# Portfolio Management Skills in AI Berkshire: Automated Auditing and Income Allocation

> Discover AI Berkshire portfolio management skills like automated auditing and income allocation. Explore `portfolio-review` and `income-investment` for efficient analysis and decision-making.

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: how-to-guide
- Published: 2026-07-27

---

**AI Berkshire provides two specialized portfolio management skills—`portfolio-review` and `income-investment`—that automate portfolio auditing, concentration analysis, stress-testing, and income-focused allocation decisions through a Task-driven WebSearch pipeline.**

The **xbtlin/ai-berkshire** repository extends beyond company research to offer comprehensive portfolio management capabilities. These skills enable investors to analyze position sizing, monitor concentration risk, and evaluate income-generating securities within the context of existing holdings. All portfolio operations follow a canonical workflow defined in `skills/*.md` files and execute through a modular architecture that separates skill definitions from data-validation utilities.

## Available Portfolio Management Skills in AI Berkshire

AI Berkshire implements two distinct skills that handle different aspects of portfolio construction and maintenance.

### `portfolio-review`: Comprehensive Portfolio Auditing and Rebalancing

The **`portfolio-review`** skill executes a full portfolio audit workflow that progresses from data ingestion to optimization recommendations. It accepts user-supplied holdings expressed as percentages, share counts with cost basis, or reads from the persistent file [`reports/portfolio-latest.md`](https://github.com/xbtlin/ai-berkshire/blob/main/reports/portfolio-latest.md). The skill enriches each position with real-time market data—including price, valuation multiples, and analyst estimates—via the **Task-driven WebSearch pipeline**.

After data enrichment, the skill performs health checks, **concentration and correlation analysis**, and **opportunity-cost ranking** to identify suboptimal allocations. It then runs stress-test scenarios against the portfolio structure and emits a structured report that overwrites [`reports/portfolio-latest.md`](https://github.com/xbtlin/ai-berkshire/blob/main/reports/portfolio-latest.md) with updated conclusions. This creates a feedback loop where subsequent skill invocations build upon previous analysis states.

### `income-investment`: Income-Oriented Allocation and Role Assignment

The **`income-investment`** skill evaluates whether a security can generate durable dividend or interest income suitable for specific portfolio roles. It assesses securities against defined allocation categories such as **core-income** and **opportunistic-income** positions.

This skill invokes **[`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py)** to perform precise yield calculations and yield-on-cost analysis. When a portfolio file is supplied, it automatically reads the latest `portfolio-review` output to ensure new income positions do not contradict existing allocation recommendations or concentration limits.

## Architecture and Workflow Integration

Both skills adhere to the **canonical workflow** architecture that separates concerns across three layers. The **skill definition** layer consists of Markdown files in `skills/*.md` that specify logic and parameters. The **execution engine** layer comprises Claude Code and Codex runtime environments that compile these definitions into executable artifacts stored in `codex-skills/*/SKILL.md`.

The **data-validation layer** provides reusable utilities that ensure reproducibility. The [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) module supplies precise valuation and scenario calculations used by both portfolio skills, while [`tools/stock_screener.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/stock_screener.py) offers additional filtering capabilities. This modular design keeps the system testable and allows the Task-driven WebSearch pipeline to inject live market data without contaminating the core logic.

## Practical Usage Examples

The following commands demonstrate how to invoke the portfolio management skills in AI Berkshire:

```bash

# Review a portfolio expressed as percentages

/portfolio-review "腾讯30%, 美团20%, 茅台20%, 现金30%"

# Review a portfolio expressed as share counts with cost basis

/portfolio-review "腾讯 500股 @480HK, 美团 1000股 @130HK"

# Use an existing saved portfolio file

/portfolio-review "我的持仓"

# Evaluate a dividend-paying security for a specific role

/income-investment "中国平安" mode=new role=core-income quantity=200 cost_basis=45 portfolio_file=reports/portfolio-latest.md

```

These invocations automatically fetch the latest market data, perform the appropriate concentration or income-fit analyses, and update the persistent portfolio report at [`reports/portfolio-latest.md`](https://github.com/xbtlin/ai-berkshire/blob/main/reports/portfolio-latest.md).

## Key Implementation Files

- **[`skills/portfolio-review.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/portfolio-review.md)** – Defines the complete portfolio-review skill including parsing logic, data enrichment procedures, concentration analysis, and stress-testing algorithms.

- **[`skills/income-investment.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/income-investment.md)** – Defines the income-investment skill specifying dividend yield calculations, portfolio role assignments, and integration points with existing portfolio data.

- **[`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py)** – Provides precise valuation, yield-on-cost, and scenario calculation functions consumed by both portfolio skills.

- **[`reports/portfolio-latest.md`](https://github.com/xbtlin/ai-berkshire/blob/main/reports/portfolio-latest.md)** – The persistent portfolio report file that `portfolio-review` reads from and writes to, maintaining state between analysis sessions.

- **[`codex-skills/portfolio-review/SKILL.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-skills/portfolio-review/SKILL.md)** – Auto-generated Codex artifact linking the portfolio-review skill definition to the execution runtime.

- **[`codex-skills/income-investment/SKILL.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-skills/income-investment/SKILL.md)** – Auto-generated Codex artifact linking the income-investment skill definition to the execution runtime.

## Summary

- AI Berkshire provides two primary **portfolio management skills**: `portfolio-review` for comprehensive auditing and `income-investment` for dividend-focused allocation.
- The **`portfolio-review`** skill performs concentration analysis, correlation checks, opportunity-cost ranking, and stress-testing while persisting results to [`reports/portfolio-latest.md`](https://github.com/xbtlin/ai-berkshire/blob/main/reports/portfolio-latest.md).
- The **`income-investment`** skill evaluates securities for specific income roles by calling **[`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py)** and cross-referencing existing portfolio data.
- Both skills utilize a **Task-driven WebSearch pipeline** for real-time market data enrichment and follow a modular architecture separating skill definitions from validation utilities.

## Frequently Asked Questions

### What is the primary function of the `portfolio-review` skill in AI Berkshire?

The `portfolio-review` skill automates complete portfolio audits by parsing holdings, enriching positions with live market data via the Task-driven WebSearch pipeline, and running concentration, correlation, and stress-test analyses. It outputs structured reports to [`reports/portfolio-latest.md`](https://github.com/xbtlin/ai-berkshire/blob/main/reports/portfolio-latest.md) and provides rebalancing recommendations based on opportunity-cost rankings.

### How does the `income-investment` skill determine if a security fits a portfolio?

The skill evaluates whether a security can generate durable dividend income appropriate for specific roles such as **core-income** or **opportunistic-income** using precise calculations from [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py). It checks yield sustainability and reads existing `portfolio-review` outputs to prevent contradictory allocation recommendations when a portfolio file is specified.

### Where does AI Berkshire store portfolio analysis results between sessions?

AI Berkshire persists portfolio analysis results in **[`reports/portfolio-latest.md`](https://github.com/xbtlin/ai-berkshire/blob/main/reports/portfolio-latest.md)**, which serves as the canonical state file. The `portfolio-review` skill reads from this file when analyzing existing portfolios and writes updated conclusions back to it, enabling cumulative analysis across multiple invocations.

### What tools support the calculation logic for AI Berkshire's portfolio skills?

The **[`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py)** module provides the core calculation engine for both skills, handling precise valuation metrics, yield-on-cost computations, and scenario analyses. The **[`tools/stock_screener.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/stock_screener.py)** utility offers additional data validation capabilities, while the Task-driven WebSearch pipeline handles external market data retrieval.