# What Is the Thesis Tracker Skill for Post-Investment Analysis?

> Understand the Thesis Tracker skill for post-investment analysis. This system monitors investment theses using verifiable hypotheses and red-line triggers, removing emotion from decisions.

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: how-to-guide
- Published: 2026-06-27

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**The Thesis Tracker skill implements a disciplined monitoring system that treats investment theses as sets of verifiable hypotheses and red-line triggers, automatically calculating a health score and flagging status changes to remove emotion from portfolio decisions.**

The **thesis tracker skill for post-investment analysis** provides a systematic framework for monitoring whether an investment thesis remains valid after purchase. Implemented in the `xbtlin/ai-berkshire` repository, this skill enforces Warren Buffett's principle of writing sell-conditions before buying, ensuring that post-investment decisions are driven by evidence rather than market noise.

## Core Concept and Philosophy

The skill operates on the principle that every investment thesis should be treated as a collection of **verifiable hypotheses** with predefined **red-line triggers**. Rather than reacting to daily price movements, the system conducts quarterly re-evaluations (or immediate checks when major events occur) to determine if the original investment logic still holds.

This design mirrors Buffett's "write the sell-conditions before you buy" approach, as documented in [`skills/thesis-tracker.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/thesis-tracker.md). By establishing concrete validation criteria upfront, the skill ensures that **discipline, transparency, and actionable triggers** drive decisions rather than emotional reactions to market volatility.

## The Two-Mode Workflow Architecture

The skill splits operation into two distinct modes that trigger automatically based on the presence of existing thesis documentation.

### Mode A: Establishing the Investment Thesis

When no existing `reports/{company_name}-thesis.md` file is found, the skill initiates thesis creation mode:

1. **Data Gathering**: Pulls price, valuation, and latest financial data via the `WebSearch` tool
2. **Validation**: Runs valuation verification through [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py)
3. **Documentation**: Writes a concise 5-sentence core thesis, hypothesis table, red-line table, and valuation anchors to `reports/{company_name}-thesis.md`

### Mode B: Quarterly Tracking and Validation

When a thesis file exists, the skill executes tracking mode:

1. **Load Context**: Reads the existing thesis, hypothesis tables, red-line conditions, and prior check records
2. **Fresh Data Pull**: Retrieves latest earnings, major events, stock price, and insider trades
3. **Hypothesis Evaluation**: Re-evaluates each hypothesis, flagging status changes using the system:
   - 🟢 **成立** (Valid)
   - 🟡 **弱化** (Weakened)
   - 🔴 **受损** (Damaged)
   - ⚫ **破裂** (Broken)
4. **Red-Line Scan**: Checks all red-line conditions; any trigger forces an immediate recommendation
5. **Health Calculation**: Updates valuation anchors and computes a quantitative health score
6. **Persistence**: Appends results to the thesis file and generates a structured action recommendation

## The Health Score Algorithm

The skill employs an explicit scoring formula to quantify thesis integrity, defined in lines 776-782 of [`skills/thesis-tracker.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/thesis-tracker.md):

```markdown
健康度 = 10 − (⚫破裂假设数×3) − (🔴受损假设数×2) − (🟡弱化假设数×1) − (红线触发数×5)

```

This formula generates a maximum score of 10, penalizing broken hypotheses most severely (-3 points each), followed by damaged hypotheses (-2), weakened hypotheses (-1), and red-line triggers (-5 each). The resulting score provides an objective metric for comparing portfolio health across holdings during `portfolio-review` operations.

