Investment Notes
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
Recommended by
nene@YouMind
Why we love this skill
Say goodbye to blind following. This skill extracts core stock competitive points from real-time news and research. It turns vast data into a visual decision memo, helping you build high-confidence investment logic and make smarter choices.
Analyze target stocks like a professional fund manager. By integrating news, research reports, and data, build high-confidence investment notes, precisely identify key contention points, and generate visual decision memos.
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ResearchSenior Stock Value Analyst
Like a top professional investment analyst, this skill analyzes A-share investment value in depth. From fundamentals and valuation to technical analysis, it generates a structured report with one click to help you make precise decisions.
ResearchMulti-Agent: A-Share Pick & IC
It's not an AI assistant, but a virtual investment research team. Common AI stock-picking tools suffer from three problems: fabricating financial figures and target prices, giving vague "bullish/bearish" remarks, and offering "buy" recommendations without clear reasoning. The Multi-Agent Investment Research Team tackles these with a three-pronged approach: 6 parallel roles, cross-validation, and mandatory source attribution. It convenes researchers, fundamental analysts, technical analysts, sentiment analysts, risk officers, and investment managers to work in parallel, deliberating like a real investment committee. What you get is not fuzzy opinions, but a professional research document with facts, signals, disagreements, risks, and every number traceable to its source. Two modes covering "researching a single stock" and "screening a batch of stocks" Mode A: Single-Stock Committee Deep Analysis — Just provide a stock (e.g., "Analyze BYD 002594"), and the skill automatically convenes a full investment committee: the researcher aggregates market data, financial reports, research reports, and industry chain positioning, presenting only objective facts; the fundamental analyst issues a financial health scorecard, key changes in the three financial statements, and PEG valuation; the technical analyst evaluates trends, moving averages, MACD, support and resistance levels, and provides a five-point buy signal hit table; the sentiment analyst scans institutional divergence, retail investor sentiment, and potential misinterpretations; the risk officer digs up counter-evidence, systematically refuting optimistic conclusions from other roles; finally, the investment manager, without adding new data, integrates everything to produce committee minutes and a one-page summary. Mode B: Multi-Condition Stock Screening — From a specified universe (e.g., CSI 300, a sector/theme basket, or your own stock pool), apply a three-layer funnel: L1 financial hard screen (three consecutive quarters of growth, ample cash flow, PEG<1 or huge increase in contract liabilities), L2 technical timing (base breakout, moving average golden cross, volume breakout, strong pullback on low volume, MACD crossing above zero line), L3 information validation (research report ratings and industry chain logic, eliminating "pure technical without fundamental basis" picks). After obtaining a candidate list, the top N stocks can automatically proceed to Mode A for deep analysis. What you will get Mode A delivers a fixed "five-piece set": ① Full analysis report integrating all six roles; ② Data source and evidence table, with each key conclusion mapped to "data → source → date"; ③ Meeting-style committee minutes (agenda → each role's view → disagreements → consensus → variables to track); ④ Risk list sorted by high/medium/low severity; ⑤ One-page investment manager summary condensing core logic, key variables, verification points, and confidence level. Mode B delivers: Candidate stock list table (ticker | name | triggered conditions | key data | source | trigger date) plus screening criteria and methodology description, optionally with the full five-piece set for top candidates. All outputs are saved as files with ticker and date in the filename for easy reuse and archiving.
Stock Report Analyzer
Financial reports are a listed company's "physical exam report", but most investors feel lost when faced with pages of dense numbers: • 📝 Can't read the three financial statements — What do the balance sheet, income statement, and cash flow statement tell you? Which numbers matter most? • 🤔 Don't know what to look at — With dozens of pages, which key metrics should you focus on? • 📊 Lack an analysis framework — Even when you find the data, how do you judge good from bad and cross-check it? • 🏭 Industry standards differ — What counts as "good" varies completely by industry; manufacturing and internet companies can't be measured with the same yardstick. • ⏰ Time cost is too high — Manually looking up data, comparing peers, and calculating growth rates can take hours for one report. This skill was built to solve exactly these problems. It automates a professional financial analyst's framework, so you just need to enter a stock code to get a structured, logical, and beginner-friendly financial report interpretation. The tool's core value lies in its rigorous analysis framework, covering multiple dimensions from assessing growth quality to analyzing business models. It not only compares core metrics like revenue, gross margin, net margin, and cash flow, but also runs a quick Q&A on the seven key financial questions investors care about most. It clearly shows whether profits are turning into real cash, and whether receivables and inventory carry potential risks. By benchmarking against industry leaders, it helps you identify a company's true position in its industry chain. When generating the report, it also evaluates valuation levels, core strengths, and potential risks, and offers data-driven recommendations on what to watch. All analysis is presented in intuitive tables and plain, easy-to-understand language, turning complex accounting terms into concrete investment logic. Whether for daily review or deep research, it helps you quickly zero in on the core issue amid massive amounts of data, improving the efficiency and accuracy of your investment decisions.
