# Meitou 5plus2 > Analyze a US stock with the "5+2 method" — a systematic fundamental framework popularized by the Chinese-language US-equity channel 美投君 (MeiTouJun). 5+2 = 7 steps: the first 5 are objective (① industry ② business model ③ management ④ financials ⑤ valuation), the last 2 are subjective (⑥ why-to-buy / investment logic, ⑦ why-not / risks). Output is a research brief — "full understanding + is it fairly valued + bull case vs bear case." Use when the user asks to "analyze TICKER with the 5+2 method" or types `/meitou TICKER`. Hard numbers come from the bundled data adapter; qualitative steps (industry/moat/management) come from web research. - Skill: `yichengyang-ethan/meitou-5plus2` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add yichengyang-ethan/meitou-5plus2` - Raw SKILL.md: https://api.skillmd.com/api/skills/yichengyang-ethan/meitou-5plus2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: yichengyang-ethan (https://skillmd.com/u/yichengyang-ethan) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/yichengyang-ethan/meitou-5plus2 --- # The 5+2 Method > Real definition (as its author puts it): **understand a company from 7 angles. The > first five are objective; the last two are subjective.** > Investment logic = my reasons to buy it; investment risk = forcing myself to think > of reasons NOT to buy it. > Detailed rules live in `references/5plus2-rubric.md`. **Before analyzing, classify the > company's archetype** via `references/06-archetype-router.md` (this is what makes the > method generalize to *any* ticker, not just the ones it was distilled from). ## When to use `/meitou TICKER`, or "analyze with the 5+2 method", or "what would the 5+2 framework say about this stock". ## Core mantras (run through everything) - **Pin down the single most important industry trend first.** What drives a company is often not its surface industry but the megatrend behind it (NVDA is *AI*, not the chip cycle). - **Focus on the core business.** Only dig into the segments that are the bulk of revenue; ignore <10% side businesses. - **Certainty > upside.** Prefer the most durable business model with the most assured demand. - **The reason for success, flipped, is the biggest risk** (the lens for step ⑦). - **End humbly.** 5+2 is the *start* of understanding a company, not a buy/sell signal. Don't fake a precise score. ## Workflow **Step 0 — Pull the hard numbers (financials / valuation).** Needs `pip install yfinance`. ```bash python3 scripts/factsheet.py TICKER --json # run from this skill's directory ``` (Human-readable: drop `--json`. The adapter only collects facts; it makes no judgement.) **Step 0.5 — Classify the company archetype (this is what makes 5+2 universal).** See `references/06-archetype-router.md`. Tag the company along 5 axes (profitability state / business model / capital intensity / growth stage / leadership & coverage) → look up the **main routing table** to get the right playbook for ④ financials, ⑤ valuation, ② moat lens, ⑥ bull archetype, ⑦ risk cluster. **If it matches none of the 8 archetypes, use the "universal fallback" playbook** — do NOT force-fit the closest case. The `[TICKER]` tags in the rubric are *examples*, not hard rules. **Step 1 — Work the 7 steps.** ①②③ are qualitative → combine with **web search** (industry TAM/growth, moat, management, latest developments); ④⑤ use the adapter numbers **plus the playbook chosen in Step 0.5** (don't pick the wrong valuation method — banks use P/B, story stocks use SOTP, consumption-SaaS uses EV/Sales); ⑥⑦ synthesize the first five. **Step 2 — Output the brief in the author's voice (label the archetype up top).** --- ## The 7-step framework (each step: what he asks / data source / judgement) ### ① Industry 〔web-led〕 - **Classify by source of profit, not surface industry** (NVDA = AI, not chips; META = digital ads, not social) → pin the most impactful trend → TAM + CAGR + adoption + geography + key customers + industry-level risk. - 🔴 **Always refresh the TAM live**: pull market size / CAGR from a **named research firm (Grand View / Mordor / Gartner / IDC), the latest figure, the correct narrow market definition**. Never reuse stale numbers or hand-wave "hundreds of billions". - **Enumerate the competitive landscape by category** (don't just name the biggest rival). - Judgement: many players racing in + high growth = tailwind; growth peaking / export bans / weak monetization = headwind. Strong-cyclical (banks/ads) → structural valuation discount, cliff-like downside. Consumer/retail → spending power (PCE/taxes) + willingness (sentiment). Actively hunt the **expectation gap** (where consensus misjudges a structural change). ### ② Business model 〔web + adapter evidence〕 - **History → how it makes money (revenue/customer mix) → name the *specific* moat type → durability → share/stickiness.