create-chapter-code-summary

name: create-chapter-code-summary description: 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. argument-hint: [chapter-number] context: fork agent: Explore

Create Chapter Code Summary Skill

Generates a Python Libraries and Code subsection for a chapter's Key Takeaways section. The output is a single, self-contained markdown code block that students can copy into an empty Google Colab notebook and run end-to-end. It serves as a pedagogical cheat sheet of the chapter's most important libraries, functions, and analytical workflow.

Why this skill exists

Students finishing a chapter need a quick-reference code summary that ties together everything they learned. Reading back through 40-70 cells to find the key commands is tedious. A well-designed cheat sheet:

• Reinforces the chapter's core workflow in a single scannable block
• Aligns with the web app's interactive key concepts, creating a bridge between reading and doing
• Gives students a runnable starting point they can modify for their own data
• Serves as a study aid before exams or when returning to material later

The pattern was proven on Chapter 1 and is now codified here for all chapters.

When to use

Invoke with /create-chapter-code-summary chNN (e.g., /create-chapter-code-summary ch05) when:

• A chapter's content is finalized and all code cells are stable
• The chapter's web app exists (needed to identify key concepts)
• You want to add the code summary before generating PDFs or publishing the book

Do not invoke this skill if the chapter is still being actively edited or if major code changes are pending.

Non-negotiables

What this skill WILL do:

• Read the chapter .qmd to extract all imports, datasets, key functions, and the analytical workflow
• Read the chapter's web app to identify key concepts that guide which code steps are most important
• Generate a single markdown code block (```python) with pedagogical comments
• Insert it into the Key Takeaways section before the "Next Steps" divider
• Add a "Try it yourself!" line with a link to an empty Google Colab notebook
• Render the chapter with Quarto to verify no errors

What this skill WILL NOT do:

• Create executable code cells ({python}) — the block is markdown-only, for students to copy
• Use collapsible toggles (code-fold) — the code is always visible
• Include dark theme styling (plt.style.use('dark_background'), custom rcParams)
• Include infrastructure code (os, random.seed, os.makedirs, directory creation)
• Include case study datasets — only the chapter's primary dataset(s)
• Modify any code cells in the chapter body
• Touch the web app files

Inputs

Before generating, collect these two sources:

1. The chapter .qmd — notebooks_quarto/chNN_*.qmd. Source of truth for all code: imports, data URLs, functions, analytical steps.
2. The chapter web app — web-apps/chNN/template.html (or dashboard.html if no template exists). Source of truth for which key concepts matter most — each widget title and key-concept callout tells you what the chapter's most important ideas are.

If the web app does not exist, proceed using only the .qmd — derive key concepts from the chapter's > **Key Concept N.M: blockquotes instead.

Workflow

Follow these phases in order.

Phase 1 — Read the chapter

Read the full chapter .qmd and extract:

• All imports from the Setup cell (the first code cell, usually with code-fold: true)
• Dataset URL(s) and loading method (pd.read_stata(), pd.read_csv(), etc.) — only the primary dataset, not case study datasets
• Key functions and methods used in the chapter body (e.g., .describe(), ols().fit(), .summary(), ax.scatter(), ax.hist())
• The analytical workflow — the sequence of steps from data loading to final results
• Key variable names — the column names used in regressions, plots, and interpretations

Phase 2 — Read the web app

Read web-apps/chNN/template.html and extract:

• Widget titles — each <h2> inside a <section class="widget"> tells you the concept name
• Key concept callouts — each .key-concept box states the core idea the widget teaches
• The progression — widgets are ordered pedagogically; the code summary should follow the same arc

Build a mapping: widget title -> key concept -> corresponding code step in the chapter.

If no web app exists, extract key concepts from the chapter's > **Key Concept N.M: blockquotes instead.

Phase 3 — Design the code block

Using the web app key concepts as a guide, decide:

1. Which code steps to include — each web app widget/key concept should map to at least one code step
2. Which libraries to import — only those actually used in the included steps
3. What to exclude — infrastructure, dark theme, case study data, redundant variations
4. The step order — follow the web app's pedagogical progression (which mirrors the chapter's narrative arc)

Typical structure for most chapters:

# LIBRARIES
# STEP 1: Load data
# STEP 2: Explore / describe (descriptive statistics)
# STEP 3: Visualize (the key visualization pattern of the chapter)
# STEP 4: Model / compute (the chapter's main analytical method)
# STEP 5: Interpret results (extract and print key statistics)
# STEP 6: Advanced application (comparison, diagnostics, or extension)

The number of steps varies by chapter (5-8 is typical). Each step should correspond to one or more web app key concepts.

