How the AI Berkshire Earnings Review Skill Works: Complete Technical Breakdown

The Earnings Review skill is a single‑agent workflow defined in skills/earnings‑review.md that executes an eight‑stage pipeline to produce deep‑read earnings reports from primary source documents, including automated data validation and management tone analysis.

The Earnings Review skill in the AI Berkshire repository transforms raw SEC filings and earnings materials into structured investment research through a reproducible, markdown‑driven pipeline. Unlike generic financial analysis tools, this skill enforces a "read the original filing" philosophy by parallelizing document acquisition, cross‑validating figures against canonical sources, and auditing every datum before publication.

Eight-Stage Workflow Architecture

The skill operates as a sequential workflow where each stage is implemented as plain‑text instructions in the skill definition file. When invoked via slash‑command (e.g., /earnings‑review 腾讯 2025Q4), the AI Berkshire runtime expands $ARGUMENTS and executes the following stages:

Stage 1: Pre‑Screening for Material Availability

Before processing begins, the skill classifies source availability into Grade A, B, or C according to skills/earnings‑review.md (lines 26‑31). This rating determines how much original documentation can be used versus reliance on third‑party summaries.

• Grade A: Full original filing obtained (10‑K/10‑Q/年报)
• Grade B: Partial original text or third‑party aggregation only
• Grade C: News reports and data‑site abstracts only

Stage 2: Parallel Document Acquisition

The skill fires parallel Task agents to download four categories of primary materials simultaneously (lines 34‑40):

1. The official filing itself (10‑K/10‑Q/annual report)
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)

Stage 3: Financial Extraction and Cross‑Validation

Core financial data is parsed from income statements, cash‑flow statements, and balance sheets. The skill then invokes tools/financial_rigor.py (lines 78‑92) to perform cross‑validation checks on revenue, market capitalization, and valuation metrics against approved sources listed in skills/financial-data.md.

Stage 4: Management Tone and MD&A Analysis

The skill conducts line‑by‑line reading of Management Discussion & Analysis (MD&A) sections and earnings call transcripts, flagging five specific signal types (lines 98‑108):

• 🟢 坦诚信号 (Candor signals) – Direct acknowledgment of challenges
• 🔵 清晰信号 (Clarity signals) – Precise, quantified guidance
• 🟡 模糊信号 (Vagueness signals) – Non‑specific optimistic language
• 🟠 转移信号 (Deflection signals) – Shifting blame or topic
• 🔴 归因外部化 (Externalization signals) – Attributing results solely to macro factors

Stage 5: Footnote and Hidden Signal Detection

A dedicated "note‑digging" phase scans financial statement footnotes for six critical risk categories (lines 31‑38):

• Related‑party transactions (关联交易)
• Equity incentive dilution (股权激励)
• Contingent liabilities (或有负债)
• Accounting policy changes (会计政策变更)
• Segment information splits (分部信息)
• Customer/supplier concentration (客户/供应商集中度)

Stage 6: Historical Context and Guidance Comparison

The skill constructs multi‑period tables placing current metrics within at least four quarters or three annual reports of historical context (lines 52‑66). It compares actual results against prior management guidance and records deviations for trend analysis.

Stage 7: Structured Report Generation

Output is rendered as a structured markdown document with seven mandatory sections (lines 70‑80):

1. 核心数据速览 (Core data snapshot)
2. 本期最重要的 3 个变化 (Top 3 changes this period)
3. 管理层语气与承诺追踪 (Management tone & commitment tracking)
4. 附注中的隐藏信息 (Hidden information in notes)
5. Q&A 关键问题 (Key Q&A highlights)
6. 对投资主题的影响 (Impact on investment thesis)
7. 结论 (Conclusion with hold/add/reduce recommendation)

Stage 8: Automated Audit and Quality Gate

Before publication, tools/report_audit.py executes a final "准出" (exit) audit (lines 94‑108). This tool:

• Extracts a data checklist from the generated report
• Re‑fetches every datum from canonical sources in skills/financial-data.md
• Issues a pass/fail verdict; only passing reports are written to disk