Investment Notes
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
Recommended by
nene@YouMind
Why we love this skill
Say goodbye to blind following. This skill extracts core stock competitive points from real-time news and research. It turns vast data into a visual decision memo, helping you build high-confidence investment logic and make smarter choices.
Analyze target stocks like a professional fund manager. By integrating news, research reports, and data, build high-confidence investment notes, precisely identify key contention points, and generate visual decision memos.
Related Skills
View allResearchSenior Stock Value Analyst
Like a top professional investment analyst, this skill analyzes A-share investment value in depth. From fundamentals and valuation to technical analysis, it generates a structured report with one click to help you make precise decisions.
ResearchMulti-Agent: A-Share Pick & IC
It's not an AI assistant, but a virtual investment research team. Common AI stock-picking tools suffer from three problems: fabricating financial figures and target prices, giving vague "bullish/bearish" remarks, and offering "buy" recommendations without clear reasoning. The Multi-Agent Investment Research Team tackles these with a three-pronged approach: 6 parallel roles, cross-validation, and mandatory source attribution. It convenes researchers, fundamental analysts, technical analysts, sentiment analysts, risk officers, and investment managers to work in parallel, deliberating like a real investment committee. What you get is not fuzzy opinions, but a professional research document with facts, signals, disagreements, risks, and every number traceable to its source. Two modes covering "researching a single stock" and "screening a batch of stocks" Mode A: Single-Stock Committee Deep Analysis — Just provide a stock (e.g., "Analyze BYD 002594"), and the skill automatically convenes a full investment committee: the researcher aggregates market data, financial reports, research reports, and industry chain positioning, presenting only objective facts; the fundamental analyst issues a financial health scorecard, key changes in the three financial statements, and PEG valuation; the technical analyst evaluates trends, moving averages, MACD, support and resistance levels, and provides a five-point buy signal hit table; the sentiment analyst scans institutional divergence, retail investor sentiment, and potential misinterpretations; the risk officer digs up counter-evidence, systematically refuting optimistic conclusions from other roles; finally, the investment manager, without adding new data, integrates everything to produce committee minutes and a one-page summary. Mode B: Multi-Condition Stock Screening — From a specified universe (e.g., CSI 300, a sector/theme basket, or your own stock pool), apply a three-layer funnel: L1 financial hard screen (three consecutive quarters of growth, ample cash flow, PEG<1 or huge increase in contract liabilities), L2 technical timing (base breakout, moving average golden cross, volume breakout, strong pullback on low volume, MACD crossing above zero line), L3 information validation (research report ratings and industry chain logic, eliminating "pure technical without fundamental basis" picks). After obtaining a candidate list, the top N stocks can automatically proceed to Mode A for deep analysis. What you will get Mode A delivers a fixed "five-piece set": ① Full analysis report integrating all six roles; ② Data source and evidence table, with each key conclusion mapped to "data → source → date"; ③ Meeting-style committee minutes (agenda → each role's view → disagreements → consensus → variables to track); ④ Risk list sorted by high/medium/low severity; ⑤ One-page investment manager summary condensing core logic, key variables, verification points, and confidence level. Mode B delivers: Candidate stock list table (ticker | name | triggered conditions | key data | source | trigger date) plus screening criteria and methodology description, optionally with the full five-piece set for top candidates. All outputs are saved as files with ticker and date in the filename for easy reuse and archiving.
Stock Report Analyzer
Financial reports are a listed company's "physical exam report", but most investors feel lost when faced with pages of dense numbers: • 📝 Can't read the three financial statements — What do the balance sheet, income statement, and cash flow statement tell you? Which numbers matter most? • 🤔 Don't know what to look at — With dozens of pages, which key metrics should you focus on? • 📊 Lack an analysis framework — Even when you find the data, how do you judge good from bad and cross-check it? • 🏭 Industry standards differ — What counts as "good" varies completely by industry; manufacturing and internet companies can't be measured with the same yardstick. • ⏰ Time cost is too high — Manually looking up data, comparing peers, and calculating growth rates can take hours for one report. This skill was built to solve exactly these problems. It automates a professional financial analyst's framework, so you just need to enter a stock code to get a structured, logical, and beginner-friendly financial report interpretation. The tool's core value lies in its rigorous analysis framework, covering multiple dimensions from assessing growth quality to analyzing business models. It not only compares core metrics like revenue, gross margin, net margin, and cash flow, but also runs a quick Q&A on the seven key financial questions investors care about most. It clearly shows whether profits are turning into real cash, and whether receivables and inventory carry potential risks. By benchmarking against industry leaders, it helps you identify a company's true position in its industry chain. When generating the report, it also evaluates valuation levels, core strengths, and potential risks, and offers data-driven recommendations on what to watch. All analysis is presented in intuitive tables and plain, easy-to-understand language, turning complex accounting terms into concrete investment logic. Whether for daily review or deep research, it helps you quickly zero in on the core issue amid massive amounts of data, improving the efficiency and accuracy of your investment decisions.
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