Data & Analytics

Research prediction markets as data sources or oracle signals for products, agents, dashboards, and corporate decision intelligence. Use for source-grounded analysis of market-implied probabilities, caveats, and integration patterns without investment advice.

SwiftUI 架构模式，使用 @Observable 进行状态管理，视图组合，导航，性能优化，以及现代 iOS/macOS UI 最佳实践。

@Observableを使用した状態管理、ビュー合成、ナビゲーション、パフォーマンス最適化、モダンなiOS/macOS UIのベストプラクティスを備えたSwiftUIアーキテクチャパターン。

生物医学文献、MeSH クエリ、PMID 検索、引用取得、および API を利用した文献モニタリングのための PubMed および NCBI E-utilities の直接検索ワークフロー。

Hosted biological-data-analysis agent (Finch) on the FutureHouse Platform. Hands a dataset + question to Finch, which builds a Jupyter notebook that explores, analyzes, and interprets the data. Use when the user has a biological dataset (omics, imaging, clinical) and a research question, and wants a multi-step analysis with code + results, not just a literature answer.

Generate publication-quality LaTeX tables from experimental results. Convert JSON/CSV data to booktabs-styled tables with bold best results, multi-row layouts, and proper captions. Use when creating result tables, comparison tables, or ablation tables for papers.

Design experiment plans with progressive stages — initial implementation, baseline tuning, creative research, and ablation studies. Plan baselines, datasets, hyperparameter sweeps, and evaluation metrics. Use when planning experiments for a research paper.

Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics. Triggers: "azure-monitor-query", "LogsQueryClient", "MetricsQueryClient", "Log Analytics", "Kusto queries", "Azure metrics".

Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text. Triggers: "text analytics", "sentiment analysis", "entity recognition", "key phrase", "PII detection", "TextAnalyticsClient".

Azure Monitor Query SDK for Java. Execute Kusto queries against Log Analytics workspaces and query metrics from Azure resources. Triggers: "LogsQueryClient java", "MetricsQueryClient java", "kusto query java", "log analytics java", "azure monitor query java". Note: This package is deprecated. Migrate to azure-monitor-query-logs and azure-monitor-query-metrics.

Generate publication-quality LaTeX tables from experimental results. Convert JSON/CSV data to booktabs-styled tables with bold best results, multi-row layouts, and proper captions. Use when creating result tables, comparison tables, or ablation tables for papers.

Design experiment plans with progressive stages — initial implementation, baseline tuning, creative research, and ablation studies. Plan baselines, datasets, hyperparameter sweeps, and evaluation metrics. Use when planning experiments for a research paper.

Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.

Generate publication-quality figures and tables from experiment results. Use when user says \"画图\", \"作图\", \"generate figures\", \"paper figures\", or needs plots for a paper.

> Dense, machine-readable reference for LLMs. No prose padding. > Version: matches latest PyFixest release.

End-to-end data analysis workflow in R or Python — from exploration through regression to publication-ready tables and figures. Make sure to use this skill whenever the user wants to run any empirical analysis, write analysis code, or produce output from data. Triggers include: "analyze this data", "run a regression", "write R code for this", "write Python code for this", "I have a dataset", "help me with this regression", "run a DiD", "run an RDD", "event study", "IV regression", "fit a mode...

Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece...

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and...

Hosted biological-data-analysis agent (Finch) on the FutureHouse Platform. Hands a dataset + question to Finch, which builds a Jupyter notebook that explores, analyzes, and interprets the data. Use when the user has a biological dataset (omics, imaging, clinical) and a research question, and wants a multi-step analysis with code + results, not just a literature answer.

Evaluates the ability of topological data analysis methods to capture developmental transitions and cell lineage dynamics in single-cell RNA sequencing time-series data. It specifically tests whether higher-order simplicial complexity can outperform conventional topological invariants like Betti numbers in identifying critical biological stages. Use when the user wants to benchmark on Farrell et al. (2018) zebrafish scRNA-seq, or asks about evaluating this task. Reports normalized simplicial ...

Evaluates ML inference accelerators under realistic Extended Reality (XR) workloads. It probes the system's ability to handle real-time, multi-task, multi-model (MTMM) pipelines with dynamic dependencies while meeting strict latency, energy, and quality-of-experience (QoE) constraints. Use when the user wants to benchmark on XRBench Scenarios, or asks about evaluating this task. Reports overall score.

This evaluation probes the effectiveness of explainable AI (XAI)-driven feature selection methods on network intrusion detection systems. It measures how well various black-box machine learning models classify network traffic flows into normal or specific attack categories when trained on different subsets of extracted features. Use when the user wants to benchmark on CICIDS-2017, RoEduNet-SIMARGL2021, or asks about evaluating this task. Reports Accuracy (Acc).

Evaluates machine learning classifiers for binary and multiclass intrusion detection in Wireless Sensor Networks (WSNs), specifically testing their robustness on imbalanced datasets and the impact of SMOTETomek resampling. Use when the user wants to benchmark on Large-scale WSN dataset, or asks about evaluating this task. Reports Accuracy.

Evaluates machine translation metrics by measuring their alignment with human judgments at segment and system levels. It probes whether metrics can correctly rank translations and systems based on quality, and tests the robustness of meta-evaluation statistics like correlation and pairwise ranking accuracy. Use when the user wants to benchmark on WMT Test Sets, or asks about evaluating this task. Reports Pearson correlation.