Investment Notes
Featured by
nene@YouMind.AI
Why we love this skill
Say goodbye to blindly following the crowd! This skill uses in-depth scanning of real-time news and brokerage research reports across the entire internet to extract the core game-theoretic points of stocks. It transforms massive amounts of information into structured investment decision memos, supplemented by visual charts, to help you build highly reliable investment logic and make more informed investment choices.
Instructions
#### describe
Reject noisy, trend-following trading. We comprehensively cleanse and integrate real-time market news, in-depth brokerage research reports, and historical financial data. Through a closed loop of "broad search - in-depth reading - logical internalization," we build your own highly reliable investment notes, rather than simply acquiring a stock price figure.
#### Core Task
For users interested in **target stocks** such as NVIDIA (e.g., material stocks). The goal is to collect **latest financial report analyses** and **industry analyst opinions** from across the internet, deeply analyze them, extract **3-5 core game theory points (Bull vs. Bear)**, and ultimately generate a structured **investment decision memo** with **visualized data charts**.
First, confirm the investment target with the user.
#### Execution Steps
**Step 1: Market Scanning**
- **Objective:** To obtain the current mainstream narrative and sentiment in the market regarding this target.
- **Action**:
- **News Aggregator**: Uses search tools to crawl the most popular news about this stock from across the web over the past week.
- **Initial Viewpoint Screening**: Quickly identify whether market sentiment leans towards "optimism" or "panic" and mark the main events that cause sentiment fluctuations (such as: earnings releases, new product launches).
**Step 2: In-depth Research Report Reading**
- **Objective:** To penetrate noise and obtain deep logic at the institutional level.
- **Action**:
- **Material Acquisition**: Collect 3-5 in-depth long-form analyses or PDF research reports from the entire internet and save them as Material.
- **Core Extraction**: AI performs deep reading of these materials to extract "performance forecasts", "risk warnings", and "unique perspectives that differ from the consensus".
- **Logical Alignment**: Compare the contradictions between different research reports (e.g., Institution A is bullish because of AI demand, while Institution B is bearish because of production capacity bottlenecks).
**Step 3: Investment Notes Generation (Thesis Synthesis)**
- **Objective:** To transform external information into a basis for personal investment decisions.
- **Output**:
- **Core Game Theory Table**: Lists the top 3 reasons for going long and short in the current market.
- **Key Metric Tracking**: Identify the KPIs that need the most attention in the next quarter (e.g., data center revenue growth).
- **Decision Recommendation**: Based on the above analysis, generate a logical deduction document for "Buy/Hold/Wait" and then generate a visual data webpage.
Use write + webpage (for the data visualization part)
description
Analyze target stocks like a professional fund manager. By integrating news, research reports, and data, build highly reliable investment notes, accurately identify key game points, and generate visual decision-making memos.
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Investment Notes
Featured by
nene@YouMind.AI
Why we love this skill
Say goodbye to blindly following the crowd! This skill uses in-depth scanning of real-time news and brokerage research reports across the entire internet to extract the core game-theoretic points of stocks. It transforms massive amounts of information into structured investment decision memos, supplemented by visual charts, to help you build highly reliable investment logic and make more informed investment choices.
Instructions
#### describe
Reject noisy, trend-following trading. We comprehensively cleanse and integrate real-time market news, in-depth brokerage research reports, and historical financial data. Through a closed loop of "broad search - in-depth reading - logical internalization," we build your own highly reliable investment notes, rather than simply acquiring a stock price figure.
#### Core Task
For users interested in **target stocks** such as NVIDIA (e.g., material stocks). The goal is to collect **latest financial report analyses** and **industry analyst opinions** from across the internet, deeply analyze them, extract **3-5 core game theory points (Bull vs. Bear)**, and ultimately generate a structured **investment decision memo** with **visualized data charts**.
First, confirm the investment target with the user.
#### Execution Steps
**Step 1: Market Scanning**
- **Objective:** To obtain the current mainstream narrative and sentiment in the market regarding this target.
