Data

News Pulse dispatches parallel AI agents to investigate company events, regulatory changes, industry moves, and market sentiment when a stock price moves sharply, then produces an event timeline, a cause attribution judgment, and a thesis-review trigger check.

Portfolio Review parses current holdings, fetches latest market data, runs health checks, concentration and correlation analysis, stress tests, and opportunity-cost ranking, then produces rebalancing suggestions and a saved portfolio report. Use it to review or optimize positions like stocks and cash.

Quality Screen applies seven elimination indicators to quickly exclude companies that do not meet first-class standards. Use it for individual stocks, industries, indices, or themes to get a pass/fail summary and sector quality ranking.

Thesis Drift compares two investment thesis reports to detect whether the thesis has drifted due to verifiable fact changes or merely wording changes. Use it after earnings, regulatory filings, or major events to update buy/hold/sell rationale without mistaking price moves or paraphrasing for fundamental change.

Thesis Tracker verifies and tracks investment theses after purchase, checking assumptions, red lines, and valuation each quarter. Use it to build a thesis for a company or run a quarterly check against the latest financial data.

Spreadsheets creates, edits, and analyzes spreadsheet files (.xlsx, .xlsm, .csv, .tsv) using formulas, formatting standards, and data analysis. Use it for financial models, data cleaning, conversions, or any task where a spreadsheet is the primary deliverable.

Golden Chat Topics indexes a Discord chat export so an agent can find discussions, code snippets, and troubleshooting steps from past team conversations. Use it when you need to look up solutions, decisions, or links from the golden chat topics chat history.

Golden Jupyter documents code examples, dependencies, and section structure from a Python data analysis notebook, helping you reproduce its analysis steps. Use it to reference methodology, visualizations, and library usage patterns.

Golden Jupyter Dir is a Jupyter notebook reference skill generated from the golden-jupyter-dir build, covering setup, data loading, evaluation, and modeling results with code examples, dependencies, and section navigation.

Golden Jupyter Kw provides a generated reference for a Jupyter notebook covering setup, modeling, data loading, and code examples. Use it to understand concepts, review methodology, or reproduce analysis steps.

Golden Jupyter Topics documents a Jupyter notebook's sections, code examples, dependencies, and key concepts. Use it to understand or reproduce the golden_jupyter_topics analysis workflow.

Analyze Results analyzes ML experiment results, computes statistics, and generates comparison tables and insights. Use when the user says "analyze results", "compare", or needs to interpret experimental data.