doc
Use this skill when reading, creating, or editing Word documents where formatting, layout, and visual presentation are important. It leverages python-docx for structured document operations and provides rendering tools to convert documents to PDF or images for verification before delivery.
git clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-Skills /tmp/doc && cp -r /tmp/doc/bundled/skills/doc ~/.claude/skills/docSKILL.md
# DOCX Skill ## When to use - Read or review DOCX content where layout matters (tables, diagrams, pagination). - Create or edit DOCX files with professional formatting. - Validate visual layout before delivery. ## Workflow 1. Prefer visual review (layout, tables, diagrams). - If `soffice` and `pdftoppm` are available, convert DOCX -> PDF -> PNGs. - Or use `scripts/render_docx.py` (requires `pdf2image` and Poppler). - If these tools are missing, install them or ask the user to review rendered pages locally. 2. Use `python-docx` for edits and structured creation (headings, styles, tables, lists). 3. After each meaningful change, re-render and inspect the pages. 4. If visual review is not possible, extract text with `python-docx` as a fallback and call out layout risk. 5. Keep intermediate outputs organized and clean up after final approval. ## Temp and output conventions - Use `tmp/docs/` for intermediate files; delete when done. - Write final artifacts under `output/doc/` when working in this repo. - Keep filenames stable and descriptive. ## Dependencies (install if missing) Prefer `uv` for dependency management. Python packages: ``` uv pip install python-docx pdf2image ``` If `uv` is unavailable: ``` python3 -m pip install python-docx pdf2image ``` System tools (for rendering): ``` # macOS (Homebrew) brew install libreoffice poppler # Ubuntu/Debian sudo apt-get install -y libreoffice poppler-utils ``` If installation isn't possible in this environment, tell the user which dependency is missing and how to install it locally. ## Environment No required environment variables. ## Rendering commands DOCX -> PDF: ``` soffice -env:UserInstallation=file:///tmp/lo_profile_$$ --headless --convert-to pdf --outdir $OUTDIR $INPUT_DOCX ``` PDF -> PNGs: ``` pdftoppm -png $OUTDIR/$BASENAME.pdf $OUTDIR/$BASENAME ``` Bundled helper: ``` python3 scripts/render_docx.py /path/to/file.docx --output_dir /tmp/docx_pages ``` ## Quality expectations - Deliver a client-ready document: consistent typography, spacing, margins, and clear hierarchy. - Avoid formatting defects: clipped/overlapping text, broken tables, unreadable characters, or default-template styling. - Charts, tables, and visuals must be legible in rendered pages with correct alignment. - Use ASCII hyphens only. Avoid U+2011 (non-breaking hyphen) and other Unicode dashes. - Citations and references must be human-readable; never leave tool tokens or placeholder strings. ## Final checks - Re-render and inspect every page at 100% zoom before final delivery. - Fix any spacing, alignment, or pagination issues and repeat the render loop. - Confirm there are no leftovers (temp files, duplicate renders) unless the user asks to keep them.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Requires a free API key from alphavantage.co.
Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
Full-stack software development agent for design, implementation, testing, and deployment. Use when the user explicitly asks for end-to-end project creation, feature development, bug fixing, or code refactoring.
Unified biological database evidence owner. Use for gene annotation, variant clinical significance, cancer mutation evidence, GWAS trait associations, pathway mapping, target-disease evidence, protein structures, protein interaction networks, reference single-cell census queries, and cross-database biological ID mapping. Do not use for full single-cell analysis, bulk RNA-seq differential expression, BAM/VCF processing, protein embedding models, metabolic flux modeling, genomic interval ML, or flow-cytometry file parsing.