AI Berkshire Financial Analysis Skills: A Modular Framework for Institutional-Grade Research

AI Berkshire offers six specialized financial analysis skills—including financial-data acquisition, financial-analyst valuation modeling, financial-detective research for private firms, income-investment evaluation, earnings-review validation, and portfolio-review monitoring—all enforced by a strict validation layer in tools/financial_rigor.py that eliminates LLM calculation errors through exact arithmetic and cross-validation.

The xbtlin/ai-berkshire repository implements a modular "skill" architecture where each financial analysis capability is expressed as a markdown file under skills/. These definitions drive Claude-Code slash-commands and Codex-generated prompts while orchestrating underlying financial rigor utilities to ensure accurate, source-validated research for both public and private companies.

Core Financial Analysis Skills

AI Berkshire organizes its capabilities into distinct roles, each targeting specific stages of the investment research workflow. All skills share a common validation layer (tools/financial_rigor.py) that guarantees exact arithmetic and enforces the >1% discrepancy rule defined in the financial data specification.

Financial-Data: Standardized Acquisition

The financial-data skill provides standardized acquisition and cross-validation of corporate financial metrics across global markets. Defined in skills/financial-data.md, this skill maintains a data-source matrix covering US equities (macrotrends, stockanalysis), Hong Kong and A-shares (aastocks, 东方财富), and Taiwan (FinMind).

The skill implements error-threshold logic: discrepancies ≤1% are auto-accepted, 1-5% trigger warnings, and >5% force manual verification (see financial-data.md lines 73-77). For Taiwanese stocks, it provides command-line wrappers invoking tools/twstock_data.py (lines 40-48).

Financial-Analyst: Deep-Dive Valuation

The financial-analyst role (defined in skills/investment-team.md) executes deep-dive balance-sheet, cash-flow, and valuation analysis for listed companies. This skill utilizes financial_rigor.py to verify market capitalization, validate valuation multiples, and perform three-scenario modeling.

According to the source code in investment-team.md (lines 79-84), the analyst must cross-validate all metrics before building forward projections, ensuring no "LLM mental math" contaminates the valuation.

Financial-Detective: Private Company Research

For unlisted firms, the financial-detective role (see skills/private-company-research.md) assembles fragmented financial information from multiple low-confidence sources. This skill combines prospectus data, media reports, and financing-round information, annotating each data point with confidence icons (🟢, 🟡, 🔴) to surface reliability levels (line 27).

The detective runs alternative valuation methods—DCF, comparable-company analysis, and financing-round analysis—using the same rigorous validation rules as public-company research.

Income-Investment: Dividend Analysis

The income-investment skill focuses on evaluating dividend yield, payout ratios, and income-generation potential. It calls financial_rigor.py for exact payout and yield calculations, ensuring that income-focused portfolio decisions rely on verified arithmetic rather than estimated approximations.

Earnings-Review: Release Validation

The earnings-review and earnings-team skills validate quarterly earnings releases and prepare briefing materials for earnings calls. As implemented in skills/earnings-review.md (lines 78-90), this workflow executes cross-validation of reported figures, verifies market-cap consistency, and runs scenario analysis to contextualize surprises against forward guidance.

Portfolio-Review: Holdings Monitoring

The portfolio-review skill applies financial-rigor checks to every holding in a portfolio. It runs verify-valuation across positions and annotates data-richness grades, enabling systematic monitoring of valuation drift and data quality degradation across the investment book.

Technical Architecture and Validation Layer

The reliability of AI Berkshire's financial analysis skills stems from its strict separation between prompt orchestration (skills) and calculation execution (tools).

Skill Definition Layer (skills/*.md)

Each markdown file under skills/ describes a complete task flow: role definition, required data inputs, execution steps, and exact CLI commands to invoke supporting tools. For example, skills/financial-data.md lists source priorities by market region and provides sample Bash invocations for twstock_data.py (lines 42-45).

Financial Rigor Engine (tools/financial_rigor.py)

The financial_rigor.py utility supplies the computational backbone with sub-commands including:

• verify-market-cap: Validates market capitalization calculations
• verify-valuation: Checks PE/PB multiple consistency
• cross-validate: Compares values across data sources with error threshold handling
• three-scenario: Runs bull/base/bear valuation modeling

This tool is called from multiple skills (see investment-team.md lines 79-84, earnings-review.md lines 78-90) to guarantee that all numerical outputs derive from exact arithmetic rather than language model approximations.

Taiwanese Market Data (tools/twstock_data.py)

For Taiwan equity markets, twstock_data.py provides a zero-dependency script wrapping the FinMind API. Referenced in financial-data.md (line 36), this tool enables real-time quote retrieval and valuation metric calculation without external dependencies.

