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

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 lo

Source repository stars
29,558
Declared platforms
0
Static risk flags
0
Last source update
2026-08-04
Source checked
2026-08-04

Decision brief

What it does—and where it fits

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.

Best for

  • 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

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/HKUDS/Vibe-Trading --skill "agent/src/skills/deep-company-series"
Safe inspection promptEditorial

Inspect the Agent Skill "deep-company-series" from https://github.com/HKUDS/Vibe-Trading/blob/3a752d5a8ed088633040893de1cc9e6dc712596f/agent/src/skills/deep-company-series/SKILL.md at commit 3a752d5a8ed088633040893de1cc9e6dc712596f. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    Phase 1: Research (before writing 01-02)

    1. getfinancialstatements — last 5 years of annuals, latest quarterly 2. getresearchreports / websearch — at least 3 independent sell-side reports (find consensus + dissent) 3. Optional: runswarm (e.g. equityresearchteam or valueinvestingcommittee) to generate an internal resear…

    getfinancialstatements — last 5 years of annuals, latest quarterlygetresearchreports / websearch — at least 3 independent sell-side reports (find consensus + dissent)Optional: runswarm (e.g. equityresearchteam or valueinvestingcommittee) to generate an internal research draft
  2. 02

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

    After each part, writefile to reports/{company}/《Understanding {company}》/0X-XX.md

    After each part, writefile to reports/{company}/《Understanding {company}》/0X-XX.mdDon't publish immediately — wait for user reviewRevise on feedback
  3. 03

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

    This is the key differentiator. Use tools to scan:

    readfile each part + reportaudit (command=extract) to pull numbers (market cap, net income, holding %, PE) from eachCross-check the same number across parts — use financialrigor (command=crossvalidate) to cross-validate the same metric's values across articles; flag 1% deviation as a caliber mismatchreadfile 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. 04

    Phase 4: Pre-publish Final Check

    reportaudit (command=verdict) as a gate on each part: extract numbers → verify → PASS/FAIL

    reportaudit (command=verdict) as a gate on each part: extract numbers → verify → PASS/FAILConfirm all numbers are traceable, no pseudo-precision, no absolutism- reportaudit (command=verdict) as a gate on each part: extract numbers → verify → PASS/FAIL - Confirm all numbers are traceable, no pseudo-precision, no absolutism
  5. 05

    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 o…

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

Permission review

Static risk signals and limitations

No configured static risk pattern was detected

This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score86/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars29,558SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
HKUDS/Vibe-Trading
Skill path
agent/src/skills/deep-company-series/SKILL.md
Commit
3a752d5a8ed088633040893de1cc9e6dc712596f
License
MIT
Collected
2026-08-04
Default branch
main
View the original SKILL.md

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).

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