deep-company-series

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 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. 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. Linear extrapolation of historical growth : 2025 +33% × 5y CAGR → 2030 X is financial illiteracy. Use scenario assumptions + high/low ranges; never a promise. Undisclosed shareholding : unlisted-company stakes are never publicly disclosed. Give a range, mark "unknowable". 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) get_financial_statements — last 5 years of annuals, latest quarterly get_research_reports / web_search — at least 3 independent sell-side reports (find consensus + dissent) Optional: run_swarm (e.g. equity_research_team or value_investing_committee) to generate an internal research draft 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: read_file each part + report_audit ( command=extract ) to pull numbers (market cap, net income, holding %, PE) from each 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 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 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 ).