Key Implementation Files and Tools

The Earnings Review skill relies on a specific file architecture within the AI Berkshire repository:

File	Purpose
skills/earnings‑review.md	Master skill definition containing all eight workflow stages
tools/financial_rigor.py	Validation utility for revenue, market‑cap, and valuation cross‑checks
tools/report_audit.py	Automated quality gate that re‑fetches and verifies every reported figure
skills/financial-data.md	Canonical catalogue of approved data sources (macrotrends, StockAnalysis, 东方财富)
skills/earnings‑team.md	Multi‑agent orchestrator that can invoke the single‑agent Earnings Review as a sub‑task

Practical Usage and Output Format

Users interact with the skill through natural slash commands. The runtime automatically parses company names and fiscal periods from $ARGUMENTS.

Quarterly analysis with explicit period:

/earnings-review 腾讯 2025Q4

Annual report auto‑detection (defaults to most recent filing):

/earnings-review 美团

Expected Output Structure

The generated markdown report follows a standardized template. Below is a truncated example showing the data tables and signal classifications:

## 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% | ✅ |
| 经营现金流 / 净利润 | 112% | 98% | +14% | >100% | ⚠️ |

## 二、本期最重要的 3 个变化

1. 毛利润率回落 0.8%——管理层归因于 X 业务的高额研发开支。
2. 资本支出 ↑ 22% → 新增 5 G 基站。
3. 关联交易：与关联方 Y 的采购价格上调 12%。

## 三、管理层语气与承诺追踪

| 信号 | 示例 | 评估 |
|------|------|------|
| 🟢 坦诚信号 | "本季度利润率下降主要因为我们在 X 领域的投入超出预期" | 正向 |
| 🔴 模糊信号 | "我们对未来充满信心" | 负向 |

## 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。
**下一个催化剂** – 8‑月新一代芯片发布。

Reports are written to reports/{company}-earnings-{period}.md upon passing the audit gate.

Summary

• The Earnings Review skill is defined entirely in skills/earnings‑review.md as a reproducible, eight‑stage markdown workflow.
• It enforces primary‑source research by parallelizing acquisition of filings, transcripts, letters, and decks, then rating material availability as Grade A, B, or C.
• Financial rigor is ensured through tools/financial_rigor.py validation and a final audit via tools/report_audit.py against canonical sources in skills/financial-data.md.
• Management communications are analyzed for five specific behavioral signals (candor, clarity, vagueness, deflection, externalization) to assess execution risk.
• The skill outputs a structured seven‑section markdown report suitable for direct inclusion in investment memos.

Frequently Asked Questions

What file defines the Earnings Review skill workflow?

The complete workflow is defined in skills/earnings‑review.md at the repository root. This markdown file contains the eight‑stage instruction set interpreted by the AI Berkshire runtime, including parallel Task agent invocations, signal classification rules, and report templates.

How does the skill validate financial data accuracy?

During Stage 3, the skill calls tools/financial_rigor.py to cross‑validate extracted figures. In Stage 8, tools/report_audit.py extracts a checklist of every numerical claim, re‑fetches each value from approved sources catalogued in skills/financial-data.md, and issues a binary pass/fail verdict before the report is published.

What are the "signal" categories used in management tone analysis?

The skill identifies five signals in MD&A and earnings call transcripts: 坦诚信号 (candor), 清晰信号 (clarity), 模糊信号 (vagueness), 转移信号 (deflection), and 归因外部化 (externalization). These are tagged line‑by‑line to flag execution risk or governance concerns that raw financial metrics might obscure.

Can the Earnings Review skill be used as part of a multi-agent workflow?

Yes. While the skill functions as a single‑agent workflow for standard requests, skills/earnings‑team.md acts as a higher‑level orchestrator that can invoke the Earnings Review skill as a sub‑task within larger multi‑agent deep‑dives, allowing coordinated analysis across multiple companies or fiscal periods.

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