Thesis Tracker logo

Thesis Tracker

OrganizationPopular
HKUDS
thesis-tracker

Buy-side discipline system. For each holding, maintain a written investment thesis — core thesis in 5 sentences, falsifiable assumptions, red lines, valuation anchors — and re-check it each quarter against new earnings/events. Scores thesis health 1-10 from assumption breakage and red-line triggers, and recommends hold / add / trim / exit. Use it right after buying a stock (to write the thesis) and every earnings season (to check it). Bridges the research→buy→monitor gap that most investors skip.

Overview

PublisherHKUDS
RepositoryVibe-Trading
Skill namethesis-tracker
Stars
35.2K
Forks
5.7K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by HKUDS on GitHub. Read the source before you install it.

Installation

Install the Thesis Tracker AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/thesis-tracker
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git /tmp/Vibe-Trading
mkdir -p .claude/skills
cp -r /tmp/Vibe-Trading/agent/src/skills/thesis-tracker .claude/skills/thesis-tracker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thesis Tracker in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Thesis Tracker on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Thesis Tracker is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Investment Thesis Tracker: Post-Buy Discipline

Track an investment thesis for a holding. First use on a company → build the thesis; later uses → quarterly check.

Supported input: company name (auto-decide build vs track); company build (force rebuild); company quarterly check (against latest earnings).

"Buying is only the beginning. The real work is continuous tracking through the holding period." — Li Lu

"When the facts change, I change my mind. What do you do, sir?" — Keynes

Design Philosophy

Most investors' process is: research → buy → pray. Without systematic post-buy tracking, they: refuse to sell when they should ("wait, it'll come back"); panic-sell when they shouldn't ("down 20%, was I wrong?"); forget why they bought ("why did I buy this again?").

Buffett and Li Lu write down sell conditions before buying, then check the thesis each quarter.

Step 1: Decide the Mode

Check whether a thesis file already exists (use read_file on reports/{company}-thesis.md): not found → Build (Mode A); found → Track (Mode B).


Mode A: Build the Investment Thesis

A0: Data Collection

Use web_search to get the current price, valuation (PE/PB/dividend yield), and latest financials. If a research report exists, read_file it first. Use financial_rigor (command=verify_valuation) to verify valuation, financial_rigor (command=verify_market_cap) to sanity-check market cap.

A1: Core Thesis (must be writable in ≤200 words)

The thesis must answer 5 questions, one sentence each:

I bought {Company} at {price} because:
1. The essence of this business is ___, and I understand how it makes money.
2. Its moat is ___, and it's widening / stable.
3. Management is ___, trustworthy because ___.
4. The current price is a {discount}% to intrinsic value; the margin of safety comes from ___.
5. Even if I'm wrong, downside is contained because ___.

If you can't fill 5 sentences, the thesis itself is flawed — the buy decision isn't clear enough.

A2: Core Assumptions List

Break the thesis into verifiable assumptions (3-7 typically; too few = shallow thinking, too many = unfocused):

#Core AssumptionHow to verifyFrequencyStatus
1e.g. revenue growth stays >15%Quarterly revenue growthQuarterly🟢 Holding
2e.g. gross margin stable >60%Quarterly gross marginQuarterly🟢 Holding

A3: Red Lines (triggering any one = must re-evaluate)

#Red-line conditionSeverityAction on trigger
1Management integrity problem (fraud, related-party)FatalExit immediately
2Core business revenue down 2 consecutive quartersSevereTrim 50%, re-evaluate
3Moat explicitly breached (competitor reaches parity)SevereDeep research, consider exit
4Regulatory change fundamentally alters the business modelSevereRe-value intrinsic value
5Management large unplanned disposalWarningInvestigate
6Macro trend reversal for this name's end-market — e.g. demographic clock (population decline) for consumer/property/education names; consumption downgrade (居民边际消费下降); generational shift away from the categorySevereRe-evaluate the long-term demand base; for consumer-dependent names, this is often a slow-moving but fatal headwind — assess whether the thesis still holds at a structurally lower growth path

The red line on macro trends (demographic clock / consumption / generational) is deliberately explicit: a value-investing thesis that picks a consumer name without weighing population decline and consumption-trend headwinds is incomplete. For non-consumer names (e.g. pure B2B industrial), this red line may be loose; for consumer/retail/education/property names, weight it heavily.

