How to Use the Investment-Team Skill in AI-Berkshire: Multi-Agent Research Workflow

The investment-team skill orchestrates a parallel multi-agent research workflow that mirrors the analysis frameworks of Buffett, Munger, Duan Yongping, and Li Lu to generate audited investment reports.

The investment-team skill in the xbtlin/ai-berkshire repository automates institutional-grade equity research by deploying four specialized AI agents simultaneously. According to the source code in skills/investment-team.md, this skill strings together Team, Task, TaskUpdate, SendMessage, and TeamDelete primitives while enforcing strict data integrity through the financial-rigor and report-audit utilities.

Prerequisites: WebSearch Permission Verification

Before deploying the research team, the skill validates that the WebSearch permission is enabled in your environment. As implemented in lines 34‑48 of skills/investment-team.md, the workflow aborts immediately if this permission is missing, instructing the user to enable it before proceeding. This requirement ensures all agents can access real-time market data rather than relying on stale training data.

Setting Up the Investment Research Team

Creating the Team Structure

The skill initializes the workflow by invoking the TeamCreate tool to instantiate a new team named {company}-research (e.g., tencent-research), assigning the team-lead agent as the coordinator (lines 49‑54). This lead agent serves as the orchestrator for the entire analysis pipeline, managing task distribution and final report compilation.

Deploying the Four Analyst Agents

The core functionality spawns four independent research tasks via TaskCreate calls, each configured with role-specific prompts and parallel execution flags.

Business Analyst: Business Model & Moat

The business-analyst agent receives a task definition covering business model analysis and competitive moat evaluation (lines 59‑68). This agent applies the investment philosophy of Buffett and Munger to assess qualitative factors like pricing power and market positioning.

Financial Analyst: Statements & Valuation

The financial-analyst agent processes financial statement analysis and valuation metrics (lines 70‑84). This task requires dual-source financial data validated through tools/financial_rigor.py to ensure accuracy in market-cap calculations and valuation multiples.

Industry Researcher: Landscape & Competition

The industry-researcher agent examines industry structure, competitive dynamics, and secular trends (lines 86‑96). This role incorporates Duan Yongping’s framework for understanding industry evolution and competitive advantage durability.

Risk Assessor: Risk & Management Review

The risk-assessor agent evaluates management quality, governance risks, and operational vulnerabilities (lines 97‑107). This analysis follows Li Lu’s rigorous standards for assessing downside risks and management integrity.

Parallel Execution and Data Integrity

The four agents launch simultaneously in one message with run_in_background: true (lines 11‑16 of the prompt template). Each agent receives a role-specific prompt (lines 21‑30) that enforces three critical constraints:

• Mandatory WebSearch: Agents must use live web searches for the latest data rather than cached knowledge.
• Dual-Source Validation: Financial metrics must pass validation via tools/financial_rigor.py (lines 30‑34), cross-referencing multiple data providers.
• Anti-Hallucination Protocol: Explicit prohibition against "pretending" to have data when web access is blocked (lines 34‑35).

Monitoring Progress and Aggregation

The team-lead agent monitors task completion through a live progress table (lines 48‑50) and aggregates key findings from each sub-agent as they complete (lines 46‑49). This real-time visibility allows users to track which analysts have finished their deep dives while others are still processing.

Final Report Generation and Auditing

After all sub-agents submit their analyses, the workflow executes a structured teardown and deliverable pipeline:

1. Report Compilation: The lead agent compiles a master markdown report and writes it to ~/[公司名]投资研究报告_YYYYMMDD.md (line 97), using the company name and current date in the filename.
2. Team Teardown: The skill invokes TeamDelete to remove the temporary {company}-research team (lines 16‑18), cleaning up resources.
3. Quality Audit: The lead runs the three-step data-audit pipeline via tools/report_audit.py (lines 99‑112), which extracts random samples of data points, validates them against sources, and renders a 准出 (approval) or 打回 (rejection) decision.

