# How to Use the /investment-team Skill for Rapid Investment Analysis

> Quickly generate investment research reports with the /investment-team skill. This AI tool analyzes companies in minutes, saving you valuable time.

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: how-to-guide
- Published: 2026-07-29

---

**The `/investment-team` skill orchestrates four parallel AI agents to transform a single company name into a fully sourced investment research report in minutes.**

The `xbtlin/ai-berkshire` repository implements this skill as a structured multi-agent workflow defined in [`skills/investment-team.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-team.md). By leveraging parallel background execution and automated data validation, the `/investment-team` skill eliminates the sequential bottlenecks of traditional investment analysis while maintaining rigorous source verification.

## How the Multi-Agent Workflow Works

The skill implements a full-stack research framework through coordinated agent orchestration. According to the source code in [`skills/investment-team.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-team.md), the workflow proceeds through seven distinct phases:

### Team Orchestration and Structure

The skill initializes a **team-lead** agent that coordinates four specialized sub-agents: `business-analyst`, `financial-analyst`, `industry-researcher`, and `risk-assessor`. Each agent receives distinct mandates covering financial trends, valuation metrics, industry dynamics, and risk assessment respectively.

### Pre-Check Validation

Before launching background agents, the skill verifies that the `WebSearch` permission is enabled (lines 34-48 of [`skills/investment-team.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-team.md)). This prevents silent degradation to training-data-only answers and ensures real-time data accessibility.

### Parallel Task Creation

The team-lead creates four simultaneous tasks using the `TaskCreate` tool with `run_in_background:true` (lines 55-84 and 109-118). Each task prompt includes:

- A localized subject line (e.g., "分析{公司名}财务数据、盈利能力与估值")
- Structured deliverable descriptions
- Mandatory `WebSearch` requirements
- Dual-source financial data verification protocols

### Execution and Monitoring

Agents execute simultaneously, reporting progress via `SendMessage`. The team-lead displays a live progress table and waits for all four analyses to complete before proceeding.

### Report Synthesis

Once all sub-agents return, the team-lead merges outputs into a single markdown document (lines 158-188) containing:

- A one-sentence investment thesis
- A 4-dimension star rating table
- Core data tables with source citations
- Bull vs. Bear arguments
- A Buffett-style investment checklist
- Suggested price ranges

### Final Audit and Cleanup

The completed report passes through [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py) for random-sample data validation (lines 199-215). After audit completion, the skill executes `TeamDelete` to tear down the agent workspace.

## Invoking the /investment-team Skill

You can trigger this rapid analysis pipeline through multiple interfaces depending on your environment.

### Claude Code Slash Command

For quick interactive use within Claude Code:

```text
/investment-team Apple

```

Replace `Apple` with your target company name or ticker symbol. The skill prompts for confirmation, runs the pre-check, and automatically initiates the full research workflow.

### Direct API Integration

Send a JSON payload to the skill endpoint:

```json
{
  "skill": "investment-team",
  "arguments": "Tesla"
}

```

The response stream delivers real-time progress updates and concludes with the assembled markdown report.

### Command-Line Orchestration

For automation or scripting environments:

```bash

# Synchronize Codex artefacts

python3 scripts/sync-codex-skills.py

# Trigger via Claude-CLI wrapper

claude --skill investment-team "Alibaba"

```

CLI execution outputs progress messages to stdout and saves the final report to `~/Alibaba投资研究报告_YYYYMMDD.md` as defined in the skill configuration.

### Direct Tool Access for Debugging

To reproduce individual sub-tasks or verify calculations manually:

```bash

# Verify valuation metrics directly

python3 tools/financial_rigor.py verify-valuation \
  --price 150 --eps 5.2 --bvps 30

# Audit an existing report

python3 tools/report_audit.py extract \
  --report ~/Alibaba投资研究报告_20260729.md

# Generate audit verdict

python3 tools/report_audit.py verdict \
  --results audit_results.json \
  --report ~/Alibaba投资研究报告_20260729.md

```

## Key Source Files

The `/investment-team` skill relies on the following components in the `xbtlin/ai-berkshire` repository:

- **[`skills/investment-team.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-team.md)** – Complete skill definition including team structure, task prompts, and synthesis logic.
- **[`codex-prompts/investment-team.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-prompts/investment-team.md)** – Entry-point prompt enabling the slash command interface for Codex users.
- **[`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py)** – Quantitative verification utilities referenced by the financial analyst agent.
- **[`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py)** – Random-sample validation system for final report verification.
- **[`scripts/sync-codex-skills.py`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/sync-codex-skills.py)** – Build script that regenerates Codex artefacts when source markdown changes.

## Summary

- The **`/investment-team`** skill deploys four specialized agents (**business-analyst**, **financial-analyst**, **industry-researcher**, **risk-assessor**) in parallel using `TaskCreate` with `run_in_background:true`.
- A **pre-check** validates `WebSearch` availability (lines 34-48) before execution to ensure data freshness.
- The workflow synthesizes sub-agent outputs into a comprehensive markdown report including star ratings, thesis statements, and valuation ranges (lines 158-188).
- **Automated auditing** via [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py) (lines 199-215) validates random data samples before workspace cleanup.
- Reports can be generated via **slash commands**, **API calls**, or **CLI scripts**, with outputs automatically saved to the filesystem.

## Frequently Asked Questions

### How long does the /investment-team skill take to generate a report?

Because the four sub-agents execute in parallel rather than sequentially, the entire workflow typically completes in minutes rather than hours. The exact duration depends on `WebSearch` latency and the complexity of the company's financial disclosures, but the parallel architecture eliminates the linear time accumulation of traditional research processes.

### What happens if WebSearch is disabled when running the skill?

The skill implements a hard pre-check (lines 34-48 of [`skills/investment-team.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-team.md)) that halts execution if `WebSearch` permissions are unavailable. This prevents the agents from generating reports based solely on training data, ensuring all outputs contain current, sourced financial information.

### Can I customize the analysis prompts or agent roles in the investment-team skill?

The agent definitions and task prompts are hardcoded in [`skills/investment-team.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-team.md) (lines 55-84). To modify the research framework, you must edit this source file and regenerate the Codex artefacts using [`scripts/sync-codex-skills.py`](https://github.com/xbtlin/ai-berkshire/blob/main/scripts/sync-codex-skills.py). The skill does not expose runtime prompt overrides through the standard slash-command interface.

### Where is the final report saved when using CLI invocation?

When triggered via `claude --skill investment-team`, the skill writes the markdown output to `~/[CompanyName]投资研究报告_YYYYMMDD.md` by default. You can inspect this file directly after the `TeamDelete` operation completes, or use [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py) to perform additional validation on the saved document.