How the `/thesis-tracker` Skill Enforces Post-Buying Discipline for Investment Thesis Validation

The /thesis-tracker skill enforces post-buying discipline by converting static research notes into a structured, quarterly-reviewed living document that triggers objective actions based on pre-defined red-lines and hypothesis validation.

Investing successfully requires more than picking winners; it requires the discipline to sell when the thesis breaks. The /thesis-tracker skill in the xbtlin/ai-berkshire repository eliminates emotional decision-making by systematizing post-buying discipline for investment thesis validation through a rigorously structured workflow defined in skills/thesis-tracker.md.

From Static Notes to Living Documents

The skill operates on the principle that an investment thesis must be a testable hypothesis rather than a narrative. It enforces discipline through two primary operational modes that transform initial research into an immutable tracking system.

Mode A: Pre-Defined Sell Conditions

When you invoke /thesis-tracker for a new company lacking a {公司名}-thesis.md file, the skill enters Mode A (建立投资论文). This guided workflow forces you to define concrete exit criteria before committing capital, including:

• A 5-sentence core thesis that fits inside reports/{公司名}-thesis.md
• A hypothesis table (核心假设清单) pairing each assumption with verification methods and quarterly review frequencies
• A red-line list (红线清单) of deal-breaker conditions
• Valuation anchors validated automatically by calling tools/financial_rigor.py verify-valuation

By requiring these elements upfront—documented in lines 34-95 of the specification—the skill ensures you cannot enter a position without explicit exit criteria, preventing retrospective rationalization.

Mode B: Quarterly Validation Workflow

Once established, the thesis enters Mode B (季度检查), triggering a systematic review process that pulls latest financials and events to update hypothesis statuses. Each hypothesis receives a discrete status marker:

• 🟢 成立 (Holding)
• 🟡 弱化 (Weakening)
• 🔴 受损 (Damaged)
• ⚫ 破裂 (Broken)

This visual tracking system, defined in the 核心假设清单 section at lines 57-67, removes ambiguity about whether your original assumptions still hold, forcing you to confront evidence that contradicts your position.

Red-Line Guardrails and Forced Actions

The most critical discipline mechanism is the red-line table (红线清单) located at lines 71-80 of skills/thesis-tracker.md. Unlike gradual hypothesis degradation, red-lines represent existential threats—such as management fraud or sustained revenue decline—that trigger immediate, predefined actions.

When a red-line condition is met, the skill mandates a concrete recommendation (e.g., 立即清仓 / immediate liquidation), removing the emotional temptation to "wait and see" or "average down" on a broken thesis.

Quantitative Health Scoring

After checking hypotheses and red-lines, the skill calculates a 论文健康度 (Thesis Health Score) from 0-10 using the weighted formula found at lines 76-86. This score maps directly to disciplined portfolio actions:

Score Range	Action	Meaning
8-10	加仓	Add to position
6-7	持有	Hold current position
4-5	减仓	Reduce position size
0-3	清仓	Exit completely

By translating qualitative concerns into a quantitative score, the system forces objective decision-making even when sunk-cost bias pressures you to hold losing positions.

Immutable Audit Trails

Every review appends a structured record to the thesis file using the 追踪记录表 format at lines 94-102:

检查日期 | 健康度 | 核心变化 | 动作建议

This append-only log creates an immutable review trail that makes it impossible to ignore past warnings or rationalize away deteriorating fundamentals. The historical record lives directly in reports/{公司名}-thesis.md, ensuring continuity across quarters and preventing the "what was I thinking?" memory lapses that plague discretionary investors.

Automation Hooks for Bias Reduction

The skill integrates with tools/financial_rigor.py to automate valuation anchor verification during both initial creation and quarterly checks. By calling verify-valuation automatically, the system reduces manual bias in interpreting whether price multiples still offer adequate margin of safety, ensuring that "cheap" is determined by algorithmic checks rather than emotional attachment.