Practical Command-Line Workflows

The following examples demonstrate the exact CLI patterns implemented in the AI Berkshire skills framework.

Cross-Validating US Financial Metrics

To pull revenue, EPS, and ROE with automatic discrepancy flagging:

python3 tools/financial_rigor.py cross-validate \
  --field revenue \
  --values '{"macrotrends":1234,"stockanalysis":1228}' \
  --unit "亿元"

This follows the priority pattern defined in financial-data.md (source 1 = macrotrends, source 2 = stockanalysis), automatically flagging any variance exceeding the 1% tolerance threshold.

Verifying Taiwanese Stock Valuations

python3 tools/twstock_data.py valuation 2330
python3 tools/financial_rigor.py verify-valuation \
  --price 520 \
  --eps 23.5 \
  --bvps 28.1

The first command obtains the latest quote and PER/PBR from FinMind (see financial-data.md lines 42-45). The second validates the derived valuation metrics against exact inputs.

Running Three-Scenario Valuation Models

python3 tools/financial_rigor.py three-scenario \
  --price 145 \
  --eps 7.2 \
  --shares 5.3 \
  --growth 5 7 12 \
  --pe 12 15 20

This replicates the workflow embedded in the financial-analyst role (see investment-team.md line 83), generating bull, base, and bear case valuations using explicit growth and multiple assumptions.

Assembling Private-Company Financials

For unlisted fintech revenue estimation with confidence tagging:

python3 tools/financial_rigor.py cross-validate \
  --field revenue \
  --values '{"prospectus": "¥1.2B", "media_report": "¥1.15B"}' \
  --unit "人民币"

This reflects the "financial-detective" pattern described in private-company-research.md (lines 78-84), enabling triangulation of fragmented data sources.

Summary

• AI Berkshire implements six distinct financial analysis skills as modular markdown definitions under skills/, covering data acquisition, valuation analysis, private company research, income investing, earnings validation, and portfolio monitoring.
• All skills route numerical calculations through tools/financial_rigor.py, enforcing exact arithmetic and a >1% cross-validation threshold that prevents LLM hallucination in financial calculations.
• The architecture supports multi-market data sourcing (US, HK, A-shares, Taiwan) with specific wrappers like twstock_data.py for regional APIs, ensuring standardized input across diverse jurisdictions.
• Private company capabilities include confidence-tagging conventions (🟢🟡🔴) and fragmented-data triangulation, extending rigorous analysis beyond publicly traded entities.
• Command-line interfaces allow direct invocation of validation utilities, enabling integration into existing workflows or automated pipeline triggers.

Frequently Asked Questions

How does AI Berkshire prevent calculation errors in financial analysis?

AI Berkshire eliminates "LLM mental math" by routing all numerical operations through tools/financial_rigor.py, a dedicated validation layer that performs exact arithmetic. According to the source code in xbtlin/ai-berkshire, this tool enforces a >1% discrepancy rule: any variance between data sources exceeding 1% triggers warnings or manual verification requirements, ensuring that valuation and cross-validation outputs derive from precise computation rather than language model estimation.

What data sources does AI Berkshire support for financial metrics?

The financial-data skill maintains a prioritized source matrix covering multiple jurisdictions. For US equities, it uses macrotrends and stockanalysis; for Hong Kong and A-shares, aastocks and 东方财富 (East Money); for Taiwan, FinMind via the twstock_data.py wrapper. This multi-source approach enables the cross-validate command to detect data anomalies by comparing values across independent providers before ingestion into analysis workflows.

Can AI Berkshire analyze private companies without public filings?

Yes, the financial-detective role specializes in unlisted company research. As defined in skills/private-company-research.md, this skill assembles fragmented information from prospectuses, media reports, and financing rounds, applying confidence icons (🟢 for high confidence, 🟡 for medium, 🔴 for low) to annotate data reliability. It runs alternative valuation methods—DCF, comparable-company analysis, and financing-round analysis—using the same rigorous validation rules applied to public securities.

What is the three-scenario valuation workflow?

The three-scenario command in financial_rigor.py generates bull, base, and bear case valuations by accepting explicit growth rates and PE multiples as parameters. This workflow, embedded in the financial-analyst role within skills/investment-team.md, requires analysts to define optimistic (e.g., 12% growth, PE 20), baseline (7% growth, PE 15), and pessimistic (5% growth, PE 12) assumptions, then calculates implied valuations using exact share counts and earnings figures rather than approximate ranges.

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