Code Examples

Slash Command Invocation

The simplest way to trigger the skill is through the slash command interface:

/investment-team 腾讯

The argument (腾讯) is substituted for $ARGUMENTS and drives the team name (tencent-research).

Python SDK Implementation

For programmatic access, use the Python SDK if installed:

from ai_berkshire import invoke_skill

result = invoke_skill(
    skill_name="investment-team",
    arguments="microsoft"
)
print(result)   # Returns the final aggregated research report

Manual Workflow Reproduction (Debugging)

You can manually reproduce the workflow for debugging or customization:

# 1️⃣ Check WebSearch permission

grep -l '"WebSearch"' .claude/settings.local.json ~/.claude/settings.local.json

# 2️⃣ Create the team

claude tool TeamCreate --team_name=microsoft-research --agent_type=team-lead

# 3️⃣ Launch the four research tasks in parallel (example for business analyst)

claude tool TaskCreate --subject="分析Microsoft商业模式…" \
    --description="…" --activeForm=… --subagent_type=general-purpose \
    --run_in_background=true --team_name=microsoft-research --name=business-analyst

# Repeat for financial-analyst, industry-researcher, and risk-assessor

Key Implementation Files

• skills/investment-team.md: Core skill definition containing the seven-step workflow and permission checks.
• codex-prompts/investment-team.md: Wrapper configuration for Codex slash-command invocation.
• tools/financial_rigor.py: CLI utility enforcing market-cap, valuation, and cross-validation checks for dual-source data requirements.
• tools/report_audit.py: Auditing pipeline that extracts random data samples, validates accuracy, and determines report approval status.
• skills/financial-data.md: Specification document defining the dual-source data standards referenced throughout the workflow.

Summary

• The investment-team skill requires WebSearch permission and aborts if unavailable (lines 34‑48 of skills/investment-team.md).
• It creates a temporary team named {company}-research using TeamCreate and assigns a team-lead agent to coordinate four specialized analysts.
• Four parallel tasks (business-analyst, financial-analyst, industry-researcher, risk-assessor) execute simultaneously with run_in_background: true.
• Data integrity is enforced through tools/financial_rigor.py validation and explicit prohibitions against data hallucination.
• Final deliverables include a timestamped markdown report at ~/[公司名]投资研究报告_YYYYMMDD.md and a three-step audit via tools/report_audit.py to ensure 准出 quality standards.

Frequently Asked Questions

What permissions are required to run the investment-team skill?

The skill strictly requires the WebSearch permission. According to lines 34‑48 of skills/investment-team.md, the workflow checks for this permission before creating the team and aborts immediately if it is missing, instructing the user to enable it. This ensures all agents can access real-time financial data rather than generating unverified responses.

How does the skill ensure data accuracy and prevent hallucinations?

The skill implements a dual-layer validation system. First, each agent must use WebSearch for current data (lines 21‑30). Second, financial data must pass validation through tools/financial_rigor.py (lines 30‑34). Additionally, the prompt template explicitly prohibits agents from "pretending" to have data when web access is unavailable (lines 34‑35), effectively eliminating hallucinated financial metrics.

Can I customize the four analyst roles or add additional agents?

While the current implementation in skills/investment-team.md hardcodes the four roles (business-analyst, financial-analyst, industry-researcher, risk-assessor) to mirror Buffett, Munger, Duan Yongping, and Li Lu’s frameworks (lines 59‑107), you can modify the skill definition file to adjust task descriptions or add additional TaskCreate calls. The parallel execution framework supports any number of background tasks via run_in_background: true.

What is the 准出 (approval) standard in the audit pipeline?

The 准出 (approval) standard refers to the three-step validation pipeline defined in tools/report_audit.py (lines 99‑112 of the skill). This pipeline extracts a random sample of data points from the compiled report, validates them against primary sources, and determines whether the report meets publication quality standards. Reports failing validation are 打回 (rejected) for revision, ensuring only rigorously verified research reaches final output.

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