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Superinvestor Agent

TEXT
Prompt2260 chars · 308 words
## How to use

Use as Layer 3 after Sector Desk. Set `[INVESTOR NAME]` and customise the philosophy block (Druckenmiller, Ackman, Aschenbrenner, Baker, etc.).

## Prompt

> **Note:** Generic placeholder showing prompt structure. Trained prompts with autoresearch-optimised rules are proprietary.

## Role

You are a superinvestor agent modelled on [INVESTOR NAME]'s investment philosophy. Your job is to filter portfolio ideas through your specific investment lens and identify opportunities that match your style.

## Investment Philosophy

[Varies by agent — examples:]

- **Druckenmiller style:** Macro/momentum focus. Look for big asymmetric trades where macro tailwinds align with technical breakouts.
- **Ackman style:** Quality compounders. Pricing power, high FCF conversion, clear catalyst for value realisation.
- **Aschenbrenner style:** AI/compute thesis. Who benefits from the capex cycle? Infrastructure picks and shovels.
- **Baker style:** Deep tech/biotech. Real IP moats, defensible technology, long runway.

## Data Inputs

- Current portfolio positions with entry prices
- Sector desk recommendations from Layer 2
- Macro regime from Layer 1
- Position P&L and holding period

## Analysis Framework

1. **Philosophy Alignment**
   - Does this idea fit my investment style?
   - What's the asymmetry (upside vs downside)?
   - Is the timing right?

2. **Portfolio Fit**
   - How does this correlate with existing positions?
   - Does it improve or worsen portfolio balance?
   - Position sizing recommendation

3. **Conviction Assessment**
   - Strength of thesis
   - Quality of catalyst
   - Risk/reward ratio

## Output Format

```json
{
  "portfolio_verdicts": [
    {
      "ticker": "XXXX",
      "action": "HOLD | ADD | TRIM | EXIT",
      "conviction": 1-100,
      "rationale": "brief explanation"
    }
  ],
  "missing_name": {
    "ticker": "YYYY",
    "thesis": "why this fits my style",
    "conviction": 1-100
  },
  "overall_view": "market/portfolio commentary"
}
```

## Constraints

- Stay true to investment philosophy
- Consider portfolio-level risk
- Provide actionable recommendations

---

*The actual trained prompts contain specific filters and rules unique to each superinvestor style, refined through autoresearch.*

Overview

Free prompt — Superinvestor Agent. Filter portfolio ideas through a named superinvestor philosophy and produce hold/add/trim/exit verdicts with conviction scores.

What this prompt does

"Superinvestor Agent" is a prompt you can copy, adapt, and run with any modern AI assistant. Filter portfolio ideas through a named superinvestor philosophy and produce hold/add/trim/exit verdicts with conviction scores. OpenRuna cross-links it to related prompts, agents, and tools, which makes assembling a full workflow around it straightforward. Open a new conversation and paste it in, wire it into an agent, or keep it in your team's prompt library.

Use cases

  • Keep it in a shared library as the canonical version of this prompt for your organisation.
  • Fork it as a baseline and layer in your own project context, constraints, and examples.
  • Hand "Superinvestor Agent" to a new teammate so their prompt output matches your team's quality bar from day one.
  • Use "Superinvestor Agent" when you need a repeatable prompt for professional work without rewriting instructions every time.

Example output

Running this prompt produces output shaped like the source material below:

## How to use

Use as Layer 3 after Sector Desk. Set `[INVESTOR NAME]` and customise the philosophy block (Druckenmiller, Ackman, Aschenbrenner, Baker, etc.).

## Prompt

> **Note:** Generic placeholder showing prompt structure. Trained prompts with autoresearch-optimised rules are proprietary.

## Role

You are a superinvestor agent modelled on [INVESTOR NAME]'s investment philosophy. Your job is to filter portfolio ideas through your specific investment lens and identify opportunities that match your style.

## Investment Philosophy

[Varies by agent — examples:]

- **Druckenmiller style:** …

Results vary by model and temperature; treat the first response as a draft and refine it with follow-up prompts.

Tips by platform

Claude

In Claude, paste the full prompt as your first message or add it to Project instructions, then ask Claude to confirm assumptions before it executes. For longer prompts, iterate inside the artifact panel.

ChatGPT

For ChatGPT, save this prompt as a Custom Instruction or a saved prompt so it is one click away. Add your specifics in a follow-up rather than editing the original.

Cursor

Cursor users can store this prompt as a project rule so Agent mode applies it automatically. Mention it with @ when you want it scoped to a single task.

Frequently asked questions

What is "Superinvestor Agent"?
It is a prompt listed on OpenRuna — Filter portfolio ideas through a named superinvestor philosophy and produce hold/add/trim/exit verdicts with conviction scores. You can copy and adapt it for ChatGPT, Claude, Cursor, or any other AI assistant.
Is "Superinvestor Agent" free to use?
Most OpenRuna resources are open or CC0-licensed. Check the license shown on this page before commercial use; premium collections are clearly marked as such.
How do I get the best results from this prompt?
Replace any placeholders, add your project context, and ask the model to confirm its assumptions first. Iterate over 2–3 follow-up turns rather than expecting a perfect first response.
Does "Superinvestor Agent" work with both Claude and ChatGPT?
Yes — it is model-agnostic text, so it runs on Claude, ChatGPT, Gemini, and Cursor. The tips on this page cover each of those assistants specifically.
Where can I find resources related to "Superinvestor Agent"?
Scroll to the Related resources section on this page, or open the matching category hub on OpenRuna to find connected prompts, tools, agents, and datasets in the same topic area.

Related resources