# How to Configure Deep-Company-Series for 8-Article Long-Form Research Output

> Learn how to configure deep-company-series for 8-article long-form research. Select high complexity, allocate pillars, and set word counts for detailed output.

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: how-to-guide
- Published: 2026-07-28

---

**To configure the deep-company-series skill for maximum 8-article output, select the high-complexity path in [`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md), allocate all eight core research pillars, and set word-count targets ranging from 4,000 to 12,000 characters per article.**

The **deep-company-series** skill in the `xbtlin/ai-berkshire` repository generates multi-article research series that provide textbook-level analysis of single companies. When configured for the maximum 8-article format, it produces comprehensive coverage suitable for conglomerates with multiple business lines and complex asset structures like Tencent.

## Select the High-Complexity Path

The 8-article configuration is explicitly designed for companies with intricate operational profiles. According to the complexity table defined in [`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md) (lines 26-30), this tier accommodates firms with multiple business lines, hidden investment portfolios, and extensive management-team history.

**High-complexity indicators include:**
- Diverse revenue streams across unrelated sectors
- Significant off-balance-sheet assets or holding-company structures
- Historical M&A activity requiring deep institutional memory

## Allocate the Eight Core Research Pillars

The skill defines eight "主轴" (core pillars) that each become a standalone article. For the full 8-article series, implement the complete template from lines 42-53 of [`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md):

1. **"你以为你看懂了 X，其实没有"** — Cognition reset that challenges superficial assumptions
2. **"X 的护城河"** — Moat analysis covering competitive advantages
3. **"X 的最大利润引擎"** — Profit engine identification and unit economics
4. **"X 藏在账上的另一家公司"** — Hidden assets and investment portfolio deep-dive
5. **"AI/时代变量"** — AI and era-specific variable impact assessment
6. **"用巴菲特方式拆 X 的财报"** — Financial statement analysis using Buffett methodology
7. **"管理层金句"** — Management team evaluation and historical decision analysis
8. **"多少钱值得买，什么信号必须卖"** — Valuation framework and exit criteria

Each pillar maps to a separate markdown file in the final output.

## Configure Article Length Targets

The skill specifies precise word-count ranges to maintain consistent depth across the series. Set your prompt parameters to target these ranges as documented in lines 46-53:

- **Article 1 (Intro):** 4,000–5,000 characters
- **Articles 2–7 (Body):** 6,000–8,000 characters each
- **Article 8 (Valuation):** 10,000–12,000 characters

These targets ensure the final decision article receives appropriate analytical weight while maintaining reader engagement throughout the series.

## Set Up the Series Infrastructure

### Create the Series Index File

Before generating content, create a `00-系列说明.md` file that serves as the internal table of contents. This file lists all eight articles in sequence but remains unpublished (lines 55-56). The index ensures logical flow between the cognition reset opening and the valuation conclusion.

### Establish the Output Directory Structure

Persist each article under the path `reports/{公司名}/《看懂{公司名}》-{YYYYMMDD}/0X-XX.md`. The "目录冲突规则" (directory conflict rules) in lines 58-60 mandate that if a previous series exists for the same company, you must create a new dated folder rather than overwriting existing files.

## Structure Individual Articles

Every article in the series must follow the standardized skeleton defined in lines 65-68 of the skill file.

### Required Header Block

Each file must begin with a header block containing the series name and article number. This metadata enables the cross-article validation tools to identify and link related files.

### Content Skeleton Requirements

- **Opening Hook:** Begin with a "数字/曲线/惊人事实" (number/chart/surprising fact) hook in the first paragraph (lines 66-68)
- **Mid-Article Structure:** Develop the pillar-specific analysis using the prescribed frameworks
- **Closing Elements:** Include a "本篇要点回顾" (key points review) bullet list and a "下期预告" (next episode teaser) at the end (lines 67-69)

## Enforce Quality Controls

### Run the 7-Item Fact-Check Checklist

Before finalizing each article, execute the checklist defined in lines 24-34. This verification ensures:
- Cross-article number consistency
- Standardized term definitions
- Elimination of duplicate counting across pillars
- Source attribution accuracy

### Execute Cross-Article Validation

After drafting all eight articles, launch the **Explore agent** to automatically verify consistency across the series. This agent checks for numerical alignment, terminology standardization, and cross-reference integrity (lines 63-70).

