How the Earnings-Team Skill Generates WeChat Articles: A Multi-Agent Pipeline for Financial Analysis

The earnings-team skill is a fully-automated multi-agent pipeline that transforms raw earnings data into publication-ready WeChat articles through three sequential phases: parallel research by four specialized master agents, synthesis by a Team Lead, and final publication refinement by an Editor and Reader agent.

The earnings-team skill in the xbtlin/ai-berkshire repository orchestrates a sophisticated workflow that converts corporate earnings reports into polished WeChat official account content. This slash-command skill leverages agent-tool primitives defined in skills/earnings-team.md to execute research, analysis, and editorial tasks concurrently, producing investor-ready articles that balance Warren Buffett-style analytical rigor with mobile-first readability.

Three-Phase Architecture of the Earnings-Team Skill

Phase 1: Parallel Research by Four Master Agents

The pipeline initiates four concurrent agents, each modeled after a specific investment philosophy. 段永平 (Duan Yongping) analyzes business model durability, 巴菲特 (Buffett) audits financial quality using delegated calculations, 芒格 (Munger) evaluates competitive landscapes, and 李录 (Li Lu) hunts for asymmetric risk signals.

Each master retrieves original filings, earnings call transcripts, and shareholder letters, then grades data availability (A-C) according to lines 33-40 of skills/earnings-team.md to determine analysis depth. They emit independent markdown reports (≈2KB each) with color-coded signals (green/amber/red) saved as {company}-earnings-{period}-{master}.md.

Phase 2: Synthesis by the Team Lead

Once all four master reports complete, the Team Lead agent cross-compares findings to surface consensus, flag contradictions, and highlight overlooked "corner" items. As implemented in lines 87-93 of skills/earnings-team.md, the Team Lead assembles a unified research draft ({公司名}-earnings-{期间}-研究底稿.md) containing an executive summary, key changes, scoring table, and a "what would the masters do?" decision matrix.

Phase 3: Publication via Editor and Reader Agents

The Editor (微信公众号编辑) rewrites the research draft into a conversational, short-paragraph layout optimized for WeChat's mobile UI (≈1K-3K words). Simultaneously, the Reader (普通投资者评审) scores the draft on readability, information value, credibility, and actionability, returning concrete "must-fix" and "nice-to-add" suggestions per the template in skills/wechat-article.md (lines 31-53).

The Team Lead applies all mandatory changes, optionally incorporates useful enhancements, and performs final proofreading to produce reports/{company}/{company}-earnings-{period}.md, a file formatted for immediate copying into the WeChat backend.

Key Technical Implementation Details

Parallel Execution: The skill uses /agent start … primitives to launch the four masters simultaneously (lines 55-57 of skills/earnings-team.md), reducing latency by conducting business-model, financial-quality, competitive, and risk analyses concurrently rather than sequentially.

Data-Availability Grading: An A-B-C rating system (lines 33-40) dynamically adjusts analysis depth based on filing completeness and transcript availability, ensuring the pipeline adapts to disclosure quality without over-committing to sparse data.

File-System Contract: The skill writes to a deterministic directory tree under reports/{company}/ (lines 99-108), creating intermediate artifacts including master analyses, research drafts, and reader reviews before emitting the final publishable markdown.

Financial Validation: Calculations are delegated to financial_rigor.py, which cross-validates revenue, cash-flow, and valuation metrics (lines 110-114) before synthesis begins, ensuring the Buffett agent's financial audit is mathematically rigorous.

Running the Earnings-Team Skill

CLI Invocation:

# Analyze Tencent Q4 2025 earnings and generate WeChat article

/clerk /earnings-team "腾讯 2025Q4"

Programmatic Execution:

import subprocess

def run_earnings_team(company, period):
    arg = f"{company} {period}"
    subprocess.run(["clerk", "/earnings-team", arg], check=True)

run_earnings_team("腾讯", "2025Q4")

Generated Directory Structure:

reports/腾讯/
├── 腾讯-earnings-2025Q4.md                # Final WeChat article (publishable)

├── 腾讯-earnings-2025Q4-研究底稿.md        # Unified research draft

├── 腾讯-earnings-2025Q4-段永平.md          # Business-model analysis

├── 腾讯-earnings-2025Q4-巴菲特.md          # Financial-quality audit

├── 腾讯-earnings-2025Q4-芒格.md            # Competitive-landscape review

├── 腾讯-earnings-2025Q4-李录.md            # Risk-signal investigation

└── 腾讯-earnings-2025Q4-读者评审.md        # Reader-agent review report

Quality Assurance and Audit

Before publication, the Team Lead executes report_audit.py to verify data consistency and markdown structure. Run the audit utilities to validate the final article:

# Extract and verify financial data consistency

python3 ~/ai-berkshire/tools/report_audit.py extract \
   --report reports/腾讯/腾讯-earnings-2025Q4.md

# Generate final verdict

python3 ~/ai-berkshire/tools/report_audit.py verdict \
   --results results.json \
   --report reports/腾讯/腾讯-earnings-2025Q4.md

A PASS verdict indicates the article meets publishing standards and is ready for the WeChat official account backend.

Summary

• The earnings-team skill operates through three distinct phases: parallel research, synthesis, and publication, orchestrated by a single slash-command invocation.
• Four specialized master agents conduct concurrent analyses of business models, financial quality, competitive position, and risk factors, each producing independent markdown reports.
• The Team Lead orchestrates synthesis and publication through Editor and Reader agents, ensuring content balances analytical depth with WeChat-optimized readability.
• All outputs follow a deterministic file-system contract under reports/{company}/, with final validation performed by financial_rigor.py and report_audit.py to guarantee data consistency.

Frequently Asked Questions

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

The earnings-team skill is a slash-command automation defined in skills/earnings-team.md that transforms raw corporate earnings data into publication-ready WeChat articles. It implements a multi-agent workflow where four investment-masters analyze financial data in parallel before an Editor and Reader refine the content for public distribution on WeChat official accounts.

How do the four master agents differ in their analysis?

Each master applies a distinct analytical framework derived from their namesake investor: 段永平 evaluates business model durability and management quality, 巴菲特 audits financial statements for accounting rigor and moat width, 芒格 assesses competitive dynamics and industry structure, and 李录 identifies red flags and asymmetric downside risks. They operate independently but concurrently, producing separate markdown reports that the Team Lead synthesizes into a unified view.

Why does the pipeline include a Reader agent?

The Reader agent (普通投资者评审) acts as a quality gate by scoring the drafted article on readability, credibility, and actionability from a non-technical retail investor's perspective. This "author-editor-reader" loop, defined in skills/wechat-article.md, ensures the final output communicates complex financial insights without requiring professional accounting knowledge, optimizing for the WeChat mobile reading experience.

How can I verify the accuracy of the generated WeChat article?

Run the report_audit.py utility located in tools/ to perform automated consistency checks on financial figures and markdown structure. Additionally, the Buffett agent invokes financial_rigor.py during the research phase to cross-validate revenue, cash-flow, and valuation calculations before synthesis begins, creating a double-validation layer for all quantitative claims.

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