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Deep Company Series

OrganizationPopular
HKUDS
deep-company-series

Write a publication-grade 8-part deep-dive series on a single company (~120k words total): cognitive reset / moat / profit engine / hidden assets / era variable (e.g. AI) / financials Buffett-style / management / valuation+redlines. The core IP is NOT writing but REVISING — a strict fact-check checklist catches pseudo-precision (probability-weighted expectations, third-party MAU discrepancies, linear extrapolation), absolute language, and cross-article number inconsistencies that most finance long-forms violate. Each piece stands alone but shares one valuation/management/price framework. Use when the user wants textbook-level depth on one company for public publishing (a single research report or earnings note is NOT this — use investment-research / earnings-review instead).

Overview

PublisherHKUDS
RepositoryVibe-Trading
Skill namedeep-company-series
Stars
33.6K
Forks
5.5K
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 Deep Company Series 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.
  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/deep-company-series .claude/skills/deep-company-series
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deep Company Series 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 Deep Company Series 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 Deep Company Series 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.

Deep-Company Series: An 8-Part Deep Dive on One Company

Write an 8-part deep-dive series (~120k words total) on a single company, from cognitive reset to a decision framework. The core IP is not "writing well" but "revising strictly" — most finance long-form violates this skill's fact-check standard.

1. When to Use

The user wants "textbook-level" deep research on a company, published as a series of long-form articles. Distinct from a single research report:

  • 8 parts, ~120k words, full loop from cognitive reset to a decision framework
  • Each part stands alone (shareable singly) but shares one valuation / management / price framework
  • Written for readers willing to spend 90 minutes understanding one company

Not for: a single research report, earnings note, sector study — use other skills (fundamentals / earnings / sector).

2. Series Template (8 Parts)

#Title templateCore questionWords
01You think you understand X — you don'tCognitive reset: break 3 common illusions4,000-5,000
02X's moat — {one-line business essence}Is the moat deep; will it be there in 5/10 years6,000-8,000
03X's biggest profit engine — {most profitable business}What is the core business; why it persists6,000-8,000
04The other company hidden on X's balance sheet — {hidden asset}Investment portfolio / subsidiary / hidden value8,000-10,000
05In the AI (or current narrative) era, is X a winner or loserEra variable: decompose the impact by business8,000-10,000
06Reading X's financials the Buffett wayFinancial depth: gross margin / FCF / ROE / SBC8,000-10,000
07{management quote} — is X's management worth entrustingCapital-allocation discipline + integrity test + succession8,000-10,000
08At what price to buy, what signal to sell (finale)DCF 3-scenario + red lines + position framework10,000-12,000

Plus 00-series-overview.md as an index (unpublished).

3. Writing Style

Voice

  • Direct, sharp, no filler — open with a number or a counterintuitive claim
  • Value-investing frame — Buffett/Munger/Duan Yongping/Li Lu lenses woven in (no name-dropping)
  • No preset stance — data first, logic next, conclusion last
  • Show both sides — every core judgment carries a "but on the other hand..."
  • Mobile preview — the first 18-20 characters must stand alone

Banned Words

BannedWhyReplace with
obviously / inevitably / certainlySubjective absolutism"the data shows" / "evidence suggests"
I think / I feelSubjective tonecut, or "under this framework"
textbook-level / brilliantHype adjectivesdescribe the concrete fact
severely mismatched / severely undervaluedStrong subjectivegive the specific discount %
perfect / flawlessOne-sidedadd the counter-observation

Title Style

  • Hook with a contrast number or a counter-consensus claim ("15 years, 7 failed challenges"; "salary 42.92M = 0.0017% of profit")
  • Neutral subtitle summarizing content
  • Avoid hype metaphors: "the next Buffett", "the X of China", "GOAT" — all banned

4. Strict Fact-Check Checklist (the Core IP)

"Pseudo-precision" traps to watch for before writing

  1. Probability-weighted expected value: 30% × A + 50% × B + 20% × C = expected +X% is almost always garbage — the probabilities are pure subjective, giving readers false precision. List scenarios + triggers + direction only; do not compute a weighted expectation.
  2. Third-party MAU/share estimates: QuestMobile / 七麦 / CBNData differ hugely (2-3× at the same point). Use only the two most-credible as anchors; describe the rest qualitatively.
  3. Linear extrapolation of historical growth: 2025 +33% × 5y CAGR → 2030 X is financial illiteracy. Use scenario assumptions + high/low ranges; never a promise.
  4. Undisclosed shareholding: unlisted-company stakes are never publicly disclosed. Give a range, mark "unknowable".
  5. Strong attribution: "competitor failed because of X." List multiple causes; this article does no single attribution.

