Pandas plugins

Automate creation, editing, formatting, extraction, and manipulation of Excel spreadsheets, Word documents, PowerPoint presentations, and PDFs. Build professional spreadsheets with financial standards and zero formula errors, analyze doc content via XML and conversions, generate slide decks and thumbnails, process PDFs with OCR, merging, encryption, and form handling.

Automate office document workflows by creating, editing, analyzing DOCX/PPTX/PDF/XLSX files, processing Google Sheets/Slides via OAuth-enabled Python CLI, extracting text/tables to Markdown/CSV/JSON/Pandas, converting formats, and enforcing Excel standards for reports.

Execute 58 bioinformatics AI agent skills on genetic files (VCF, FASTQ, h5ad, proteomics) for pharmacogenomics, ancestry inference, scRNA-seq analysis, metagenomics profiling, variant annotation, GWAS, and clinical reporting. Generate reproducible markdown reports, plots, CSV/JSON outputs, and bundled environments via deterministic local Python runs with privacy safeguards.

Automate Chinese research paper workflows: brainstorm topics and structures interactively, synthesize literature from PubMed/arXiv with BibTeX, generate Python data visualizations/stats code using matplotlib/seaborn/scipy, draft evidence-driven chapters, peer-review with checklists, polish bilingual text, and compile publication-ready LaTeX with journal templates.

Equip AI agents to fully manage Microsoft Dataverse workflows: automate environment setup and connections via PAC CLI, handle solution lifecycles from creation to promotion across environments, design schemas with tables/columns/relationships/forms/views, and perform data operations like querying, CRUD, bulk CSV imports using Python SDK and Web API.

Build forecasting datasets and fine-tune models with Lightning Rod SDK. Ingest data from BigQuery, local files, or web sources, apply temporal splitting and domain transforms, then train using GRPO or SFT patterns.

Dispatch AI agents to forecast, score, classify, research, deduplicate, merge, and filter rows in Python pandas DataFrames at scale using natural language instructions. Access remote MCP endpoints for search tools and Everyrow shared databases for queries, schema inspection, and manipulation via API keys.

Analyze survey microdata with weighted pandas DataFrames to compute Gini coefficients, poverty rates, quantiles, and inequality metrics. Impute missing values using ML methods like random forest and XGBoost from donor data. Calibrate weights to population targets with L0 regularization. Enhance datasets like CPS ASEC and run PolicyEngine microsimulations for tax-benefit policy impacts across populations.

Automate end-to-end ML performance investigations: research SOTA papers and architectures, generate phased plans, judge experimental methodologies, profile bottlenecks, run metric-improvement campaigns with atomic git commits, auto-rollback on regressions, and leverage specialist agents for data lifecycle and deep paper analysis.

Automate creation, editing, analysis, and data extraction from office documents including DOCX with tracked changes and comments, PDFs with tables and forms, XLSX spreadsheets with formulas and charts, PPTX presentations via XML and HTML conversion. Use AI agent to plan ordered skill chains for complex goals and run health checks on skills.

Model and analyze electric power networks with pandapower: construct grids from buses, lines, transformers, loads, generators; compute AC/DC power flows, optimal power flow, IEC 60909 short circuits, state estimation, time series simulations, topology checks, and plots.

Generate optimized SQL queries from natural language for BigQuery, PostgreSQL, MySQL, and Snowflake; perform cohort analysis on CSV/Excel user data to compute retention rates, visualize trends, and detect anomalies; evaluate A/B tests with statistical significance, confidence intervals, and launch recommendations.

Streamline end-to-end data science and ML workflows: frame business problems into ML tasks, preprocess and validate data with quality checks, perform EDA on diverse formats, design and execute experiments with hyperparameter tuning via Optuna and interpretability via SHAP, audit reproducibility and leakage, evaluate model performance and readiness for deployment, generate model cards, and extract structured learnings into docs.

Search arXiv, INSPIRE-HEP, NASA ADS, HEPData, and Zenodo for physics papers and experimental data by keywords, authors, or IDs; download BibTeX, LaTeX source, CSV/YAML/JSON/ROOT files; visualize posterior samples from PolyChord/MultiNest with corner plots, marginals, and KL divergence using matplotlib/pandas.

Automate academic research in finance, economics, and real estate: search literature via Corbis, generate and rank novel ideas with heuristics, screen for novelty and journal fit, visualize trends and gaps with Python-generated figures, draft structured reviews and positioning memos, read/summarize papers with a subagent, and audit citations in LaTeX files.

Automate end-to-end AI/ML academic research workflows: perform topic-driven literature surveys with taxonomies, gaps, and innovations; generate peer/meta-reviews for paper PDFs; detect/insert citations in LaTeX; evaluate/refine ideas into proposals with diagrams/experiments; create reveal.js slides/posters from papers.

Build domain expertise on any topic via zero-context research pipeline: generate question trees, discover and fetch sources to disk, index into .mv2 knowledge store, distill compact artifacts for agent use without raw docs in context. Run Python in isolated Docker sandboxes for safe data analysis, prototyping, and DSPy sub-agents.

Balance class-imbalanced datasets for machine learning workflows using oversampling (SMOTE, ADASYN, Borderline-SMOTE), undersampling, or hybrid techniques applied only to training splits, then save resampled X/y train/test data to an output directory.

Automate end-to-end content creation: collect RSS hotspots and trends, generate viral AI-drafted articles for WeChat Public Account, Xiaohongshu, and Feishu with style imitation and visuals, review for quality/compliance risks, analyze performance from data files with charts, and publish via APIs or Playwright browser automation after one-time credential setup.