docx-writer
Draft and generate structured Word documents, reports, proposals, and formal writeups.
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Draft and generate structured Word documents, reports, proposals, and formal writeups.
Downloads education datasets from configured mirror sources (parquet/CSV) with local Polars filtering. Use when writing fetch scripts or retrieving CCD, IPEDS, CRDC, SAIPE data. Load after education-data-explorer — retrieval here, not discovery.
Build useful spreadsheets, financial models, formulas, pivots, and editable xlsx outputs.
Python table formatting with great-tables: publication-quality display tables from Polars/pandas DataFrames. GT() grammar-of-tables object, fmt_*() number formatting, tab_*() structure (header, spanners, stub, source notes), tab_style() and data_color() styling, cols_*() column operations, and HTML/LaTeX export via as_raw_html()/write_raw_html()/as_latex(). Use when execution language is Python and the task involves formatted data tables, summary tables, or descriptive-stat tables for reports. R equivalent: gt (use when execution language is R). For regression/model tables in Python, use the estimator's own output (pyfixest etable(), statsmodels summary()) — great-tables has no modelsummary equivalent. For figures/charts use plotnine or plotly, not this skill.
R table formatting with gt, kableExtra, modelsummary. Publication-quality tables: gt() grammar-of-tables, fmt_*() formatting, tab_*() structure, gtsave() export. Use when execution language is R. Python equivalent: great-tables (use when execution language is Python).
Quarto document system for R: .qmd format with knitr engine, YAML frontmatter, code chunks with execution options, rendering to HTML/PDF. DAAF's R notebook format — Stage 9 literally archives executed R scripts in globally and per-chunk non-evaluating chunks paired with real execution logs. Optional display-only content may preview existing Parquet data or show already-created figures, but may not analyze data or generate figures. Use when execution language is R. Python equivalent: marimo.
Implement LDA topic modeling to discover latent topics in document collections. Use this skill when the user needs to extract topics from a text corpus, categorize documents by theme, or explore thematic structure — even if they say 'what are the main topics', 'topic extraction', or 'document clustering by theme'.
Generate, remix, or edit images with Nanobanana / Nano Banana 2 through the bundled Gemini CLI wrapper. Use this whenever the user wants AI image generation or editing, especially for reference-image composition, character consistency, grounded visuals that may need live web search, style transfer, marketing graphics, product mockups, social assets, or when they explicitly mention Nanobanana, Gemini image models, Google image generation, AI drawing, 图片生成, AI绘图, 图片编辑, or 生成图片.
Calculate text similarity using lexical and semantic methods for matching and deduplication. Use this skill when the user needs to find similar documents, detect near-duplicates, or measure semantic closeness between texts — even if they say 'how similar are these texts', 'find duplicates', or 'semantic matching'.
Conduct Exploratory Data Analysis (EDA) using descriptive statistics, visualizations, and data quality checks. Use this skill when the user has a dataset and needs to understand its structure, find patterns, detect anomalies, or prepare data for further analysis — even if they say 'what does this data look like', 'find interesting patterns', 'clean this data', or 'summarize this dataset'.