bundled/skills/doc/SKILL.md
Use when the task involves reading, creating, or editing `.docx` documents, especially when formatting or layout fidelity matters; prefer `python-docx` plus the bundled `scripts/render_docx.py` for visual checks.
npx skillsauth add foryourhealth111-pixel/vco-skills-codex docInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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soffice and pdftoppm are available, convert DOCX -> PDF -> PNGs.scripts/render_docx.py (requires pdf2image and Poppler).python-docx for edits and structured creation (headings, styles, tables, lists).python-docx as a fallback and call out layout risk.tmp/docs/ for intermediate files; delete when done.output/doc/ when working in this repo.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.
No required environment variables.
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
development
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
development
Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.
development
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
development
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.