What Is the earnings-review Skill? A Primary-Source Analysis Tool for Financial Filings

The earnings-review skill is a Claude-Code slash-command that performs deep, primary-source-driven analysis of company earnings filings (10-K/10-Q, annual reports, transcripts) by reading original documents directly rather than relying on filtered second-hand summaries.

In the xbtlin/ai-berkshire repository, this skill implements a rigorous four-stage pipeline designed to mirror the due diligence approach of value investors like Warren Buffett and Li Lu. Unlike conventional research workflows that aggregate analyst reports and news headlines, the earnings-review skill enforces strict data provenance and validation standards.

Architecture of the earnings-review Skill

The skill is defined in skills/earnings-review.md and executes through a structured pipeline that prioritizes original document authenticity over intermediary interpretations.

Stage 1: Data Availability Classification (资料可得性评级)

The workflow begins by classifying source availability into A/B/C levels. This rating determines analysis depth and confidence bounds.

• A-level: Full original filings available (10-K/10-Q, complete transcripts)
• B-level: Partial documents or delayed SEC EDGAR access
• C-level: Reliance on secondary aggregation only

According to the source code, B-level analyses trigger explicit annotations marking derived numbers as "非原始来源" (non-primary source) 【source】.

Stage 2: Primary Source Acquisition (获取一手资料)

The skill launches parallel background agents via the Task tool to fetch raw reports from:

• SEC EDGAR (US filings)
• HKEX (Hong Kong Exchange)
• 巨潮资讯 (CNINFO for Chinese A-shares)
• Company IR websites directly

When full text is unavailable, the skill falls back to structured data merging defined in skills/financial-data.md, maintaining lineage tracking for all data points 【source】.

Stage 3: Financial Data Extraction and Validation (核心财务数据提取与验证)

This stage extracts income statement, cash flow, and balance sheet metrics, then runs cross-validation using tools/financial_rigor.py. The validation ensures:

• Revenue figures match between filings and investor presentations
• Market cap calculations verify against exchange data
• Non-GAAP reconciliations check against standard adjustments

The cross-validate and verify-market-cap functions in tools/financial_rigor.py provide the data integrity checks required before analysis proceeds 【source】.

Stage 4: MD&A Deep Reading and Tone Analysis (管理层讨论精读)

The final stage analyzes Management Discussion & Analysis (MD&A) sections for qualitative signals:

• Tone cues: 坦诚 (frank), 清晰 (clear), 模糊 (vague), 转移 (deflection), 归因外部化 (external attribution)
• Promise tracking: Compares forward guidance against actual outcomes
• Risk surfacing: Flags related-party transactions, contingent liabilities, and accounting policy changes

This stage generates the judgment-focused insights that differentiate primary-source research from quantitative screening 【source】.

earnings-review vs. Secondary Research: Key Differences

The fundamental distinction lies in information provenance and processing depth.

Secondary research aggregates filtered information—analyst summaries, news headlines, and data-site snapshots. These sources introduce delays, editorial bias, and missing contextual nuance.

The earnings-review skill explicitly rejects second-hand information. As noted in the design documentation at lines 13-19 of skills/earnings-review.md, the workflow insists on reading primary documents directly, eliminating the "telephone game" of financial interpretation 【source】.

Key differentiators include:

• Source fidelity: Starts from original filings rather than intermediary summaries
• Data validation: Requires every key number to validate against at least two independent sources
• Narrative analysis: Evaluates management tone and commitment, not just disclosed metrics
• Output format: Produces concise, thesis-focused reports answering whether earnings beat expectations and how they impact the investment case

How to Use the earnings-review Skill

The skill exposes as a slash-command within Claude-Code environments configured for the ai-berkshire project.

Fetch a specific quarterly filing:

/earnings-review 腾讯 2025Q4

Automatically select the most recent filing:

/earnings-review 美团 最新

Handle incomplete source material with B-level classification:

/earnings-review 拼多多 2024年报

When invoked, the command triggers the four-stage pipeline, returning a structured report that includes validated financial tables, MD&A sentiment analysis, and an investment thesis impact assessment.

Implementation Files and Dependencies

The earnings-review skill relies on specific files within the repository:

File	Role
skills/earnings-review.md	Full specification including the four-stage execution pipeline and validation steps
skills/financial-data.md	Fallback data aggregation strategy when original filings are missing
tools/financial_rigor.py	Cross-validation commands (cross-validate, verify-market-cap) for data integrity
tools/report_audit.py	Final "准出" (quality gate) audit before report publication
codex-prompts/earnings-review.md	Auto-generated Claude-Code prompt mapping markdown specs to runnable commands

These components implement a primary-source-first workflow that stands apart from secondary research approaches.

Summary

• The earnings-review skill is a Claude-Code slash-command for primary-source financial analysis defined in xbtlin/ai-berkshire.
• It executes a four-stage pipeline: data availability classification, primary source acquisition, financial validation using tools/financial_rigor.py, and MD&A tone analysis.
• Unlike secondary research, it rejects filtered information and reads original 10-K/10-Q filings and transcripts directly.
• Usage follows the pattern /earnings-review [company] [quarter/年份/最新] with automatic fallback handling for incomplete sources.

Frequently Asked Questions

What is the earnings-review skill in ai-berkshire?

The earnings-review skill is a specialized Claude-Code command defined in skills/earnings-review.md that automates deep analysis of company earnings filings. It fetches original 10-K, 10-Q, and annual report documents, validates financial data against multiple sources, and analyzes management tone to produce investment-grade research reports.

How does earnings-review differ from using financial data websites?

While financial data sites provide aggregated metrics and analyst summaries, the earnings-review skill reads primary documents directly from SEC EDGAR, HKEX, and company IR sites. It validates every key number through tools/financial_rigor.py and analyzes Management Discussion & Analysis sections for qualitative signals like tone and commitment tracking—capabilities unavailable in standard data terminals.

What happens when original filings are unavailable for earnings-review?

When primary sources are missing, the skill marks the analysis as B-level availability and annotates derived figures as non-primary sources. It then falls back to structured data aggregation defined in skills/financial-data.md, maintaining transparency about data lineage while still providing analytical value.

Which tools support the earnings-review validation process?

The validation relies on tools/financial_rigor.py for cross-referencing financial metrics and tools/report_audit.py for final quality assurance. These Python modules provide the cross-validate and verify-market-cap functions that ensure data integrity before the final report generation stage.

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