Investment memo
Featured by
nene@YouMind.AI
Why we love this skill
Cut through market noise and build high-conviction investment memos. This skill goes beyond simple stock prices, deeply analyzing real-time news, brokerage reports, and financial data. It extracts key bull vs. bear arguments and generates structured investment decisions with visual charts, empowering you to make informed choices for your target stocks.
Instructions
#### Description
Reject noise trading driven by market herd mentality. Perform comprehensive cleansing and integration of real-time market news, in-depth brokerage research reports, and historical financial data. Through a closed-loop process of "broad search → deep reading → logic internalization," build your own high-conviction investment notes—rather than merely obtaining a stock price number.
#### Core Task
For the **"Target Stock" $material (e.g., NVIDIA)** that the user is focused on. Goal: Collect **"Latest Earnings Analysis"** and **"Industry Analyst Perspectives"** from across the web, conduct deep reading to extract **3-5 key points of contention (Bull vs Bear)**, and ultimately generate a structured **"Investment Decision Memo"** with **visual data charts**.
Confirm the investment target with the user first.
#### Execution Steps
**Step 1: Broad Market Scanning**
- **Objective**: Capture the market's prevailing narratives and sentiment toward the target.
- **Actions**:
- **News Aggregation**: Use the Search tool to crawl high-engagement news about the stock from the past week across the web.
- **Sentiment Screening**: Quickly identify whether market sentiment leans "bullish" or "fearful," and flag the main events driving sentiment fluctuations (e.g., earnings release, new product launch).
**Step 2: Deep Research Report Reading**
- **Objective**: Cut through the noise to obtain institutional-grade analytical logic.
- **Actions**:
- **Material Acquisition**: Collect 3-5 in-depth long-form analyses or PDF research reports from across the web, and Save them as Materials.
- **Core Extraction**: AI conducts deep reading of these materials, distilling "earnings forecasts," "risk warnings," and "unique perspectives that differ from consensus."
- **Logic Alignment**: Compare contradictions between different research reports (e.g., Firm A is bullish due to AI demand; Firm B is bearish due to capacity bottlenecks).
**Step 3: Investment Note Generation (Thesis Synthesis)**
- **Objective**: Transform external information into personal investment decision criteria.
- **Output**:
- **Key Thesis Table**: List the Top 3 reasons for the current market's bull and bear cases.
- **Key Metrics Tracking**: Flag the KPIs most critical to monitor next quarter (e.g., data center revenue growth rate).
- **Decision Recommendation**: Based on the above analysis, generate a logic-driven document with "Buy/Hold/Watch" recommendations.
then create a visual data webpage for data presentation.
description
Transform raw market data into high-conviction investment notes. Get deep analysis, bull/bear cases, and clear recommendations with visual charts, helping you make informed decisions.
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Investment memo
Featured by
nene@YouMind.AI
Why we love this skill
Cut through market noise and build high-conviction investment memos. This skill goes beyond simple stock prices, deeply analyzing real-time news, brokerage reports, and financial data. It extracts key bull vs. bear arguments and generates structured investment decisions with visual charts, empowering you to make informed choices for your target stocks.
Instructions
#### Description
Reject noise trading driven by market herd mentality. Perform comprehensive cleansing and integration of real-time market news, in-depth brokerage research reports, and historical financial data. Through a closed-loop process of "broad search → deep reading → logic internalization," build your own high-conviction investment notes—rather than merely obtaining a stock price number.
#### Core Task
For the **"Target Stock" $material (e.g., NVIDIA)** that the user is focused on. Goal: Collect **"Latest Earnings Analysis"** and **"Industry Analyst Perspectives"** from across the web, conduct deep reading to extract **3-5 key points of contention (Bull vs Bear)**, and ultimately generate a structured **"Investment Decision Memo"** with **visual data charts**.
Confirm the investment target with the user first.
#### Execution Steps
**Step 1: Broad Market Scanning**
- **Objective**: Capture the market's prevailing narratives and sentiment toward the target.
- **Actions**:
- **News Aggregation**: Use the Search tool to crawl high-engagement news about the stock from the past week across the web.
- **Sentiment Screening**: Quickly identify whether market sentiment leans "bullish" or "fearful," and flag the main events driving sentiment fluctuations (e.g., earnings release, new product launch).
**Step 2: Deep Research Report Reading**
- **Objective**: Cut through the noise to obtain institutional-grade analytical logic.