Phase 4 — Write the code block

Generate the code following these formatting rules:

Structure:

• Start with a banner: # CHAPTER N CHEAT SHEET: Chapter Title
• Group imports under # --- Libraries --- with inline comments explaining each library's role
• Separate each step with a banner: # ===...=== / # STEP N: Description / # ===...===
• End with the final analytical output (a summary table, a key statistic, or a comparison)

Comments:

• Every import gets an inline comment explaining its purpose (e.g., # data loading and manipulation)
• Every step gets a 1-2 line comment explaining why this step matters, not what it does syntactically
• Key methods get brief inline comments on first use (e.g., # marginal effect: $/sq ft)
• Do NOT over-comment — one comment per conceptual idea, not per line

Self-containedness:

• All imports at the top
• Dataset URL defined as a variable (full URL, not referencing a constant from the Setup cell)
• All intermediate variables defined within the block
• No references to variables from the chapter's other code cells
• Must run in a fresh Python environment (Google Colab)

Styling:

• Use default matplotlib colors (no hex codes from the dark theme)
• Use figsize=(10, 6) for plots (standard)
• Use descriptive axis labels and titles
• For regression output, use both print() for key stats and .summary() for the full table

Phase 5 — Insert into the chapter

Insert the code summary into the Key Takeaways section of the .qmd file:

Location: After the last bold-header subsection in Key Takeaways (e.g., **Prerequisites and Mathematical Background:**) and before the --- horizontal rule that precedes **Next Steps:**.

If no --- / **Next Steps:** divider exists, insert before ## Practice Exercises.

Format:

**Python Libraries and Code:**

This single code block reproduces the core workflow of Chapter N. It is self-contained — copy it into an empty notebook and run it to review the complete pipeline from [brief description of what the pipeline covers].

` ` `python
[the generated code block]
` ` `

**Try it yourself!** Copy this code into an empty Google Colab notebook and run it: [Open Colab](https://colab.research.google.com/notebooks/empty.ipynb)

The intro sentence should be adapted to each chapter — mention the specific topic (e.g., "from data loading to hypothesis testing" or "from summary statistics to confidence intervals").

Phase 6 — Verify

1. Render the chapter: cd book && quarto render notebooks_quarto/chNN_*.qmd
2. Confirm no rendering errors
3. Visually check the output — the code block should appear as a syntax-highlighted, always-visible block in the Key Takeaways section

Code block design rules

These rules are non-negotiable. They encode the conventions proven on Chapter 1.

What to include vs. exclude

Always include:

• The 3-4 core libraries the chapter uses (pandas, matplotlib, statsmodels, scipy, etc.)
• Loading the chapter's primary dataset from its GitHub URL
• Descriptive statistics (.describe(), .value_counts(), etc.) — every chapter uses these
• The chapter's signature visualization (scatter, histogram, box plot, etc.)
• The chapter's main analytical method (OLS, t-test, confidence interval, F-test, etc.)
• Extracting and printing key results with interpretation
• At least one step that connects to the chapter's central takeaway message

Always exclude:

• Setup infrastructure (os, random, RANDOM_SEED, directory creation)
• Dark theme code (plt.style.use('dark_background'), plt.rcParams.update(...))
• Case study datasets (Mendez convergence clubs, DS4Bolivia) — those are for practice
• Redundant variations of the same analysis (e.g., if the chapter shows 3 scatter plots, include 1)
• Print statements for setup confirmations ("Setup complete!")
• Intermediate debugging or exploration code
• Code that only exists to support later sections (e.g., helper functions used once)

Use judgment for:

• Multiple regression models — include if comparing models is a key concept, otherwise pick the main one
• Data cleaning steps — include if the chapter teaches data preparation, otherwise skip
• Advanced methods — include if they are a key concept in the web app, otherwise skip

Handling chapters without web apps

If a chapter does not yet have a web app in web-apps/chNN/:

1. Extract key concepts from the chapter's > **Key Concept N.M: blockquotes
2. Use the chapter's section headers (## N.1, ## N.2, etc.) to determine the narrative arc
3. Follow the same 6-phase workflow, substituting key concept blockquotes for web app widgets

Handling chapters with existing code summaries

If the chapter already has a **Python Libraries and Code:** subsection:

1. Read the existing code block
2. Determine if it needs updating (e.g., chapter content changed, web app was added/modified)
3. If updating, replace the existing block — do not create a second one
4. If the existing block is adequate, report "Code summary already exists and is up to date" and stop

Reference files

• references/CH01_REFERENCE.md — the Chapter 1 code summary with annotations explaining each design decision. Use this as the gold standard when generating summaries for other chapters.

Relationship to other skills

/chapter-standard chNN              (ensure chapter structure is correct)
      |
/improve-readability chNN           (improve prose and code clarity)
      |
/proofread chNN                     (fix text quality issues)
      |
/create-chapter-code-summary chNN   (THIS SKILL — add code cheat sheet)
      |
/compile-book chNN                  (generate PDF)

The code summary should be one of the last things added before PDF generation, since it depends on the chapter's code being finalized.

The web app is an input to this skill (read-only), not a dependency that must be created first. If the web app exists, use it to guide key concept selection. If not, use the chapter's Key Concept blockquotes.

Version: 1.0 Created: 2026-04-18 Prerequisite: Chapter content finalized in .qmd. Web app recommended but not required. Audience: Beginner econometrics students — comments and structure should assume no prior Python experience beyond the chapter itself.