# Income Investment

AI Berkshire skill: Income Investment: Durable and Opportunistic Distribution Analysis.

- Repository: https://github.com/xbtlin/ai-berkshire (16972 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add xbtlin/ai-berkshire --skill income-investment -g -a claude-code -y`
Codex: `npx skills add xbtlin/ai-berkshire --skill income-investment -g -a codex -y`
Cursor: `npx skills add xbtlin/ai-berkshire --skill income-investment -g -a cursor -y`
Gemini CLI: `npx skills add xbtlin/ai-berkshire --skill income-investment -g -a gemini-cli -y`

## Description

AI Berkshire skill: Income Investment: Durable and Opportunistic Distribution Analysis. Source: skills/income-investment.md.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Codex adapter note

This skill is generated from `skills/income-investment.md` so Claude Code and Codex users share one canonical workflow.

- Treat `$ARGUMENTS` as the user's request in the current Codex thread.
- When the source mentions Claude-only surfaces such as Task, Agent, WebSearch, Bash, Read, or Write, use the closest Codex capability available in this session: subagents when available, web search when needed, shell commands for local tools, and normal file edits for workspace files.
- Use shared project tools from `tools/` in this repository. Prefer running commands from the repository root with paths like `python3 tools/financial_rigor.py ...`; if the current thread starts outside the repo, locate the actual checkout path first instead of assuming a fixed home-directory path.
- Before starting research, run the `date` command to confirm today's date; treat it as the baseline for "latest" data and state the data cutoff date in the report header. Never assume the current date from training data.
- Preserve the research quality rules from `AGENTS.md`: cross-check financial data, use exact arithmetic tools for valuation/math, and clearly label uncertainty and source gaps.

# Income Investment: Durable and Opportunistic Distribution Analysis

Analyze `$ARGUMENTS` to answer:

> Can this company produce sufficiently durable and attractive distributable income to justify a portfolio role, either as a long-term income conviction or as an opportunistic yield position?

Never treat a high displayed yield as evidence of a good opportunity. This workflow is for learning and research, not personalized investment advice.

## Input

Use this command form:

```text
/income-investment "<company or ticker>" [mode=new|existing] [role=core-income|opportunistic-income|unspecified] [quantity=...] [cost_basis=...] [portfolio_weight=...] [target_yield=...] [tax_residence=...] [portfolio_file=...] [horizon=...]
```

The rest: https://github.com/xbtlin/ai-berkshire/blob/HEAD/codex-skills/income-investment/SKILL.md