- **Action**:
- **News Aggregator**: Uses search tools to crawl the most popular news about this stock from across the web over the past week.
- **Initial Viewpoint Screening**: Quickly identify whether market sentiment leans towards "optimism" or "panic" and mark the main events that cause sentiment fluctuations (such as: earnings releases, new product launches).
**Step 2: In-depth Research Report Reading**
- **Objective:** To penetrate noise and obtain deep logic at the institutional level.
- **Action**:
- **Material Acquisition**: Collect 3-5 in-depth long-form analyses or PDF research reports from the entire internet and save them as Material.
- **Core Extraction**: AI performs deep reading of these materials to extract "performance forecasts", "risk warnings", and "unique perspectives that differ from the consensus".
- **Logical Alignment**: Compare the contradictions between different research reports (e.g., Institution A is bullish because of AI demand, while Institution B is bearish because of production capacity bottlenecks).
**Step 3: Investment Notes Generation (Thesis Synthesis)**
- **Objective:** To transform external information into a basis for personal investment decisions.
- **Output**:
- **Core Game Theory Table**: Lists the top 3 reasons for going long and short in the current market.
- **Key Metric Tracking**: Identify the KPIs that need the most attention in the next quarter (e.g., data center revenue growth).
- **Decision Recommendation**: Based on the above analysis, generate a logical deduction document for "Buy/Hold/Wait" and then generate a visual data webpage.
Use write + webpage (for the data visualization part)
description
Analyze target stocks like a professional fund manager. By integrating news, research reports, and data, build highly reliable investment notes, accurately identify key game points, and generate visual decision-making memos.
Related Skills
View allAcademic Paper Three-Track Polishing System v2.0
Native-speaker editor-level paper polishing, three-track customized approach directly meeting top journal review standards. Three independent engines precisely align with your target submission model: A. Journal Benchmarking: Locking onto the style guidelines of top journals like Nature and IEEE, utilizing the journal's unique sentence structure preferences (Nature prefers active voice + interdisciplinary readability, IEEE prefers passive voice + high-density technical vocabulary), ensuring your draft directly matches the target journal's standard model. B. Model Article Replication: In-depth analysis of the sentence structure, conjunction preferences, and writing rhythm of your favorite benchmark articles, making your manuscript infinitely close to the top journal's model in language style. C. Pure Refinement: Activating universal top journal standards, performing four-dimensional foundation polishing (grammar correction, word restructuring, intonation unification, extreme clarity), significantly enhancing academic tension and readability. Surgical-level block-by-block refinement, every change is visible and logically enforced. Forced block processing (500-800 words each time) breaks through the AI attention window limitation, polishing sentence by sentence without roughness. Dual-core adversarial engine (Core A language restructuring + ... (Core B Quality Control) Transparent annotation throughout, outputting 3-5 editor-in-chief level revision notes each time, explaining "why this revision aligns with top journal preferences." Absolutely faithful semantics: only changes the expression, not the core content; never tamper with data or invent new viewpoints for you. After opening, directly select the polishing mode (A Journal Benchmark/B Sample Replication/C Pure Refinement), then paste your paper in paragraphs (500-800 words per paragraph is recommended, such as the first half of the introduction). The system will refine each paragraph with editor-in-chief annotations. After confirming there are no errors, proceed to the next paragraph. Fully interactive guidance, no complex configuration required. The accompanying tool, the official polishing module of the "Academic Paper Full-Process Writing System v3.1 (Wuyuan × AFP)," forms a complete closed loop from topic selection to drafting to final polishing. Applicable scenarios: Papers requested by reviewers to be "native-language polished," papers aiming for top journals like Nature/Science, and master's/doctoral students and young scholars wanting to learn the writing style of top journals. Key differences: Several times cheaper than hiring a polishing company, several times more accurate than AI-powered one-shot revisions, and several times more efficient than blind self-editing.
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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