** - **For SaaS, first identify "the one thing rivals structurally cannot do"** (e.g. a multi-cloud-neutral data platform is something the cloud vendors' own warehouses can't be) — *then* list switching costs / data gravity. Don't default to "network effect". - Moat library: network effect (CUDA-style ecosystem) / switching costs + vertical depth / scale + capital intensity "winner-keeps-winning" (foundry) / data & content flywheel / culture / one-stop platform / membership flywheel / cross-cloud neutrality. - **value-driver ≠ revenue-driver**: the business that drives the stock may not be the revenue bulk (a carmaker's value can sit in its self-driving option, not car sales). - Data: web (moat narrative/share) + adapter `moat.roic`, gross margin as moat evidence. ### ③ Management 〔web-led〕 - Core: **technical background + long tenure + stable culture** (need not be a founder). - **Founder-led = a plus** (higher ceiling, suited to disruptive innovation); check dual-class control. - **Double-edged**: a strong personality / concentrated control → over-reliance is a tail risk (write into ⑦). - **Judge professional (non-founder) CEOs by track record, NOT by a mechanical "non-founder = question mark" penalty.** A pro CEO can be a clear net positive — look at the actual execution (e.g. a sales-led → product-led culture switch that the latest results validate; "risk removed, trust restored"). - Negative tilt: short-seller "great marketer, profits don't follow" claims, prior baggage, spread too thin → management can be a net risk. ### ④ Financials 〔adapter-driven, pick metrics by archetype〕 - **Universal base (any stock)**: revenue growth + **gross margin (level + *trend* + benchmark; S&P 500 avg ~32%)** + **Debt / Total Assets < 40% (Buffett line; adapter's `statements.debt_to_assets_pct` = TOTAL debt incl. leases, which matches the ~20% read on AMZN — bond-only understates lease-heavy names like AMZN/retail/airlines; NOT D/E)** + FCF persistently positive + net margin. ⚠️ Over-high margins are themselves fragile → also a ⑦ risk. - **Semis/hardware**: gross margin = a **supply/demand thermometer** (high = demand >> supply); heavy-asset → check capex is covered by operating cash flow. - **Software SaaS**: revenue growth + *is it accelerating* + NRR (>120% healthy); **for GAAP-loss names, strip out SBC first** (`statements.sbc_pct_revenue`; add it back to see real profit) + check if buybacks offset the dilution + **judge profitability by FCF margin (>20%)**; asset-light → low capex; debt on a net-cash basis (converts vs cash). - **Capital-intensive / AI-infra**: capex/revenue + net leverage (net debt/EBITDA; >3 = downgrade risk) + whether FCF turned negative on capex + watch the rating agencies. - **Banks/financials**: efficiency ratio (opex/revenue, lower better) + ROTCE + CET1 (gross margin / debt ratio / FCF do NOT apply). - **Retail/consumer**: gross margin may be *deliberately* low (membership ~11%, so "GM = moat" fails) → look at stable net margin + renewal rate (90%+) + same-store growth + inventory turns. - **Turnaround / heavy-asset IDM**: read the *repair slope / inflection*, not the absolute level. - ⚠️ Compare margins on a like-for-like basis (don't pit one firm's gross margin against another's operating margin). - Data: adapter `growth.*`, `quality.*`, `statements.*` (debt/assets incl. leases, gross-margin trend, SBC), `valuation.ev_to_sales`. ### ⑤ Valuation — method chosen by archetype (see the decision tree in 06-archetype-router.md) - **Relative**: profitable large-cap → **forward PE (never trailing)**, triangulate vs market (SPX fwd ~22–25x) / own history / peers. Profitable high-growth software → high forward PE + P/S. **GAAP-loss / consumption SaaS → EV/Sales (better than P/S)** + the **Meritech rule** (sustaining >10x EV/Sales needs growth >20% AND FCF margin >20%) + **quality-adjusted peer comp (don't judge cheap/expensive against a single rival)**. **Story/option stocks → don't use relative multiples (distorted)**. - **Banks → P/B × BPS** (ROTCE-driven), not PE. - **Turnaround / trough earnings → "market-cap headroom" build-up** (TAM × share × net margin × PE + option value), because DCF and historical PE both break. - **Absolute DCF**: give **base / bull / bear** vs current price. Story stocks → **sum-of-parts SOTP-DCF** (discount each line, see where value concentrates). - ⚠️ **Universal valuation discipline**: ① **anchor the DCF base BELOW the analyst mean** (he runs conservative); ② **target price ∈ [base, bull]** — it can sit above base on high conviction, it is *not* simply the base; ③ conclusion is a range: undervalued / fair / overvalued / "neither cheap nor expensive". - ⭐ **Valuation ⊥ risk**: ⑤ answers "cheap or not", ⑦ answers "dare to buy or not". A stock can be "somewhat cheap" *and* "high risk". **"Expensive" can itself be the #1 risk** (slow to digest + no downside protection). - Data: adapter `forward_pe`, `ev_to_sales`, `price_to_sales`, `reverse_dcf_implied_growth`, `analyst_target_asymmetry`. ⚠️ For story stocks don't over-trust these multiples → go SOTP + qualitative. ### ⑥ Investment logic (why buy) 〔synthesis〕 One-line bull thesis + the precondition (he stresses *certainty*). **First test/falsify the popular bear narrative with data.