Investor Skills is an open-source library that organizes investing judgment—such as evaluating opportunities, managing risk, and acting under uncertainty—into structured, reusable instructions for people and AI finance agents. It is designed for studying and applying investment approaches, including inside Questflow and other agent tools. The catalogue includes portable skill packages from the library.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add questflowai/investorskills --skill mungergit clone --depth 1 https://github.com/questflowai/investorskillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/questflowai/investorskills/munger)<a href="https://agentmods.dev/skills/questflowai/investorskills/munger"><img src="https://agentmods.dev/badge/skills/questflowai/investorskills/munger/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/questflowai/investorskills/munger"><img src="https://agentmods.dev/badge/skills/questflowai/investorskills/munger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00030 | $0.00512 |
| Opus 5 | $0.00015 | $0.00256 |
| Sonnet 5 | $0.00006 | $0.00102 |
| Haiku 4.5 | $0.00003 | $0.00051 |
Grade A, and why
munger scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Munger Mental Models
Use this skill to apply Charlie Munger-style judgment: invert the problem, remove obvious stupidity, examine incentives, use multiple mental models, and only act when quality and understanding are unusually high.
When To Use
Use this skill when the user asks for:
- Mental-model analysis of an investment
- Inversion and pre-mortem review
- Incentive and management-quality analysis
- Whether a decision is inside the circle of competence
- Concentrated decision review before acting
Trigger phrases include Munger, mental models, inversion, incentives, latticework, circle of competence, and avoid stupidity.
Do Not Use When
- The user needs a mechanical technical entry.
- The decision depends on short-term price action only.
- There is not enough information to understand incentives and business quality.
- The user wants confirmation of a decision they already made.
Inputs Needed
- Company or decision context
- Business model and economics
- Incentives of management, customers, regulators, and competitors
- Known risks, unknowns, and failure modes
- Current valuation or opportunity cost if relevant
Process
- Invert: ask how this investment could fail badly.
- Check circle of competence and reject what cannot be understood.
- Examine incentives and agency problems.
- Apply multiple models: competition, scale, habit, psychology, leverage, regulation, and opportunity cost.
- Look for lollapalooza effects where several forces reinforce each other.
- Decide whether the answer is obvious enough to justify concentration.
Output Format
# Munger View: [Decision]
## Verdict
Act / Wait / Pass / Too Hard
## Inversion
## Incentives
## Mental Models Applied
## Quality Filter
## Opportunity Cost
## Biggest Ways This Fails
## Missing Data
Guardrails
- Do not overcomplicate what should be rejected quickly.
- Do not ignore incentives.
- Do not invest outside the circle of competence.
- Do not mistake intelligence for judgment.
- Avoid obvious stupidity before seeking brilliance.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 81 lines · 30 tokens per session scan A df3e7ff3bae7
munger is a skill published in the GitHub repository questflowai/investorskills (1,842 stars, last pushed 17d ago), licensed MIT. It adds 30 tokens to every session and 512 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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