## Style and Privacy Requirements

Adhere to the style guide in lines 87-94: maintain a direct, data-first tone; avoid absolute-value words; and ensure the first 18-20 characters function as a "stand-alone" mobile preview.

Before committing, perform a grep search for personal identifiers. Push the series to the repository only after explicit user approval (lines 71-78).

## Programmatic Configuration

Invoke the skill via the AI-Berkshire CLI to generate the configured 8-article series:

```python
from pathlib import Path
import subprocess

def generate_deep_series(company_name: str, complexity: str = "high"):
    """
    Wraps the deep-company-series skill to produce an 8-article series.
    """
    prompt = f"""# {company_name}

    $ARGUMENTS = {company_name}
    """
    result = subprocess.run(
        ["ai-berkshire", "run", "deep-company-series"],
        input=prompt.encode(),
        capture_output=True,
    )
    return result.stdout.decode()

# Generate for a high-complexity firm

print(generate_deep_series("腾讯"))

```

Alternatively, use the bash interface:

```bash
ai-berkshire run deep-company-series --company "腾讯" --complexity high

```

Both methods reference the master template at [`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md) and output eight markdown files following the architectural specifications above. After modifying the skill, run [`scripts/sync-codex-skills.py`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/sync-codex-skills.py) to regenerate the Codex-compatible prompt at [`codex-prompts/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-prompts/deep-company-series.md).

## Summary

- **Select high-complexity tier** in [`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md) (lines 26-30) to unlock the 8-article format for conglomerate analysis
- **Implement all eight pillars** from the template (lines 42-53), mapping each to a numbered markdown file
- **Target specific word counts:** 4k-5k for intro, 6k-8k for body articles, 10k-12k for valuation conclusion
- **Create `00-系列说明.md`** as the internal index and store articles in dated folders under `reports/{公司名}/`
- **Enforce structural standards:** header blocks, data-first hooks, key-point reviews, and next-episode teasers
- **Validate quality** using the 7-item checklist and Explore agent cross-article verification
- **Maintain style compliance** with mobile-optimized openings and privacy grep checks before release

## Frequently Asked Questions

### What distinguishes the 8-article configuration from shorter series?

The 8-article configuration activates the "high-complexity" path designed for companies with multiple business lines, hidden assets, and rich management history. According to [`skills/deep-company-series.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/deep-company-series.md) (lines 26-30), shorter series (3-5 articles) use condensed pillar combinations, while the 8-article format dedicates individual articles to hidden assets, AI variables, and management history that simpler corporate structures do not require.

### How does the Explore agent validate cross-article consistency?

The Explore agent automatically scans all eight markdown files in the series directory to verify numerical consistency, standardized terminology, and cross-reference integrity (lines 63-70). It ensures that financial figures cited in the profit engine article align with the valuation article, and that management quotes in article 7 do not contradict historical data in article 1.

### Can I customize the word-count targets for specific articles?

While the skill recommends 4,000–5,000 characters for the introduction, 6,000–8,000 for middle pillars, and 10,000–12,000 for the final valuation piece (lines 46-53), these ranges serve as guidance rather than hard limits. However, deviating significantly from these targets may break the "textbook-level" pacing expected by readers of the 8-article format.

### What happens if a previous series exists for the same company?

The "目录冲突规则" (directory conflict rules) in lines 58-60 prohibit overwriting existing series. When a folder matching `reports/{公司名}/《看懂{公司名}》-{YYYYMMDD}/` already exists, you must generate a new timestamped directory rather than appending to or replacing the previous analysis. This preserves historical versions and prevents accidental data loss.