## Implementation Details and File Structure

The skill integrates with the broader `ai-berkshire` architecture through specific file paths and tooling:

**Entry Point**: Invoked via the slash command `/thesis-tracker` (registered in [`README.md`](https://github.com/xbtlin/ai-berkshire/blob/main/README.md) and [`README_EN.md`](https://github.com/xbtlin/ai-berkshire/blob/main/README_EN.md))

**Data Sources**: 
- `WebSearch` tool for market data
- [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) for valuation verification

**Persistence Layer**: 
- Canonical thesis documents stored in `reports/{company_name}-thesis.md`
- Markdown format enables human readability while maintaining structured data

**Codex Compatibility**: 
- Auto-generated version at [`codex-skills/thesis-tracker/SKILL.md`](https://github.com/xbtlin/ai-berkshire/blob/main/codex-skills/thesis-tracker/SKILL.md) ensures identical behavior for Claude Code and Codex users
- Maintains parity with the canonical source in [`skills/thesis-tracker.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/thesis-tracker.md)

**Integration Points**: 
- `investment-research` and `news-pulse` skills can reference thesis files for context
- `portfolio-review` skill aggregates health scores across holdings

## Practical Usage Examples

The skill accepts specific command syntax to trigger different operational modes:

```markdown

# Establish new thesis (first use)

/thesis-tracker 拼多多

# Force re-establishment (when rewriting thesis)

/thesis-tracker 拼多多 建立论文

# Quarterly check (auto-detects existing thesis)

/thesis-tracker 拼多多 季度检查

```

Each execution returns a structured markdown report containing:
- **Health score** (0-10 scale)
- **Updated hypothesis table** with current status flags
- **Red-line status** (triggered or clear)
- **Valuation comparison** (purchase price vs. current)
- **Action recommendation** (加仓/Add, 持有/Hold, 减仓/Reduce, 清仓/Exit)

## Summary

- The **thesis tracker skill for post-investment analysis** transforms qualitative investment theses into quantitative, verifiable frameworks stored in `reports/{company_name}-thesis.md`
- **Dual-mode operation** automatically handles initial thesis creation (Mode A) and ongoing quarterly validation (Mode B) based on file existence checks
- **Objective health scoring** uses the formula `健康度 = 10 − (⚫×3) − (🔴×2) − (🟡×1) − (红线×5)` to eliminate subjective bias in portfolio reviews
- **Red-line triggers** provide immediate exit signals when fundamental conditions break, enforcing the discipline of pre-defined sell criteria
- **Cross-skill integration** allows `portfolio-review` to aggregate health scores while leveraging [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) for valuation anchoring

## Frequently Asked Questions

### How does the health score calculation work in the Thesis Tracker?

The health score uses a subtraction-based algorithm where 10 is the maximum possible score. The system subtracts 3 points for each broken hypothesis (⚫), 2 points for each damaged hypothesis (🔴), 1 point for each weakened hypothesis (🟡), and 5 points for any triggered red-line condition. This weighted approach prioritizes structural thesis breaks over gradual deterioration, ensuring that critical failures trigger immediate action recommendations.

### What determines whether Mode A or Mode B executes?

The skill automatically detects the presence of a thesis file at `reports/{company_name}-thesis.md`. If no file exists, Mode A (Establish Thesis) executes to create the initial hypothesis tables and red-line conditions. If the file exists, Mode B (Tracking Check) loads the existing thesis and evaluates current data against the stored hypotheses. Users can force Mode A execution by appending "建立论文" (establish thesis) to the command.

### Where are the thesis documents stored and what format do they use?

All thesis documents persist as markdown files in the `reports/` directory, named `{company_name}-thesis.md`. This format stores the core 5-sentence thesis, hypothesis tables with status flags, red-line trigger definitions, valuation anchors, and historical check records. The markdown structure enables both human readability and programmatic parsing by other skills like `portfolio-review` and `investment-research`.

### How does this system prevent emotional decision-making?

By requiring investors to define specific red-line triggers and verifiable hypotheses before purchase (Mode A), the skill creates a contractual framework that governs post-investment decisions. When Mode B executes, it evaluates objective data against these pre-established criteria rather than current price action or market sentiment. The quantitative health score and forced recommendations (加仓/持有/减仓/清仓) remove discretion during periods of volatility, ensuring that exits occur when the thesis breaks rather than when fear peaks.