Agent SkillsHKUDS/Vibe-Trading › deep-company-series

deep-company-series

GitHub

针对单一公司撰写约12万字的8篇深度系列报道,涵盖认知重塑、护城河、财务及管理评估。核心在于严格的修订与事实核查,确保数据一致性与客观性,适用于出版级长文创作。

agent/src/skills/deep-company-series/SKILL.md HKUDS/Vibe-Trading

Trigger Scenarios

需要对公司进行多部分深度剖析 生成出版级长篇财经系列文章 要求严格的事实核查与修订

Install

npx skills add HKUDS/Vibe-Trading --skill deep-company-series -g -y
More Options

Non-standard path

npx skills add https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/deep-company-series -g -y

Use without installing

npx skills use HKUDS/Vibe-Trading@deep-company-series

指定 Agent (Claude Code)

npx skills add HKUDS/Vibe-Trading --skill deep-company-series -a claude-code -g -y

安装 repo 全部 skill

npx skills add HKUDS/Vibe-Trading --all -g -y

预览 repo 内 skill

npx skills add HKUDS/Vibe-Trading --list

SKILL.md

Frontmatter
{
    "name": "deep-company-series",
    "category": "analysis",
    "description": "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)."
}

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 template Core question Words
01 You think you understand X — you don't Cognitive reset: break 3 common illusions 4,000-5,000
02 X's moat — {one-line business essence} Is the moat deep; will it be there in 5/10 years 6,000-8,000
03 X's biggest profit engine — {most profitable business} What is the core business; why it persists 6,000-8,000
04 The other company hidden on X's balance sheet — {hidden asset} Investment portfolio / subsidiary / hidden value 8,000-10,000
05 In the AI (or current narrative) era, is X a winner or loser Era variable: decompose the impact by business 8,000-10,000
06 Reading X's financials the Buffett way Financial depth: gross margin / FCF / ROE / SBC 8,000-10,000
07 {management quote} — is X's management worth entrusting Capital-allocation discipline + integrity test + succession 8,000-10,000
08 At what price to buy, what signal to sell (finale) DCF 3-scenario + red lines + position framework 10,000-12,000

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

Banned Why Replace with
obviously / inevitably / certainly Subjective absolutism "the data shows" / "evidence suggests"
I think / I feel Subjective tone cut, or "under this framework"
textbook-level / brilliant Hype adjectives describe the concrete fact
severely mismatched / severely undervalued Strong subjective give the specific discount %
perfect / flawless One-sided add 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

Grade Type Handle
🔥 Hard error wrong number / attribution / caliber Must fix
⚠️ Subjective strong subjective word / hype metaphor Soften or cut
🔬 Granularity source label, caliber refinement Balance against readability
❓ Unreliable large third-party discrepancies Deleting 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).

Version History

  • 0aa45a9 Current 2026-07-24 17:45

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