metricsai

Inserts a pedagogical "Key Concepts" glossary section into a metricsAI chapter notebook (.qmd), between Chapter Overview and Setup. Each entry has a bold term, a 1–2 sentence definition, and two side-by-side collapsible Quarto callouts — a dataset-grounded *Example* and a vivid *Analogy* — so readers can skim definitions and expand only the panels they need. The new section is ADDITIVE: it complements (does not replace) the chapter's existing inline `> **Key Concept N.M**` blockquotes by picking 5–8 concepts disjoint from them, sized to chapter complexity, and tied to the chapter's `**What you'll learn:**` bullets. Refuses to run if the section already exists. Fully autonomous one-shot — drafts content, inserts, syncs all formats (.ipynb / .md / .html), verifies the HTML render, and reports. Does not commit. Goal is to foster learning through dataset-grounded examples and vivid analogies. Invoke via /add-key-concepts-section chNN.

Creates a self-contained Python code summary ("cheat sheet") for a metricsAI chapter's Key Takeaways section. Reads the chapter .qmd and its web app to identify key libraries, functions, datasets, and concepts, then generates a single markdown code block that reproduces the chapter's core workflow. The code block is pedagogical (commented), self-contained (runs in an empty Colab notebook), and aligned with the web app's key concepts. Invoke via /create-chapter-code-summary chNN.

Audits and improves the readability and pedagogical scaffolding of a metricsAI chapter web app (web-apps/chNN/template.html). Restructures each widget into a six-part pattern proven on Chapter 2 — Motivation → Key Concept → What you can do here → Controls + Chart → Try-this with reveals → Take-away. Uses the chapter's .qmd as the source of truth for Key Concept wording. Invoke via /improve-webapp-readability chNN [--apply] [--all]. Only touches prose scaffolding and CSS; never touches JavaScript, data, or chart behavior.

Creates a single-file interactive HTML dashboard for a metricsAI chapter. Follows the design and pedagogical conventions established by the Chapter 2 app (Plotly.js, brand palette, light/dark theme, embedded JSON, URL-hash state, Try-this prompts per widget, Reset buttons, delta readouts). Flexible across chapters via reusable widget snippets and a scaffolding script. Invoke via /web-app <chNN>. The goal is pedagogical learning through interaction — design widgets that target specific Key Concepts, not pretty charts.

Audits and improves readability of metricsAI chapter notebooks (.qmd). Checks code cell clarity, explanation ordering, interpretation guidance, regression output presentation, prose quality, and jargon management. Generates a prioritized report with specific line numbers and before/after suggestions. Use after chapter-standard compliance, before proofread. Supports --apply for automated fixes and --all for batch analysis.

Verifies and standardizes metricsAI chapter notebooks against CH01-04 template. Checks structure (45-75 cells, 70-80% markdown), front matter (visual summary, Learning Objectives, overview, setup), content (section numbering, 7-11 Key Concepts), back matter (Key Takeaways, Practice Exercises). Generates compliance report with CRITICAL/MINOR/SUGGESTIONS. Use when reviewing chapters, before PDF generation, or ensuring consistency. Supports --apply for automated fixes.

Compiles all chapter PDFs into a single metricsAI book PDF. Detects modified chapters, regenerates their PDFs, then merges all chapters with cover page, copyright page, Brief Contents, Detailed Contents, Key Concepts TOC, clickable TOC hyperlinks, page numbers, and section-level PDF bookmarks. Use after editing any chapter notebook.

Proofread metricsAI Jupyter notebook chapters for text quality issues. Detects missing spaces (concatenated words), doubled words, broken LaTeX, unclosed markdown formatting, inconsistent terminology, and content accuracy. Use after standardization, before final PDF generation, or when reviewing chapter quality. Supports --fix for automated corrections.