Dividend Investing Guide
Plan purchases and track dividend progress
Description
A personal assistant for managing a dividend investing plan: It automatically creates and maintains a data profile document in the user’s own board, covering holdings, cost basis, budget, cumulative dividends, stock watchlist, goals, per-share dividend reference data, and transaction records. Based on this information, it generates monthly purchase instructions, quarterly checkups, automatic dividend-season records, progress cards, and annual reviews. Scheduled tasks can also send proactive reminders at key times, such as the first day of each month.
Related Skills
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Stock Review & Strategy v2.0
The Stock Investment Review Scoring and Strategy Optimization System reads the user's existing trading logs, account overview, position data, and market index information to automatically complete daily, weekly, monthly, or quarterly reviews. It not only calculates returns, positions, number of trades, and position changes, but also scores trading quality, operational discipline, account positioning, position control, risk handling, and strategy effectiveness. It identifies recurring trading issues and outputs executable stop-profit, stop-loss, position management, and strategy optimization suggestions for the next period.
Daily Stock Trade Log Generator
The Daily Stock Trade Log Generator creates structured daily stock trading logs based on uploaded real account screenshots, trade records, and position details. It organizes account overview, daily profit/loss, buy/sell operations, transaction costs, position details, market environment, and risk alerts. At the end of the log, it outputs standardized review interface data for subsequent weekly reviews, monthly summaries, and trading score analysis.
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.
Dividend Investing Guide
Plan purchases and track dividend progress
Description
A personal assistant for managing a dividend investing plan: It automatically creates and maintains a data profile document in the user’s own board, covering holdings, cost basis, budget, cumulative dividends, stock watchlist, goals, per-share dividend reference data, and transaction records. Based on this information, it generates monthly purchase instructions, quarterly checkups, automatic dividend-season records, progress cards, and annual reviews. Scheduled tasks can also send proactive reminders at key times, such as the first day of each month.
Related Skills
View allStock Review & Strategy v2.0
The Stock Investment Review Scoring and Strategy Optimization System reads the user's existing trading logs, account overview, position data, and market index information to automatically complete daily, weekly, monthly, or quarterly reviews. It not only calculates returns, positions, number of trades, and position changes, but also scores trading quality, operational discipline, account positioning, position control, risk handling, and strategy effectiveness. It identifies recurring trading issues and outputs executable stop-profit, stop-loss, position management, and strategy optimization suggestions for the next period.
Daily Stock Trade Log Generator
The Daily Stock Trade Log Generator creates structured daily stock trading logs based on uploaded real account screenshots, trade records, and position details. It organizes account overview, daily profit/loss, buy/sell operations, transaction costs, position details, market environment, and risk alerts. At the end of the log, it outputs standardized review interface data for subsequent weekly reviews, monthly summaries, and trading score analysis.
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.
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