** Common bull archetypes: growth + moat → certainty / a "toll booth" independent of who wins downstream / mature compounder (Costco-style: stickiness + price hikes + buybacks) / defensive counter-cyclical compounder / cyclical recovery + structural improvement / turnaround (new CEO + catalyst + sector beta + expectation gap) / high ceiling, firm floor. Story stocks → write a **conditional bull** ("only if you believe X gets commercialized"). Often cites authorities (Munger/Buffett). ### ⑦ Investment risk (why not buy) 〔synthesis〕 Bear case. Mantra: **the reason for success, flipped, is the risk** (find the single most important metric, ask when it deteriorates). Risk archetype library: ① core driver disappoints (supplier-type → watch downstream customers' capex) ② competition / share loss ③ supply chain / geopolitics / single point ④ high-multiple growth stock: growth peaks → multiple compresses ⑤ founder/strong-man concentration & key-man dependence ⑥ heavy-asset depreciation lag squeezing margins ⑦ macro / cyclical demand ⑧ regulation / antitrust / privacy ⑨ capital misallocation / overspend ⑩ subsidy roll-off ⑪ story-stock tech that never ships ⑫ customer concentration ⑬ balance-sheet / capex spiral ⑭ strong-cycle downturn (non-linear) ⑮ systemic (e.g. private-credit) ⑯ valuation itself too high ⑰ slow innovation/AI adoption ⑱ post-rally pullback / yield-ramp / can the turnaround become systemic ⑲ **the business model gets bypassed by a new paradigm** (e.g. AI agents reading raw data directly → the "middle layer" is skipped) — **⑦ must always include this paradigm-disruption layer**. ⚠️ **Don't pile on risks the company has already neutralized** (e.g. SBC offset by buybacks = controllable, not a standalone risk). **Tone: contextualize, don't catastrophize.** --- ## Productized conclusion: the rating card (see references/05-rating-system.md) 🔴 **Coverage gate first.** The free 5+2 write-up is the *understanding layer*; a full rating card (rating + tier + target + range) is the *paid/tracking layer*. **Only produce a rating card if the name is in the tracking universe.** For a name that isn't covered, end at "fairly valued + valuation range + risks to monitor" — don't fabricate a card (if the user explicitly wants a prediction, label it "⚠️ prediction, not yet covered"). - **Rating STRONG BUY / BUY / HOLD** (no SELL in the covered set) ≈ upside-to-target × conviction: STRONG BUY = high upside (≥~28%) + high conviction; BUY = moderate upside, or high upside suppressed by risk; HOLD = limited upside and/or thesis unproven. - **Tier 1–4 = risk/certainty tier** (driven by ⑦ + moat depth + balance-sheet safety): 1 = safest/deepest moat → 4 = most speculative. - **Target ∈ [DCF base, bull]**; the bear/bull range = the DCF bear/bull scenarios. ## Output template (research brief) ``` 📊 5+2 · {TICKER} {Name} ({industry}) 〔Archetype: {1-8 or universal fallback} | ⑤ valuation method: {forward PE / EV-Sales / SOTP-DCF / P-B×BPS / cap-headroom}〕 〔Coverage gate: only show the【Verdict】card if the name is covered; otherwise drop it, keep just the ⑤ valuation range + note "not yet covered"〕 【Verdict】{STRONG BUY/BUY/HOLD} · Tier {1-4} · Target ${} (range ${bear}–${bull}) · Price ${} ({+upside}%) ① 【Industry】 {key trend}; size {TAM/CAGR}; structure {…}; tailwind/headwind: {…} ② 【Business model】 how it earns: {…} | moat: {type + durability} (ROIC {x}%) | share {…} ③ 【Management】 {founder? technical? reputation? succession? track record?} ④ 【Financials】 {metrics by archetype} GM/efficiency {x}% (trend {↑/↓}) | net margin/ROTCE {x}% | LT-debt/assets {x}% ({safe?}) | FCF {+/−} ⑤ 【Valuation】 {method by type} fwd PE/EV-Sales {x}x vs market/own history | DCF/SOTP base/bull/bear → {under/fair/over} ────────── ⑥ 【Why buy】 {one-line bull + precondition} ⑦ 【Why not】 ① {…} ② {…} ③ {paradigm-disruption} (core: success rides on X, failure too) 💡 Takeaway: {his voice — balanced, humble} ⚠️ Data: factsheet.py @ {date}; {missing/qualitative items}. Reproduces an analytical framework, not investment advice; "5+2 is only the start." ``` ## Voice (see references/voice.md) Loves analogies ("selling shovels in a gold rush"), plain-spoken, a "takeaway" after each step, **always pairs opportunity with risk**, humble ("just a starting point, for reference"), often cites research houses (Grand View / ARK / Mizuho, etc.). ## Data & limits - The adapter needs only `pip install yfinance`; it collects facts, makes no calls. - ④⑤ lean on the adapter; ①②③ require **web search** (industry/moat/management need the latest qualitative info). - Always stamp the data date + missing items; close with "not investment advice". - Adapter provides: `statements.debt_to_assets_pct` (total debt incl. leases, <40%), `gross_margin_trend_q` (5q), `sbc_pct_revenue`, `valuation.ev_to_sales`, ROIC, reverse-DCF implied growth, analyst-target asymmetry. Not available: NRR, forward EPS (use web).