Inspect an agent’s current model, processing limits, workspace, tools, and message routing details. Diagnose unavailable capabilities, adjust permitted runtime settings, or keep temporary session notes, with advance notice before changes that affect the model or limits.
Read, create, and edit Excel workbooks, including formulas, formatting, charts, and multiple sheets. Clean CSV or TSV tables and prepare spreadsheet deliverables, with guidance on choosing fixed calculated values or formulas that update later.
Set up and manage DeepTutor from the command line, a text interface for running commands. Run learning tasks, build searchable document collections, manage sessions and notebooks, diagnose installation issues, and launch the server or Web app.
Read, summarize, create, and edit Word documents in .docx format. Build reports with headings, tables, and images, replace text in existing files, or add tracked changes and comments for document review.
Create PowerPoint presentations from outlines, update text, tables, charts, and images in existing slides, or extract slide text and speaker notes. Export decks to PDF or images when the required conversion tools are available.
Extract text and tables from PDFs, create new documents, and reorganize pages. Use it to merge or split files, fill forms, add watermarks or password protection, and turn pages into images for visual inspection.
Design and improve DeepTutor skills by turning concrete usage examples into reusable instructions. Learn how to write descriptions that signal when to use a skill, structure SKILL.md, and separate supporting reference material.
Build a pair trading strategy for two related instruments using their price ratio. Measure how far the ratio moves from its recent average, generate opposite buying and selling signals, and close positions when the ratio returns near that average.
Analyze crypto perpetual futures funding payments and spot–futures price differences to assess carry trades. Compare exchanges, identify crowded positions, and summarize potential returns alongside risks such as funding reversals and liquidation.
Check investment research for bias before searching and before writing conclusions. A short checklist broadens coverage beyond large companies and English sources, challenges bullish assumptions, and checks whether important figures are current.
Apply statistical tests to financial time series, model changing volatility, and diagnose regression problems. Estimate uncertainty in strategy metrics through bootstrap resampling, which repeatedly samples existing data, and organize findings into a statistical testing report.
Evaluate companies using projected cash flows, dividends, business segments, and comparisons with peers. Select suitable valuation methods, examine how assumptions change estimated value, and check for problems that can make a seemingly cheap stock misleading.
Finds and retrieves paid financial datasets when free sources lack coverage or detail. Guides comparison of providers, success rates, prices, and required inputs, while keeping execution costs and remaining credits visible.
Document why you own a stock, the assumptions behind the purchase, and conditions for reconsidering it. Review these against quarterly earnings and major events, score the investment rationale's health, and outline whether to hold, add, trim, or exit.
Collect financial discussions from Twitter/X, Telegram, Discord, and Reddit, then measure sentiment and discussion volume. Use the resulting data to study market attention, distinguish author groups, and test whether social signals relate to future returns.
Turn trading rules into Python code and configuration files, then test them against historical data. Review performance, position sizing, and trading costs, and revise strategies using the results produced by the backtest engine.
Retrieve price and trading volume data for US, Hong Kong, and Canadian stocks, ETFs, and indices through Yahoo Finance. Also gather company profiles, financial statements, dividends, and ownership data for market research and historical strategy testing.
Retrieve financial data through Tushare using Python. Find relevant APIs for Chinese stocks, ETFs, funds, bonds, economic indicators, and news, then use documented parameters to obtain pandas DataFrames, tables ready for further analysis.
Analyze the volatility implied by option prices across strike prices and expiration dates. Compare calendar spreads and other volatility strategies, then assess portfolio risk using Greeks, measures of how option values respond to market changes.
Assess market sentiment using fear and greed measures, options activity, margin trading, foreign investment flows, and social media discussion. Combine indicators on a common scale to produce a dashboard and explain signs of excessive optimism or fear.
Backtest stock and cryptocurrency option strategies using theoretical prices derived from underlying prices. Simulate combinations of option positions, track profit and loss, and export Greeks, measures of sensitivity to price, volatility, and time.
Manage multi-step financial research with a clear objective, acceptance criteria, and linked evidence. Track findings across turns, audit whether each criterion is supported, and report unresolved gaps while keeping the workflow limited to research.
Explain portfolio performance by separating the effects of sector allocation, stock selection, market factors, and timing. Compare results with a benchmark, assess risk-adjusted returns, and prepare a report showing where excess returns came from.
Analyze scheduled token releases, their recipients, and trading volume to estimate potential selling pressure. Review project treasury holdings, operating runway, and token supply growth to assess funding sustainability and the effect of dilution on staking yields.
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