# How to Generate a Deep Company Series Analysis with AI Berkshire

> Generate a deep company series analysis with AI Berkshire. This powerful AI tool automates an eight-article research pipeline, delivering publication-ready investment analysis.

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: how-to-guide
- Published: 2026-06-27

---

**AI Berkshire's `/deep-company-series` skill orchestrates an eight-article, ~120,000-word research pipeline that produces publication-ready investment analysis through a four-phase multi-agent workflow.**

The `xbtlin/ai-berkshire` repository provides a specialized command-line tool for generating comprehensive deep company series analysis. This skill leverages a multi-agent framework to automate the creation of rigorous, fact-checked investment research suitable for professional publication. When you generate a deep company series analysis, the system produces eight interconnected markdown articles stored in a structured directory hierarchy.

## Understanding the `/deep-company-series` Skill Architecture

The deep company series functionality is defined in **[`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md)**, which serves as the orchestration blueprint for the entire research pipeline. This skill implements the same four-master framework used across AI Berkshire's investment research tools, ensuring consistency with rigorous financial analysis standards.

### The Eight-Article Blueprint

The skill automatically generates a standardized series structure beginning with a `00-系列说明.md` index page followed by eight specialized articles (01-08). According to the skill definition in [`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md) (lines 20-32), each article targets specific focus questions with defined word-count ranges:

- **Article 01**: Foundational misconceptions and business model clarity
- **Articles 02-07**: Sequential deep-dives into financial metrics, competitive positioning, management quality, and risk factors
- **Article 08**: Valuation synthesis with entry/exit signals

### Writing Discipline and Quality Controls

The skill enforces strict editorial standards through two enforcement mechanisms defined in [`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md) (lines 47-63). First, a **"禁用词"** (banned words) list eliminates subjective phrasing and promotional language. Second, the **"写作风格规范"** (writing style specifications) mandate neutral tone and bullet-point structure for scannability.

Before publication, a **7-point "严苛事实核查 Checklist"** (Strict Fact-Check Checklist) runs validation across all drafts (lines 66-86). This validates cross-article number consistency, data-source labeling, and terminology standardization.

## The Four-Phase Research Workflow

When you execute `/deep-company-series`, the skill initiates a structured workflow defined in [`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md) (lines 98-130).

### Phase 1 – Research and Data Collection

The system requires three input categories to seed the series:

- Annual and quarterly reports (10-K, 10-Q equivalents)
- Minimum three independent sell-side research reports
- Optional pre-computed analysis from `/investment-team` or `/investment-research` skills

This data ingestion phase establishes the factual foundation before any drafting begins.

### Phase 2 – Sequential Drafting

The skill writes articles sequentially (01→08) into `reports/{Company}/《看懂{Company}》/` directories. As specified in [`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md) (lines 107-112), drafts remain in local storage without immediate Git commits, allowing for iterative refinement between articles.

### Phase 3 – Cross-Article Consistency Scan

After all eight drafts complete, an **Explore agent** validates consistency across the series. This scan checks for numeric value alignment, standardized terminology usage, and cross-reference accuracy between articles (lines 14-21).

### Phase 4 – Privacy Scan and Publication

Before final commit, a `grep`-based privacy scan executes to detect potential PII leaks:

```bash
grep -r "linxuan\|/Users/" reports/{Company}/《看懂{Company}》/

```

If no matches return, the series passes validation and becomes eligible for Git commit and push operations (lines 24-30).

## Step-by-Step Implementation Guide

To generate a deep company series analysis in your local environment, follow these installation and execution steps.

### Installation

First, install the skill using the provided installation script:

```bash

# Clone the repository first

git clone https://github.com/xbtlin/ai-berkshire.git
cd ai-berkshire

# Install the skill (choose based on your AI coding tool)

./scripts/install-codex-skills.sh

# OR

./scripts/install-claude-commands.sh

```

### Running the Series Generator

Execute the skill with a target company name. For example, to analyze PDD Holdings:

```bash
/deep-company-series 拼多多

```

The system responds with creation confirmation and a file list:

```

✅ 已创建 “拼多多” 系列项目
📄 生成 00‑系列说明.md、01‑你以为你看懂了拼多多，其实没有.md … 08‑多少钱值得买，什么信号必须卖.md
🔎 请提供公司年报、最近季报以及至少 3 份卖方研报链接

```

### Reviewing Drafts

During the drafting phase, inspect individual articles before final publication:

```bash
cat reports/拼多多/《看懂拼多多》/01-你以为你看懂了拼多多，其实没有.md

```

### Final Verification and Commit

After the cross-article scan completes, run the privacy verification:

```bash
grep -r "linxuan\|/Users/" reports/拼多多/《看懂拼多多》/

```

If no privacy warnings appear, commit the series:

```bash
git add reports/拼多多/《看懂拼多多》/
git commit -m "Add deep-company-series for 拼多多"
git push

```

## Key Source Files and Dependencies

The deep company series analysis relies on several critical components within the AI Berkshire repository:

- **[`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md)**: Core skill definition containing the eight-article templates, writing rules, and workflow orchestration logic.
- **[`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py)**: Supporting utility for precise financial calculations, including market-cap verification and cross-validation functions used throughout the series generation.
- **`reports/腾讯/《看懂腾讯》/`**: Reference implementation showing the complete output structure for Tencent (腾讯), demonstrating the expected directory layout and article formatting.

## Summary

- **AI Berkshire** provides the `/deep-company-series` skill for automated generation of comprehensive eight-article investment research series.
- The workflow follows four phases: Research → Draft → Cross-Article Scan → Publication, enforced through [`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md).
- Each series produces approximately **120,000 words** across eight standardized articles with strict fact-checking requirements.
- Output files generate in `reports/{Company}/《看懂{Company}》/` with mandatory privacy scanning before Git commit.
- The system requires annual reports, quarterly filings, and minimum three sell-side research reports as input seeds.

## Frequently Asked Questions

### What input data does the deep company series require?

The skill requires annual reports, recent quarterly filings, and at least three independent sell-side research reports to initialize the research phase. You may optionally supplement this with existing outputs from `/investment-team` or `/investment-research` skills to accelerate the process.

### How long does it take to generate a complete eight-article series?

While timing varies by company complexity, the skill is designed to produce approximately **120,000 words** across eight articles. The sequential drafting process (Phase 2) runsarticle-by-article to maintain narrative consistency, with the cross-article scan (Phase 3) running only after all drafts complete.

### Can I customize the article templates for specific industries?

The current implementation in [`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md) uses fixed eight-article templates (01-08) with specific focus questions. While the titles and focus areas are standardized for investment analysis consistency, you can modify the underlying skill file before installation to adjust word-count targets or specific focus questions for sector-specific needs.

### How does the system ensure factual accuracy across all eight articles?

The **7-point Strict Fact-Check Checklist** runs automated validation for numerical consistency, data-source labeling, and terminology standardization. Additionally, the **Cross-Article Scan** (Phase 3) employs an Explore agent to verify that metrics referenced in early articles match values cited in later valuation synthesis sections.