# How the AI Berkshire Earnings Review Skill Works: Complete 8-Stage Technical Breakdown

> Discover how the AI Berkshire earnings review skill works with this 8-stage technical breakdown. Learn about data validation, tone analysis, and report generation.

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: deep-dive
- Published: 2026-06-30

---

**The AI Berkshire earnings review skill operates as a single-agent markdown-defined workflow that downloads primary financial documents, cross-validates data against approved sources, analyzes management tone signals, and passes through a rigorous audit gate before generating a structured investment report.**

The earnings review skill in the `xbtlin/ai-berkshire` repository implements a "deep-read" philosophy by processing first-hand source material through eight discrete analytical stages. Defined entirely in the Markdown-based skill file [`skills/earnings-review.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/earnings-review.md), this agentic workflow transforms raw SEC filings, earnings call transcripts, and footnotes into audited investment analysis without requiring human intervention in the data pipeline.

## What Is the AI Berkshire Earnings Review Skill?

The **earnings review skill** is a single-agent workflow designed to produce institutional-grade earnings reports from primary source materials. Unlike automated scrapers that rely on third-party summaries, this skill orchestrates parallel document acquisition, financial rigor checks, and semantic analysis of management discussions to surface alpha-generating signals hidden in regulatory filings. The runtime interprets the Markdown instructions in [`skills/earnings-review.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/earnings-review.md) as executable logic, allowing the skill to be invoked via slash commands like `/earnings-review 腾讯 2025Q4`.

## The 8-Stage Operational Pipeline

The skill executes an eight-stage pipeline, with each step codified in the skill definition file and supported by specific utility modules in the `tools/` directory.

### Stage 1: Material Availability Rating (A/B/C Classification)

Before processing begins, the system classifies source document availability into three tiers:

- **A级 (Grade A):** Full original documents obtained (10-K/10-Q/年报, earnings transcripts, shareholder letters)
- **B级 (Grade B):** Partial original documents or third-party compilations only
- **C级 (Grade C):** News reports and data website abstracts only

This rating determines the depth of original documentation used in subsequent analysis and is recorded at lines 26-31 of [`skills/earnings-review.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/earnings-review.md).

### Stage 2: Parallel Document Acquisition via Task Agents

The skill fires parallel **Task** agents to download four categories of primary materials simultaneously:

1. Regulatory filings (10-K/10-Q/年报)
2. Earnings call transcripts (业绩电话会纪要)
3. Management shareholder letters (管理层致股东信)
4. Investor-day presentation decks (投资者日材料)

This parallel acquisition prevents cascading delays if individual sources are slow to respond, as implemented in lines 34-40 of the skill definition.

### Stage 3: Financial Data Extraction and Cross-Validation ([`financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/financial_rigor.py))

After acquisition, the pipeline parses income-statement, cash-flow, and balance-sheet items, then invokes **[`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py)** to perform cross-validation checks on revenue consistency, market-cap calculations, and valuation metrics. This verification step ensures that extracted figures match across multiple primary sources before entering the analysis phase (lines 78-92).

### Stage 4: Management Tone and MD&A Signal Analysis

The skill performs line-by-line semantic analysis of Management Discussion & Analysis (MD&A) sections and earnings call transcripts, flagging five specific signal types:

- **🟢 坦诚信号 (Candid signals):** Explicit acknowledgment of problems or misses
- **🟢 清晰信号 (Clear signals):** Specific, quantified guidance or explanations
- **🔴 模糊信号 (Vague signals):** Generic confidence statements without specifics
- **🔴 转移信号 (Deflecting signals):** Blaming external factors or avoiding the question
- **🔴 归因外部化 (External attribution):** Excessive blame on macro conditions or competitors

This tonal analysis occurs at lines 98-108 and contributes to the final investment thesis.

### Stage 5: Footnote Mining for Hidden Risks

The pipeline automatically inspects financial statement footnotes for six critical risk categories (必查附注项):

- **关联交易 (Related-party transactions):** Pricing and volume with affiliates
- **股权激励 (Equity incentives):** RSU/option dilution impacts
- **或有负债 (Contingent liabilities):** Off-balance-sheet obligations
- **会计政策变更 (Accounting policy changes):** Revenue recognition or depreciation method shifts
- **分部信息 (Segment splits):** Geographic or product-line performance dispersion
- **客户/供应商集中度 (Customer/supplier concentration):** Revenue dependency risks

This stage, defined at lines 31-38, often uncovers material information absent from headline figures.

### Stage 6: Historical Trend and Guidance Deviation Analysis

The system constructs multi-period comparison tables spanning at least 4 quarters (or 3 annual reports) to contextualize current performance. It specifically compares actual results against prior **management guidance** (管理层指引), recording deviations and accounting for any guidance revisions made during the period (lines 52-66).

### Stage 7: Structured Markdown Report Assembly

Verified data and analytical signals are rendered into a standardized markdown structure containing:

1. **核心数据速览 (Core data snapshot):** Key metrics with YoY and QoQ deltas
2. **本期最重要的 3 个变化 (Top 3 changes):** Material business developments
3. **管理层语气与承诺追踪 (Tone & commitment tracking):** Signal inventory with examples
4. **附注中的隐藏信息 (Hidden signals in notes):** Footnote-derived risks
5. **历史对比与预期差 (Historical variance):** Guidance tracking tables
6. **问答质量 (Q&A quality):** Analyst question depth assessment
7. **结论 (Conclusion):** Investment thesis impact and position recommendation

The report is written to the `reports/` directory with naming convention `{company}-earnings-{period}.md`.