Practical Usage Examples

Invoke the skill using the following syntax patterns:

/thesis-tracker 腾讯                # First-time creation (Mode A)

/thesis-tracker 腾讯 建立论文      # Force thesis rebuild after major pivot

/thesis-tracker 腾讯 季度检查      # Run quarterly validation (Mode B)

A typical quarterly check outputs a structured report like this:

# 腾讯 – 投资论文追踪报告（2026-04-09）

一、论文健康度评分：7/10  
二、核心假设检查  
| # | 假设                     | 上次状态 | 最新证据               | 当前状态 | 变化 |

|---|--------------------------|----------|------------------------|----------|------|
| 1 | 收入增速≥15%            | 🟢 成立   | Q4收入增速12%          | 🟡 边际弱化 | ⚠️   |
| 2 | 毛利率≥60%               | 🟢 成立   | 毛利率61.2%            | 🟢 成立   | —    |

三、红线检查  
| # | 红线条件                | 是否触发 | 证据 |

|---|------------------------|:--------:|------|
| 1 | 管理层诚信问题          | ❌ 未触发 | —    |
| 2 | 核心业务连续2季下滑    | ❌ 未触发 | —    |

四、本期关键变化：收入增速放缓至12%，但利润率提升。  
五、估值更新：PE从18×降至16×，安全边际仍在30%上。  
六、结论与行动建议：**持有**，关注收入增速继续走低的风险。  
七、下次检查重点：下一季报的收入增长率与管理层持股变动。  

（记录已追加至 `reports/腾讯-thesis.md`）

Summary

• Pre-defined exit criteria: Mode A forces documentation of red-lines and valuation anchors before purchasing, eliminating retrospective rationalization.
• Quarterly hypothesis validation: Mode B systematically checks assumptions against fresh data, updating statuses with explicit icons (🟢🟡🔴⚫).
• Quantitative health scoring: A 0-10 weighted score translates thesis strength into actionable portfolio decisions (加仓, 持有, 减仓, 清仓).
• Immutable audit trails: Append-only logs in reports/{公司名}-thesis.md prevent ignoring historical warnings.
• Automated validation: Integration with tools/financial_rigor.py verify-valuation reduces manual bias in valuation checks.

Frequently Asked Questions

What triggers the red-line alert mechanism in the thesis tracker?

Red-line alerts trigger when pre-defined deal-breaker conditions documented in the 红线清单 (lines 71-80 of skills/thesis-tracker.md) are breached. These include existential threats like management fraud, sustained revenue decline, or business model disruption. When triggered, the skill forces a concrete action recommendation—typically 立即清仓 (immediate liquidation)—removing emotional discretion from the exit decision.

How does the thesis health score determine portfolio actions?

The 论文健康度 score (0-10) calculated by the weighted formula in skills/thesis-tracker.md maps directly to disciplined actions: scores of 8-10 recommend 加仓 (adding to the position), 6-7 indicate 持有 (hold), 4-5 suggest 减仓 (reduce), and 0-3 mandate 清仓 (full exit). This quantitative translation prevents subjective justification for holding deteriorating positions.

Where is the investment thesis data stored and how is it structured?

Thesis data lives in reports/{公司名}-thesis.md files within the repository. Each file contains structured sections including the 5-sentence core thesis, a hypothesis verification table (核心假设清单), a red-line conditions table (红线清单), valuation anchors, and an append-only tracking log (追踪记录表) that records every quarterly check with dates, health scores, and action recommendations.

Can the thesis tracker integrate with external financial data sources?

Yes, the skill calls tools/financial_rigor.py verify-valuation to automate valuation anchor verification during both initial thesis creation and quarterly reviews. While the specification references tools/websearch for retrieving current prices, financials, and news events, the actual data fetching is implemented through the tool layer, allowing the thesis tracker to validate hypotheses against real-time market data without manual input.

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