How the AI Berkshire /investment-team Skill Manages Multi-Agent Parallel Research with Distinct Perspectives

The AI Berkshire /investment-team skill orchestrates multi-agent parallel research by deploying four specialized sub-agents simultaneously—each embodying a distinct investment philosophy—while enforcing rigorous data-verification standards and aggregating outputs into a unified investor dossier.

The xbtlin/ai-berkshire repository implements this architecture in skills/investment-team.md, leveraging Claude Code’s Team and Task primitives to simulate a four-person research team with complementary analytical frameworks. This design enables true parallelism where each agent investigates the target company through a specific lens without sequential bottlenecks.

Team Composition: Four Distinct Investment Perspectives

According to the source code in skills/investment-team.md (lines 11-18), the skill initializes a structured research team comprising five distinct roles. The team-lead (user) coordinates the effort, while four AI agents adopt specific investment philosophies:

• Business Analyst: Embodies the 段永平 (Duan Yongping) view, focusing on business model quality and competitive moats.
• Financial Analyst: Adopts the 巴菲特 (Warren Buffett) perspective, concentrating on financial metrics and intrinsic valuation.
• Industry Researcher: Channels the 芒格 (Charlie Munger) approach, emphasizing industry dynamics and competitive landscape.
• Risk Assessor: Applies the 李录 (Li Lu) framework, evaluating management integrity and downside risks.

This multi-agent parallel research structure ensures that no single analytical bias dominates the final output.

Pre-Execution: AI-Bias Rating and Team Initialization

Before spawning agents, the skill evaluates the AI researchability of the target company, assigning an A-B-C rating that measures information richness and potential AI bias (lines 21-30). This rating propagates to all sub-agents via the task descriptions, allowing each to adjust its confidence level and research depth accordingly.

The orchestration begins with a TeamCreate call (lines 34-40), which instantiates the team with a company-specific identifier and assigns the user as the team-lead.

Parallel Task Distribution and Execution

The core of the multi-agent parallel research capability lies in four simultaneous TaskCreate operations defined in lines 40-92. Each task targets a dedicated research domain:

1. Business Model Analysis: Moat durability and competitive advantages.
2. Finance & Valuation: Financial statements and intrinsic value calculations.
3. Industry & Competition: Market positioning and secular trends.
4. Risk & Management: Governance quality and downside scenario modeling.

Rather than executing sequentially, the skill launches all four agents in a single message using the Task tool with parameters subagent_type: general-purpose and run_in_background: true (lines 94-104). Each agent receives a prompt template injecting the company name, specific role identifier, and tailored task description containing perspective-specific analytical frameworks.

Data Verification and Cross-Validation

To maintain rigorous standards across parallel streams, each agent must adhere to a strict data-sourcing contract. The skill mandates two independent sources for all financial data—for example, cross-referencing macrotrends against stockanalysis for US equities.

Additionally, agents invoke the financial_rigor.py utility (lines 64-68) to verify market-cap calculations, valuation multiples, and scenario modeling. This ensures that despite operating independently, all four perspectives rely on consistently validated inputs defined in skills/financial-data.md.

Aggregation and Final Synthesis

While sub-agents execute in parallel, the team-lead monitors progress through a live tracking table (lines 30-34). Upon completion of all four reports, the lead sends shutdown_request signals to each sub-agent and compiles the final deliverable.

The synthesized report includes:

• Executive summary integrating all four perspectives.
• Scoring table with weighted metrics.
• Data snapshot with verified sources.
• Pros/cons matrix.
• Investment checklist.
• Final recommendation.

The completed dossier is written to a dated markdown file, processed through tools/report_audit.py for random-sample verification, and the entire team structure is dismantled via TeamDelete (lines 84-102).

Invoking the Investment Team

To trigger the multi-agent parallel research workflow from Claude, use the following command syntax:

# Example: invoking the investment‑team skill from Claude

/investment-team 公司名="腾讯" 日期=20260630

For programmatic integration, construct a payload targeting the Claude API:

import json, subprocess

# Build the skill invocation payload

payload = {
    "skill": "/investment-team",
    "arguments": {
        "公司名": "腾讯",
        "日期": "20260630"
    }
}

# Send to Claude (the surrounding platform handles the HTTP call)

response = subprocess.check_output(
    ["curl", "-X", "POST", "-d", json.dumps(payload),
     "https://api.anthropic.com/v1/complete"], text=True)

print("Claude response:", response)

Summary

• The /investment-team skill creates four specialized agents (Business, Financial, Industry, Risk) plus a human team-lead, each adopting distinct investment philosophies (段永平, 巴菲特, 芒格, 李录).
• Parallel execution is achieved through run_in_background: true and subagent_type: general-purpose flags in the Task tool, launching all research streams simultaneously.
• An AI-bias rating (A-B-C) calibrates agent confidence before research begins, ensuring appropriate skepticism for information-sparse targets.
• Data rigor is enforced via mandatory dual-source verification and the financial_rigor.py tool for financial metrics.
• The team-lead aggregates outputs into a structured investment dossier, validates it through report_audit.py, and cleans up resources with TeamDelete.

Frequently Asked Questions

What are the specific analytical perspectives assigned to each agent in the AI Berkshire investment team?

The skill assigns four distinct value-investment philosophies: the Business Analyst adopts the 段永平 (Duan Yongping) focus on business quality and moats; the Financial Analyst applies the 巴菲特 (Buffett) emphasis on financial metrics and intrinsic value; the Industry Researcher channels the 芒格 (Munger) perspective on competitive dynamics and industry trends; and the Risk Assessor utilizes the 李录 (Li Lu) framework for management evaluation and risk identification.

How does the skill ensure data accuracy when agents operate in parallel?

Each agent must verify findings against two independent data sources (such as macrotrends and stockanalysis for US equities) and invoke the tools/financial_rigor.py script for market-cap and valuation cross-checks. This shared data-quality contract ensures consistency across all parallel research streams despite their simultaneous execution.

Can the /investment-team skill handle private companies or limited data scenarios?

Yes. The skill implements an AI researchability rating (A-B-C) that evaluates information richness before team creation. For private companies or data-scarce targets, this rating signals agents to lower confidence thresholds and adjust research depth, while skills/private-company-research.md demonstrates a similar team-based pattern adapted for non-public entities.

What happens if one parallel agent fails or returns incomplete data?

The team-lead monitors progress through a live tracking table and can issue shutdown_request signals to individual agents. If an agent fails, the lead can re-dispatch the specific TaskCreate call for that perspective without restarting the entire workflow, ensuring robustness in the multi-agent parallel research pipeline.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}