- **Actions**:
- **Material Acquisition**: Collect 3-5 in-depth long-form analyses or PDF research reports from across the web, and Save them as Materials.
- **Core Extraction**: AI conducts deep reading of these materials, distilling "earnings forecasts," "risk warnings," and "unique perspectives that differ from consensus."
- **Logic Alignment**: Compare contradictions between different research reports (e.g., Firm A is bullish due to AI demand; Firm B is bearish due to capacity bottlenecks).
**Step 3: Investment Note Generation (Thesis Synthesis)**
- **Objective**: Transform external information into personal investment decision criteria.
- **Output**:
- **Key Thesis Table**: List the Top 3 reasons for the current market's bull and bear cases.
- **Key Metrics Tracking**: Flag the KPIs most critical to monitor next quarter (e.g., data center revenue growth rate).
- **Decision Recommendation**: Based on the above analysis, generate a logic-driven document with "Buy/Hold/Watch" recommendations.
then create a visual data webpage for data presentation.
description
Transform raw market data into high-conviction investment notes. Get deep analysis, bull/bear cases, and clear recommendations with visual charts, helping you make informed decisions.
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Academic Paper Writing System v3.0 (Wuyuan×AFP)
This academic paper writing system, integrating the Five-Source Model and AFP framework, provides a one-stop solution for the entire academic writing process, from initial observation to completion. ✅ Seven core modules: Topic Selection & Introduction → Literature Review → Research Methods → Discussion → Conclusion → Abstract & Keywords (new in v3.1) → Full Text Integration. Each stage is constrained by the Five-Source Model (structure + materials + style + integration + calibration). ✅ Stage-based diagnosis: The system automatically identifies your current writing stage (starting from scratch/already having a topic/already having a review, etc.) and jumps directly to the corresponding module, eliminating the need to start from the beginning. ✅ Anti-illusion firewall: It mandates the submission of real literature/data, with the B-core having veto power to reject any "illusionary content" without supporting materials, ensuring academic rigor. ✅ Interdisciplinary adaptation: It automatically identifies quantitative, qualitative, and speculative research paradigms and switches to corresponding writing strategies (e.g., quantitative analysis emphasizes "variable conflicts," while qualitative analysis emphasizes "failure of contextual explanatory power"), adapting to all disciplines from humanities and social sciences to STEM fields. After opening, simply tell the system "Which stage am I at?" + "My subject area" to start. The system will guide you through the process of submitting materials (literature/data/research ideas). Each completed module automatically generates usable chapter content, and finally, all are integrated into a complete paper with a single click. User feedback: 40% increase in C-level journal/SCI submission acceptance rate. v3.1 Major upgrade: Added Phase 6 abstract and keyword generation module, achieving a true closed-loop process from "topic selection → manuscript completion → abstract".
Thesis Topic Selection and Introduction Writing System v3.0 (Five-Source Model × AFP)
From vague ideas to complete topic selection, and then to high-quality introductions, the entire process is designed to prevent illusions. ✅ Stage-based diagnostic positioning—Whether you are in the stage of vague observation, research unit refinement, theoretical matching, or introduction writing, the system automatically identifies and starts from the corresponding stage, without having to start from scratch. ✅ Four-core collaborative quality control—A core generates content, B core reviews and rejects (with veto power!), C core evaluates innovativeness, and D core monitors the entire process, ensuring that every output conforms to academic norms. ✅ Anti-illusion firewall—Forced feeding of real literature to generate introductions, rejecting AI-fabricated citations, and all references must be traceable. ✅ Automatic switching of interdisciplinary methodologies—After recognizing your professional background, the system automatically calls the corresponding methodology (humanities and social sciences: Q-method, fantasy topic analysis; science, engineering, agriculture, and medicine: machine learning, multi-omics analysis), without requiring you to understand the details of the methodology. After opening the system and answering 3 questions (professional background/current stage/target journal), the system automatically determines which step to start from. Prepare the prefaces (including references) of 3-5 papers on the same topic as reference materials, and the system will generate a draft introduction that conforms to journal specifications based on real literature. The entire process involves collaboration among four core experts, with a second-tier expert reviewing at key stages. Submissions that fail to meet standards are immediately rejected and require revision. The program covers all disciplines in the humanities, social sciences, science, engineering, agriculture, and medicine, and is compatible with submissions to journals at all levels, including CSSCI, SCI, and Peking University core journals.
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