Do the Right Things - موجّه تحليل بالذكاء الاصطناعي

Use this Duan Yongping rule prompt to apply “افعل الأشياء الصحيحة” to a specific company. It turns a vague opinion into a repeatable checklist: what facts you must verify, which assumptions matter most, what would invalidate the thesis, and the common misreads that create false certainty. Expect a written output you can save: a thesis summary, key risks, and next-step questions for filings and earnings calls. If a claim matters, require primary-source citations before you act. Educational only — not investment advice.

الموجّه الكامل

أنت محلل استثماري مدرّب على مبدأ Duan Yongping: "Do the Right Things". مهمتك تحليل {اسم الشركة} من خلال هذا المنظور المحدد.

## السياق
يعلّم Duan Yongping: "The most important thing is to do the right thing, then do things right. Many people focus on efficiency while doing the wrong thing. First make sure you're on the right path."

## إطار التحليل

### 1. تقييم تطبيق المبدأ
- كيف ينطبق هذا المبدأ تحديداً على {اسم الشركة}؟
- ما جوانب الشركة الأكثر صلة بـ"Do the Right Things"؟
- قيّم التوافق: قوي / متوسط / ضعيف
- على ماذا سيركز Duan Yongping أولاً؟

### 2. الأدلة الكمية
- حدد 3-5 مؤشرات مالية رئيسية ذات صلة
- حلل هذه المؤشرات خلال السنوات 5-10 الماضية
- قارن مع المنافسين والمعايير التاريخية
- هل الأرقام تتحسن أم مستقرة أم تتدهور؟

### 3. التحليل النوعي
- قيّم العوامل غير القابلة للقياس التي سيفحصها Duan Yongping
- جودة الإدارة وتوافقها مع هذا المبدأ
- ديناميكيات الصناعة والموقف التنافسي
- استدامة نموذج الأعمال من هذا المنظور

### 4. تقييم المخاطر
- ما المخاطر التي يبرزها هذا المبدأ لـ{اسم الشركة}؟
- ما إشارات التحذير التي سيحددها Duan Yongping؟
- اختبار الضغط: كيف ستؤدي الشركة في ظروف معاكسة؟
- ما أسوأ سيناريو من منظور هذا المبدأ؟

### 5. تحديد الفرص
- ما الفرص التي يكشفها هذا التحليل؟
- هل هناك نقاط قوة مخفية قد يقلل السوق من قيمتها؟
- ما المحفزات التي قد تطلق القيمة؟

### 6. Duan Verdict
- هل تجتاز {اسم الشركة} اختبار "Do the Right Things"؟
- التقييم: 1-10
- توصية واضحة: شراء / احتفاظ / تجنب
- ملخص في فقرة واحدة

## تنسيق المخرجات
قدم بيانات محددة في كل قسم. اختم بحكم حاسم.

Related reading (close the loop)

Pick one path below to turn the output into a checkable, repeatable decision policy.

Educational only. Verify facts with primary sources and apply your own constraints.

ℹ️هذا المحتوى متاح حاليًا باللغتين الصينية والإنجليزية فقط.

Basic Questions

What's the difference between Duan's 'do right things' and 'do things right'?
Core idea: first ensure you're doing the right thing, then do the thing right

✅ Using this AI prompt, you can systematically analyze any company or investment opportunity from this principle's perspective.

The prompt guides you to:
1. Assess whether the investment target meets this principle's core requirements
2. Identify key risks and blind spots
3. Provide a 1-10 comprehensive rating

Start by analyzing companies you know well for practice, then apply the framework to new investment decisions.

Usage Tips

Is the AI's 1-10 rating reliable?
⚠️ The rating may miss 'doing the right things' — the most fundamental standard.

The rating's value:
- If the AI detects obvious integrity issues or ethical risks, the score will reflect it
- Helps you reassess a company from 'right vs. wrong' rather than 'profit vs. loss'
- Can be used as an elimination tool — companies 'doing wrong things' should be excluded regardless of other dimensions

Key limitations:
- Duan's 'right things' is a values judgment that AI can't truly grasp
- Whether management is 'Ben Fen' (dutiful) requires long-term observation and interpersonal perception
- Many companies look good on the surface but secretly do 'wrong things' (accounting manipulation, excessive marketing) — AI may be deceived by surface data

✅ Right approach: Use the AI for preliminary ethical risk screening, but the final judgment on 'Is this company doing the right things?' must come from your own values and long-term observation.

Getting started

Does this prompt give investment advice or buy/sell calls?
No. It is a research helper that turns your thinking into checkable inputs and constraints: what evidence you must verify, what would prove the thesis wrong, and what common misreads to avoid. Treat the output as a draft, not a signal. Validate every material number against primary sources (filings, earnings releases, investor presentations, transcripts), and do not act unless you can write down (1) position-size limits and (2) explicit invalidation triggers.
What inputs should I provide for a reliable result?
At minimum: a 1-sentence business model summary, your current thesis (why it wins/loses), time horizon, and risk constraints; a valuation/price range; and the latest financial statements (profit quality, cash flow, debt/liquidity). Add context that reduces hallucinations: the exact filing period, known one-offs, key competitors, and what you do NOT know yet. If an input is missing, label it as missing evidence instead of letting the model guess.

Validation and boundaries

How do I validate the output?
Validate falsifiable claims one by one. Rewrite each key statement into something you can check: the metric, the period, and the source. Numbers must match filings; management claims must be traceable to transcripts/guidance; and “moat” claims need observable evidence (pricing power, retention, switching costs, cost structure). Anything you cannot verify becomes a follow-up task, not a decision trigger. If the model cites dates, confirm they are not beyond its knowledge cutoff.
When should I NOT act on the output?
If you cannot write down invalidation triggers, a position-size cap, or primary-source evidence for the key claims behind “افعل الأشياء الصحيحة”, do not act. The safer move is usually to reduce size, slow down, and schedule the next review.

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