more inspiration on designing powerful prompts and workflows, you can also check out this deep dive on Claude vs Google Antigravity for coding and analysis.

Case study: Tesla’s 27/100 risk score

When you run Tesla (TSLA) through this framework, the overall score comes out at 27/100 – firmly in the high‑risk zone.

That doesn’t mean Tesla is a bad company. It means that, at current prices, the numbers suggest you’re taking on a lot of risk relative to what the financials currently justify.

How Tesla scores on valuation

This is where Tesla gets hit hardest.

• The trailing price-to-earnings ratio is around 353x earnings. For every $1 of profit Tesla makes, you’re paying about $353 to own it. The S&P 500 average is closer to 20–25x.
• EV/EBITDA is around 119x, versus an auto industry average closer to 8–12x.

On traditional valuation metrics, Tesla looks extraordinarily expensive. For the scorecard, that translates into a very low valuation score, which drags down the overall result.

How Tesla scores on financial health

This is Tesla’s strongest area and a big reason the total score isn’t even lower.

• Tesla holds roughly $44 billion in cash and short-term investments.
• Total debt is relatively low.
• The current ratio (a measure of whether short-term obligations can be paid) is very healthy.

From a balance sheet perspective, Tesla looks solid. The company has plenty of liquidity and isn’t weighed down by dangerous levels of debt. In the framework, this translates into a strong financial health score that offsets some of the valuation risk.

How Tesla scores on growth and earnings quality

This is where the picture becomes more mixed – and uncomfortable for some shareholders.

On the negative side:

• Tesla missed Q1 car delivery estimates.
• It also missed energy storage estimates.
• Revenue growth has slowed.
• Margins have compressed due to price cuts aimed at maintaining volume.

At the same time, there are bright spots:

• The energy storage business is scaling quickly and looks promising.
• However, the core car business still makes up the bulk of revenue, and that segment is under pressure from Chinese EV competitors and a broader slowdown in EV demand.

When a stock is priced for perfection but the underlying metrics show slowing growth and shrinking margins, the risk score for this category rises sharply.

What a 27/100 score really means

A low score doesn’t say “this company will fail.” It says “you’re paying a high price for a lot of future promises that aren’t fully reflected in the current numbers.”

For Tesla, the market is effectively pricing in:

• Successful full self-driving at scale
• Robotaxis and autonomous fleets
• Dominance in energy and storage

Those may or may not happen, but they are not yet clearly visible in the financials. That gap between today’s numbers and tomorrow’s hopes is where risk lives – and what the 27/100 score is flagging.

If you’re thinking about buying a stock with a similar profile, this kind of report helps you go in with your eyes open. You can still decide to take the bet, but you’re doing it knowing exactly which parts of the story are backed by data and which parts are pure expectation.

How to build your own stock risk report with Claude

You can recreate this system for any stock you’re interested in. The process looks like this:

1. Define your categories and weights – for example, 35% valuation, 35% financial health, 30% growth.
2. List the exact metrics you want Claude to pull for each category (P/E, EV/EBITDA, cash, debt, current ratio, revenue growth, margins, estimate beats/misses, etc.).
3. Tell Claude how to score each metric on a 0–100 scale and how to combine them into one overall score.
4. Ask for a structured output: category breakdowns, commentary, and a final verdict.

Once you’ve written this prompt, you can reuse it for any ticker. Paste in the company name and symbol, run it in your browser, and you’ll get a full risk report in about a minute – something that used to require expensive tools or subscriptions.

AI as an investing copilot, not a replacement

The real power of this approach isn’t that AI replaces your judgment. It’s that it accelerates the boring, data-heavy parts of analysis so you can focus on decisions.

Claude can:

• Pull and organize the numbers
• Apply a consistent scoring framework
• Surface where the risk actually is

You still decide what level of risk you’re comfortable with, how much you trust the future story, and whether the potential upside is worth it. AI just makes it much faster and easier to get to that point.

From here, try running this framework on stocks you already own. See how they score, where their weaknesses are, and whether the risk you’re taking matches your conviction. That’s where AI stops being a toy and starts becoming a real investing tool.