Deep Researcher

Use this skill during research for lightweight forecast evidence analysis: base-rate checks, prediction-market anchors, scenario ranges, directional factor summaries, implied probabilities, and monitoring indicators. Triggers: "forecast", "prediction", "probability", "odds", "base rate", "scenario", "Polymarket", "prediction market", "will happen", "market-implied", "confidence interval", "price target", "expected value". Outputs: forecast evidence notes or compact forecast inputs returned in your ResearchNotes for writer synthesis.

Use this skill for converting researched facts or user-provided data into structured tables by writing code, then running Python/pandas calculations in the job-scoped sandbox. This skill is for numeric normalization, tabular analysis, rankings, growth rates, summary statistics, CSV/JSON generation, and markdown tables. Triggers: "compute table", "calculate growth", "normalize values", "extract figures", "rank companies", "QoQ", "YoY", "CAGR", "summary statistics", "CSV", "JSON", "markdown table", "standardize quarters", "standardize currencies", "compare over time". Outputs: Markdown tables, CSV text, JSON records, summary statistics, rankings, and data-quality notes.

Use this skill to turn researched or computed numeric data into source-grounded charts. It has two delivery modes and picks one from the tools available to you. When an `execute` tool (sandbox) is available, render a PNG chart plus its CSV with Python/matplotlib and embed it as a durable `artifact://` reference. When there is no sandbox, emit the chart as an inline fenced `chart` (or `chart-carousel`) JSON spec that the web UI renders deterministically, followed by a portable Markdown table. Triggers: "chart", "plot", "graph", "bar chart", "line chart", "visualize", "trend over time", "compare visually", "figure", "ranking", "top-N", "distribution". Outputs: either a PNG chart artifact (plus CSV and manifest) or an inline chart spec.

Use this skill for small deterministic calculations during research when pandas/table analysis is unnecessary. Triggers: "calculate", "arithmetic", "unit conversion", "percentage point", "expected value", "weighted average", "range", "ratio", "sanity check", "implied value", "probability conversion". Outputs: concise calculation notes, JSON snippets, or Markdown bullets returned in your ResearchNotes for later synthesis.

Use this skill when the final answer strategy calls for a long-form report, publication-quality research writeup, comprehensive analysis, deep dive, whitepaper-style narrative, or detailed cited Markdown report. Triggers: "long_form_report", "long-form report", "comprehensive report", "publication-ready", "deep dive", "in-depth analysis", "whitepaper", "research report", "detailed report", "full report". Outputs: A polished, cited Markdown report with coherent structure, analytical depth, source-grounded claims, limitations, and a sources section.

Use this skill when the final answer strategy calls for a prediction, forecast, probability estimate, price target, expected value, threshold outcome, scenario outlook, or prediction-style research report. This skill takes priority over general long-form writing skills for forecasts, stock-price outlooks, probabilities, odds, price targets, expected values, and next-period value estimates, even when the requested answer is concise. Triggers: "prediction", "forecast", "probability", "odds", "confidence interval", "price target", "expected value", "will X happen", "stock price", "market outlook", "scenario analysis", "threshold", "single forecast", "predict". Outputs: A cited Markdown prediction report with a clear forecast, confidence interval or range, executive summary, supporting and opposing factors, uncertainties, recommendations or implications, methodology note, and sources.