The 7 mandatory revision checks

□ 1. Cross-article number consistency: market cap, Non-IFRS net income, key holding % aligned across the series
□ 2. Caliber labeling: Non-IFRS / GAAP / Non-IFRS-SBC / FCF — which is used, clear throughout
□ 3. Double-counting scan: consolidated subs are NOT in the "investment portfolio"; SOTP doesn't count them twice
□ 4. Peer-comparison fairness: don't compare "core-business PE (cash + portfolio stripped)" with "peer PE (not stripped)"
□ 5. Probability-weighted expectations deleted (see above)
□ 6. Absolute language softened: grep "obviously|inevitably|severely|textbook|perfect"
□ 7. Third-party data sourced: every non-filing data point followed by "(source: X)"

Known hard-error risks (list before writing)

  • Historical return multiples: use cumulative-invested basis (e.g. Riot 33×, not 58×)
  • Shareholding %: use the latest filing/financial-app basis (e.g. Tencent's Meituan stake changes with disposals)
  • "Distribution accounting": treated as disposal gain under IFRIC 17, recognized on declaration date
  • Share count rebounds: SBC granted in clusters at year-start can lift share count short-term

5. Execution

Phase 1: Research (before writing 01-02)

  1. get_financial_statements — last 5 years of annuals, latest quarterly
  2. get_research_reports / web_search — at least 3 independent sell-side reports (find consensus + dissent)
  3. Optional: run_swarm (e.g. equity_research_team or value_investing_committee) to generate an internal research draft
  4. Confirm the 8-part core theses with the user (avoid writing the wrong direction)

Phase 2: Writing (01→08 in order, no skipping)

  • After each part, write_file to reports/{company}/《Understanding {company}》/0X-XX.md
  • Don't publish immediately — wait for user review
  • Revise on feedback

Phase 3: Cross-Article Consistency Scan (after all 8)

This is the key differentiator. Use tools to scan:

  1. read_file each part + report_audit (command=extract) to pull numbers (market cap, net income, holding %, PE) from each
  2. Cross-check the same number across parts — use financial_rigor (command=cross_validate) to cross-validate the same metric's values across articles; flag >1% deviation as a caliber mismatch
  3. read_file checks: is each term (FBS, SBC, Non-IFRS) defined at first use; do "see part 06" references actually resolve; do recaps match body numbers
  4. Absolute-language scan: grep "obviously|inevitably|severely|perfect" and soften each

Phase 4: Pre-publish Final Check

  • report_audit (command=verdict) as a gate on each part: extract numbers → verify → PASS/FAIL
  • Confirm all numbers are traceable, no pseudo-precision, no absolutism

6. Revision-Feedback Handling

1. Verify facts first (don't just change)

If the user says "X is wrong", use get_financial_statements / web_search to cross-check the original; present "user's number vs what I found vs what I used".

2. Grade the revision

GradeTypeHandle
🔥 Hard errorwrong number / attribution / caliberMust fix
⚠️ Subjectivestrong subjective word / hype metaphorSoften or cut
🔬 Granularitysource label, caliber refinementBalance against readability
❓ Unreliablelarge third-party discrepanciesDeleting is safer than editing

3. Cascade check after a fix

Before fixing one spot, think "where else is this number/concept referenced":

  • Market cap changed → cascade to PE / core-business PE / discount / FCF yield
  • Holding % changed → fix TOP-10 sort + historical holding table + disposal list
  • Caliber changed → fix first definition + later references + recap

7. What This Skill Does NOT Do

  • Does not make investment decisions for the reader — every part ends with "not investment advice"
  • Does not predict prices — only "scenarios + triggers"
  • Does not compute a weighted "expected annualized return" — subjective probability misleads
  • Does not write "famous investor X also holds" — using someone else's holding to back your judgment is anti-value-investing
  • Does not force all 8 parts — if a part lacks enough standalone content (e.g. management isn't distinctive), merge it or reduce the count

One-liner: writing an "Understanding X" series is about revising strictly, not writing well — most finance long-form dies from pseudo-precise numbers, subjective weighted expectations, and absolute language. This skill exists to flag all those traps before writing and sweep them clean after (report_audit + financial_rigor.cross_validate).

Frequently asked questions

What does the Deep Company Series AI skill do?

Write a publication-grade 8-part deep-dive series on a single company (~120k words total): cognitive reset / moat / profit engine / hidden assets / era variable (e.g. AI) / financials Buffett-style / management / valuation+redlines. The core IP is NOT writing but REVISING — a strict fact-check checklist catches pseudo-precision (probability-weighted expectations, third-party MAU discrepancies, linear extrapolation), absolute language, and cross-article number inconsistencies that most finance long-forms violate. Each piece stands alone but shares one valuation/management/price framework. Us...

Why use Deep Company Series on TypingMind?

Because you install it once and use it with any model. Deep Company Series 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 Deep Company Series in TypingMind?

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

Which AI models can use Deep Company Series?

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 Deep Company Series?

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

Is the Deep Company Series 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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