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).
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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.
The user wants "textbook-level" deep research on a company, published as a series of long-form articles. Distinct from a single research report:
Not for: a single research report, earnings note, sector study — use other skills (fundamentals / earnings / sector).
| # | 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Plus 00-series-overview.md as an index (unpublished).
| 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 |
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.2025 +33% × 5y CAGR → 2030 X is financial illiteracy. Use scenario assumptions + high/low ranges; never a promise.□ 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)"get_financial_statements — last 5 years of annuals, latest quarterlyget_research_reports / web_search — at least 3 independent sell-side reports (find consensus + dissent)run_swarm (e.g. equity_research_team or value_investing_committee) to generate an internal research draftwrite_file to reports/{company}/《Understanding {company}》/0X-XX.mdThis 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 eachfinancial_rigor (command=cross_validate) to cross-validate the same metric's values across articles; flag >1% deviation as a caliber mismatchread_file checks: is each term (FBS, SBC, Non-IFRS) defined at first use; do "see part 06" references actually resolve; do recaps match body numbersreport_audit (command=verdict) as a gate on each part: extract numbers → verify → PASS/FAILIf 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".
| 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 |
Before fixing one spot, think "where else is this number/concept referenced":
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).
HKUDS/Vibe-Trading
2dab6b1
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