How the Deep Company Series Generates Comprehensive Research Reports in AI Berkshire

The Deep Company Series is an eight-part, AI-driven workflow defined in skills/deep-company-series.md that transforms a single company name into a 120,000-word research suite through structured templates, strict fact-checking protocols, and four execution phases.

The deep company series serves as the primary research engine for the AI Berkshire project, converting a simple company query into a comprehensive, publication-ready investment analysis. Orchestrated through the canonical skill definition in skills/deep-company-series.md, this system enforces institutional-grade quality controls across every stage of the writing process.

Triggering the Research Pipeline

Users initiate the workflow by issuing the slash command /deep-company-series <company> within Claude or Codex interfaces. This command launches the "deep-company-series" skill, which acts as the central orchestrator for the entire pipeline.

The skill definition is mirrored in codex-skills/deep-company-series/SKILL.md to ensure identical behavior across environments. Whether running locally or in a cloud agent, the system references the same source file, guaranteeing consistent output formatting and quality standards.

/deep-company-series 腾讯

Local execution loads the YAML skill definition directly from the repository:

python3 -c "import pathlib, yaml; \
skill = yaml.safe_load(open('skills/deep-company-series.md')); \
print(f\"Launching {skill['name']} for {sys.argv[1]}\")" 腾讯

The Eight-Article Template Structure

The skill defines a rigid eight-article template (01-08) that structures the final research suite. Each slot targets specific analytical dimensions with assigned word counts to ensure comprehensive coverage.

The standardized sequence includes:

• 01-认知重置.md – Cognition reset and foundational context
• 02-护城河.md – Moat analysis and competitive positioning
• 03-利润引擎.md – Profit engine and revenue drivers
• 04-隐性资产.md – Hidden assets and off-balance-sheet value
• 05-AI时代前景.md – AI-era outlook and technological disruption
• 06-财务深度.md – Financial deep-dive and ratio analysis
• 07-管理层评估.md – Management assessment and governance
• 08-估值与卖出.md – Valuation metrics and exit signals

Generated articles are stored sequentially under reports/<公司名>/《看懂<公司名>》/, creating a navigable research directory:

reports/拼多多/《看懂拼多多》/
│   01-认知重置.md
│   02-护城河.md
│   …
│   08-估值与卖出.md
│   00-系列说明.md   ← internal index, not published

Editorial Standards and Quality Gates

Before any content generation begins, the skill enforces strict writing-style rules covering tone, vocabulary restrictions, headline formatting, and a blacklist of subjective words. These constraints ensure the output remains data-driven and investment-grade rather than speculative.

The workflow implements two mandatory seven-item checklists:

The "伪精确" (Pseudo-Precision) Checklist – Forces verification of weighted probabilities, prevents over-extrapolation, and requires disclosure of holdings or conflicts of interest before saving drafts.

The "修订时必跑的检查" (Mandatory Revision Checks) – Ensures cross-article consistency, proper unit labeling for all financial figures, and rigorous source attribution for every statistical claim.

The Four-Phase Execution Pipeline

The deep company series operates through a structured four-phase workflow defined in the skill file:

Phase 1 – Research
The system ingests five years of annual reports and a minimum of three sell-side analyst analyses. Research agents (/investment-team or /investment-research) compile foundational data before writing begins.

Phase 2 – Writing
Articles are generated sequentially in the 01-08 order, with each draft stored immediately to reports/<公司名>/《看懂<公司名>》/0X-XX.md. This linear approach ensures narrative coherence and logical dependency chains between sections.

Phase 3 – Cross-Article Scan
An "Explore agent" executes automated consistency checks across the entire series. This scan validates numeric consistency, terminology definitions, and internal reference linking using the helper script:

python3 scripts/run-explore-agent.py \
  --target reports/拼多多/《看懂<公司名>》/ \
  --checks numeric-consistency terminology

Phase 4 – Final Release Check
A local grep operation scrubs personal identifiers from all files. The system injects the mandatory disclaimer ("不构成投资建议") and verifies no private data remains before marking the series as publication-ready.

Revision Handling and Compliance

Post-generation modifications follow a structured triage system categorizing feedback as hard-error, subjective, granular, or unreliable. Each accepted change triggers a linked-update scan that propagates necessary adjustments across all eight articles to maintain internal consistency.

Publication guardrails enforce strict compliance rules: no private data may be exposed, and all outputs must include the standardized investment disclaimer. These checks are hard-coded into the skill definition at lines 84-90 of skills/deep-company-series.md, ensuring automated enforcement before any public release.

Summary

• The deep company series uses the skill file skills/deep-company-series.md as its single source of truth for orchestrating AI-generated research.
• It produces eight standardized articles (01-08) covering moats, financials, management, and valuation, totaling approximately 120,000 words.
• Two seven-item checklists enforce fact-checking and cross-article consistency before publication.
• The four-phase pipeline (Research → Writing → Cross-Article Scan → Final Release) ensures institutional-grade quality control.
• Generated content is stored in reports/<公司名>/《看懂<公司名>》/ with automated scans for numeric consistency and terminology alignment.

Frequently Asked Questions

What is the deep company series in AI Berkshire?

The deep company series is a structured AI workflow that converts a single company name into a comprehensive eight-part research report. Defined in skills/deep-company-series.md, it coordinates multiple specialized agents to produce approximately 120,000 words of investment-grade analysis covering competitive moats, financial health, and valuation metrics.

How many articles does the deep company series generate?

The workflow generates eight distinct markdown articles numbered 01 through 08, plus an internal index file (00-系列说明.md). The series covers cognition reset, moat analysis, profit engines, hidden assets, AI outlook, financial deep-dives, management assessment, and valuation signals.

What quality checks ensure accuracy in the deep company series?

The system enforces two seven-item checklists: the "伪精确" checklist prevents pseudo-precision and undisclosed conflicts, while the "修订时必跑的检查" ensures cross-article consistency and proper source attribution. Additionally, the "Explore agent" runs automated numeric consistency scans during Phase 3 of the pipeline.

Where are the generated reports stored?

Completed articles are saved to reports/<公司名>/《看懂<公司名>》/ within the repository root, following the naming convention 0X-标题.md. This directory structure separates research outputs by company while maintaining a standardized eight-file format for every analysis.

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