Agent Skills Hub

data-analysis

Data Analysis Specialist for EDA, statistical modeling, SQL queries, and Python-based visualization. Turn raw datasets into actionable insights through rigorous quantitative methods.

cohort-analysis

Perform cohort analysis on user engagement data. Identify retention trends, feature adoption rates, churn patterns, and generate actionable research recommendations through quantitative data analysis.

Data Analysis Helper

A versatile data analysis assistant for loading datasets, performing statistical calculations, visualizing trends, and generating professional summary reports.

python

Python coding assistant providing best practices, PEP 8 enforcement, automated testing with pytest, and dependency management using uv.

deepscan-monitor

Manage long-running PapersFlow DeepScan research workflows with asynchronous monitoring, live progress tracking, and automated report generation.

market-debrief-cn

A deep analysis tool for A-share markets generating interactive, FT-style HTML daily reports using multi-agent parallel architecture, AkShare data, and Tavily news.

neurokit2

Comprehensive biosignal processing toolkit for ECG, EEG, EDA, RSP, PPG, EMG, and EOG signal analysis, enabling psychophysiology research and multi-modal integration.

financial-analyzing

Analyze financial data, calculate key performance metrics like margins and ROI, and generate structured analytical reports.

statsmodels

Statistical modeling and econometrics library for Python. Performs OLS, GLM, mixed models, ARIMA, diagnostics, and inference for rigorous scientific analysis.

statistical-analysis

Guided statistical analysis with test selection, assumption checking, power analysis, and APA-formatted reporting for academic and experimental research.

networkx

Comprehensive toolkit for graph creation, network analysis, and visualization in Python. Ideal for analyzing relationships, centrality, community detection, and synthetic network generation across diverse research domains.

scikit-learn

Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.