### Stage 8: Automated Audit and Quality Gate ([`report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/report_audit.py))

Before publication, the skill triggers **[`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py)** to execute a three-step verification:

1. **Extract:** Auto-generates a data checklist from the report content
2. **Re-fetch:** Retrieves every cited datum from approved sources listed in [`skills/financial-data.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/financial-data.md) (macrotrends, StockAnalysis, 东方财富, etc.)
3. **Verdict:** Produces a pass/fail determination based on data consistency

Only reports passing this audit gate are finalized, ensuring zero unverified figures reach the output (lines 94-108).

## Invoking the Skill: CLI Usage and Examples

Users interact with the skill through slash commands. The runtime automatically expands `$ARGUMENTS` into the user-provided company ticker and reporting period.

```bash

# Quarterly review with explicit period

/earnings-review 腾讯 2025Q4

```

When invoked, the skill executes all eight stages and outputs a structured analysis:

```markdown

## 一、核心数据速览

| 指标 | 本期 | 上期 | YoY | 管理层指引 | 是否达标 |
|------|------|------|-----|------------|----------|
| 总收入 | 527.3 B | 508.1 B | +3.8% | 530 B | ✅ |
| 毛利润率 | 45.2% | 46.0% | -0.8% | ≥45% | ✅ |

## 四、附注中的隐藏信息

- 关联交易：关联方 Y 关联交易金额 3.2 B，定价高于行业平均 15%
- 股权激励：本期 RSU 发行 1.1 M 股，稀释率 0.6%

## 七、结论

**超预期** – 收入与毛利率基本符合指引，现金流质量提升  
**对持仓** – 维持/适度增持

```

For annual report reviews without specifying a period, the skill auto-detects the most recent filing:

```bash

# Auto-detects latest annual report (年报)

/earnings-review 美团

```

## Core Architecture and File Dependencies

The earnings review skill relies on a specific file ecosystem within `xbtlin/ai-berkshire`:

| File | Purpose |
|------|---------|
| [`skills/earnings-review.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/earnings-review.md) | Primary skill definition containing all 8 stage instructions |
| [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) | Validation engine for revenue, market-cap, and valuation sanity checks |
| [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py) | Quality gate implementation (`extract` and `verdict` commands) |
| [`skills/financial-data.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/financial-data.md) | Canonical source catalog (macrotrends, StockAnalysis, 东方财富) for audit re-fetch |
| [`codex-skills/earnings-review/SKILL.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-skills/earnings-review/SKILL.md) | Codex-compatible wrapper auto-generated from the main skill |
| [`skills/earnings-team.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/earnings-team.md) | Multi-agent orchestrator that can invoke this skill as a sub-task |

## Summary

- The **earnings review skill** is defined in [`skills/earnings-review.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/earnings-review.md) as an 8-stage markdown workflow
- It implements a **material availability rating** (A/B/C) before processing to ensure source quality
- Parallel **Task** agents acquire primary documents while **[`financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/financial_rigor.py)** validates numerical integrity
- Semantic analysis flags five distinct **management tone signals** (candid, clear, vague, deflecting, externalizing)
- Automated **footnote mining** detects related-party risks, dilution, and contingent liabilities
- The **[`report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/report_audit.py)** utility enforces a zero-tolerance data verification gate before publication
- Output follows a strict 7-section markdown template suitable for investment committee review

## Frequently Asked Questions

### How does the earnings review skill verify data accuracy?

The skill employs a two-layer verification system. During **Stage 3**, **[`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py)** cross-validates extracted figures across income statements, cash flows, and balance sheets. During **Stage 8**, **[`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py)** re-fetches every cited data point from approved sources cataloged in [`skills/financial-data.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/financial-data.md) (such as macrotrends and 东方财富), comparing re-fetched values against the report content to ensure zero errors before publication.

### What is the difference between Grade A, B, and C material ratings?

**Grade A (A级)** indicates the system obtained complete original filings and transcripts, enabling deep fundamental analysis. **Grade B (B级)** means only partial originals or third-party summaries were available, limiting footnote analysis. **Grade C (C级)** signifies reliance on news reports only, triggering a caution flag in the final report regarding source reliability.

### Can the earnings review skill analyze companies outside the US?

Yes. The skill supports both SEC filings (10-K/10-Q) and Chinese regulatory filings (年报). The parallel document acquisition stage specifically searches for localized primary materials including Chinese earnings call transcripts and investor-day decks, while [`skills/financial-data.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/financial-data.md) maintains approved data sources for both US and Chinese equities.

### How does the skill detect manipulative management language?

During **Stage 4**, the skill scans MD&A and call transcripts for linguistic patterns associated with opacity. It specifically flags **模糊信号 (vague signals)** like generic confidence statements lacking specifics, **转移信号 (deflecting signals)** where questions are evaded, and **归因外部化 (external attribution)** where management excessively blames macro factors. These signals are tabulated with direct quotes to support the final tone assessment.