Remember

Tech Stock Earnings Deep Dive Analysis and Multi-Perspective Investment Memo System (v3.0). Covers 16 major analysis modules (A-P), 6 investment philosophy perspectives, institutional-grade evidence standards, anti-bias framework, and actionable decision system. When users mention topics such as tech company earnings analysis, quarterly/annual report interpretation, earnings call, revenue growth analysis, margin changes, guidance, valuation models, DCF, reverse DCF, EV/EBITDA, PEG, Rule of 40, management analysis, competitive landscape, position sizing, whether to buy/sell/add to a tech stock position, how to interpret a company's latest earnings, doing a deep dive, multi-angle valuation, how investment masters view a company, variant view, key forces, kill conditions, ownership structure, executive team, partner ecosystem, macro policy impact, etc., this skill should be used. Even if the user simply asks "help me look at NVDA's latest earnings" or "how did META do this quarter" or "sh

Analyze product screenshots to extract feature lists and generate development task checklists. Use when: (1) Analyzing competitor product screenshots for feature extraction, (2) Generating PRD/task lists from UI designs, (3) Batch analyzing multiple app screens, (4) Conducting competitive analysis from visual references.

Multi-asset correlation and cointegration analysis via Longbridge Securities — computes Pearson / Spearman return correlation matrix for 2–10 symbols, rolling 60-day correlation, Engle-Granger cointegration (ADF unit root), and spread half-life (AR(1) estimate). Used for portfolio decorrelation and pairs-trading pre-screening. Triggers: "相关性", "协整", "相关系数", "相关矩阵", "滚动相关", "去相关", "多标的相关", "相關性", "協整", "相關係數", "相關矩陣", "滾動相關", "去相關", "correlation", "cointegration", "correlation matrix", "rolling correlation", "Pearson", "Spearman", "decorrelation", "multi-asset correlation", "ADF test", "相关分析", "相關分析", "pairwise correlation".

Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or the loop cap is reached. Use when: implementing features from a spec or plan, hardening existing code, fixing a batch of issues, or any multi-file task that benefits from a build-verify-fix cycle.

Quantitative statistics framework for time-series analysis using Longbridge price data — ADF unit root test (stationarity), cointegration (Engle-Granger / Johansen), GARCH volatility modelling (conditional heteroskedasticity), regression diagnostics (Durbin-Watson / Breusch-Pagan), bootstrap confidence intervals, hypothesis tests (t-test / F-test). Requires statsmodels and scipy. Triggers: "量化统计", "ADF检验", "单位根", "协整检验", "GARCH", "自相关", "异方差", "Bootstrap", "假设检验", "量化統計", "ADF檢驗", "單位根", "協整檢驗", "異方差", "假設檢驗", "quantitative statistics", "ADF test", "unit root", "cointegration", "GARCH", "autocorrelation", "heteroskedasticity", "bootstrap", "hypothesis test", "statsmodels".

Estimate intrinsic value of stocks and companies using DCF, dividend discount models, comparable multiples, and residual income. Use when the user asks about discounted cash flow, DCF models, WACC, terminal value, dividend discount models, comparable multiples, or sum-of-the-parts valuation. Also trigger when users mention 'what is this stock worth', 'fair value estimate', 'Gordon growth model', 'free cash flow valuation', 'cost of equity', 'sensitivity analysis', 'exit multiple', or ask whether a stock is overvalued or undervalued.