Duan Yongping: "There are only three reasons to sell: 1. you got the buy wrong; 2. the fundamentals changed; 3. you found something better."

A4: Valuation Anchors

MetricAt buyOptimistic targetNeutral targetPessimistic
Price
PE (TTM)
Market cap
Intrinsic value estimate
Margin of safety

A5: Save the Thesis

Use write_file to write reports/{company}-thesis.md, including: build date, buy price and position, core thesis (5 sentences), assumptions list, red lines, valuation anchors, tracking table (empty initially).


Mode B: Tracking Check

B1: Read the Existing Thesis

Use read_file to load core thesis, assumptions, red lines, last check record.

B2: Gather Latest Data

Use web_search / get_stock_news / get_financial_statements: ① latest earnings (if new quarter/year); ② recent material events (management, regulation, competition); ③ current price and valuation; ④ insider transactions (major shareholder changes).

B3: Check Each Assumption

#Core AssumptionLast statusLatest evidenceCurrent statusChange
1Revenue growth >15%🟢 HoldingQ4 revenue growth 12%🟡 Weakening⚠️

Status definitions:

  • 🟢 Holding — latest data supports
  • 🟡 Weakening — still acceptable, but trend is unfavorable
  • 🔴 Impaired — data clearly does not support
  • ⚫ Broken — assumption has been overturned

B4: Red-Line Check

Check each red line; any triggered → flag prominently in the report + clear action recommendation.

B5: Valuation Update

Use financial_rigor (command=verify_valuation) to recompute PE/PB/ROE; compare against buy-time and last check:

MetricAt buyLast checkCurrentChange
Price
PE (TTM)
Intrinsic value estimate
Margin of safety

B6: Output the Tracking Report

Structure: ① Thesis health score (out of 10); ② Assumption check table; ③ Red-line check table; ④ Key changes this period (≤500 words); ⑤ Valuation update; ⑥ Conclusion and action; ⑦ Focus for next check.

Health formula: 10 - (⚫broken × 3) - (🔴impaired × 2) - (🟡weakening × 1) - (red-lines triggered × 5), min 1, max 10.

ScoreMeaningAction
9-10All assumptions hold, thesis stronger than at buyConsider adding
7-8Core assumptions hold, some weakeningContinue holding
5-61-2 assumptions impaired, core logic intactHold but raise alertness
3-4Multiple assumptions impaired, foundation shakenConsider trimming
1-2Red line triggered or core assumption brokenStrongly consider exit

The conclusion must clearly answer: ① Is the thesis still intact? (intact / weakening / impaired / broken); ② What to do? (add / hold / trim / exit); ③ Next check timing (next earnings / a specific event).

B7: Update the Thesis File

Use write_file to append this check to the tracking table in reports/{company}-thesis.md:

Check dateHealthKey changeAction
2026-04-097/10Revenue growth slowed to 12%, but margins improvedHold

Key Principles

  • Write sell conditions before buying — decisions made calmly beat decisions made in panic.
  • Assumptions must be verifiable — "a great company" is not a thesis; "ROE>25% and stable" is.
  • Act when a red line triggers — "let's wait and see" is how big losses happen.
  • Thesis breaking ≠ stock falling — a 30% price drop doesn't force a sell; a broken thesis does.
  • Be honest about mistakes — if the thesis was wrong, admit it; don't defend it to save face.
  • Weigh macro headwinds for consumer names — demographic decline and consumption downgrade are slow but can be fatal for consumer/property/education theses; bake them into red lines, not just quarterly checks.

Frequently asked questions

What does the Thesis Tracker AI skill do?

Buy-side discipline system. For each holding, maintain a written investment thesis — core thesis in 5 sentences, falsifiable assumptions, red lines, valuation anchors — and re-check it each quarter against new earnings/events. Scores thesis health 1-10 from assumption breakage and red-line triggers, and recommends hold / add / trim / exit. Use it right after buying a stock (to write the thesis) and every earnings season (to check it). Bridges the research→buy→monitor gap that most investors skip.

Why use Thesis Tracker on TypingMind?

Because you install it once and use it with any model. Thesis Tracker is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Thesis Tracker in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/thesis-tracker. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Thesis Tracker?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Thesis Tracker?

As many as you like. As long as a model supports skills, you can use Thesis Tracker with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Thesis Tracker AI skill free?

Yes. It is published on GitHub by HKUDS under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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