developer-day-curriculum

name	developer-day-curriculum
description	Generate workshop agendas and hands-on curriculum for customer developer days, technical training sessions, and field engagements
license	MIT
metadata	{"version":"1.0.0","author":"Michael Lynn [mlynn.org](https://mlynn.org)","category":"education","domain":"technical-training","updated":"2026-03-01T00:00:00.000Z","python-tools":"audience_analyzer.py, agenda_generator.py, exercise_scaffolder.py","tech-stack":"python, json, markdown"}

developer-day-curriculum

Trigger

Use this skill when planning customer developer days, field workshops, technical training sessions, or conference tutorials.

Trigger phrases:

• "Plan a developer day for [customer]"
• "Create workshop agenda"
• "Design hands-on training"
• "Build curriculum for [topic]"
• "Generate developer day schedule"

Overview

Developer days require careful planning: the right topics, appropriate depth, hands-on exercises that work, and perfect pacing. This skill analyzes customer needs, generates time-blocked agendas, creates scaffolded exercises, and produces follow-up materials.

Not for: Self-paced courses or recorded content. This is for live, interactive workshops with hands-on components.

How to Use

Quick Start

1. Analyze audience:

   python scripts/audience_analyzer.py customer-profile.json --output analysis.json
   

2. Generate agenda:

   python scripts/agenda_generator.py analysis.json --duration 6 --output agenda.md
   

3. Create exercises:

   python scripts/exercise_scaffolder.py agenda.md --output exercises/
   

Python Tools

• scripts/audience_analyzer.py — Analyze customer tech stack, experience level, goals
• scripts/agenda_generator.py — Generate time-blocked agenda with topics and breaks
• scripts/exercise_scaffolder.py — Create hands-on exercise templates and solution code

Reference Docs

• references/workshop-design-patterns.md — Proven workshop structures and pacing
• references/customer-engagement-best-practices.md — Field engagement patterns from 50+ developer days

Templates & Assets

• assets/agenda-template-halfday.md — 3-4 hour workshop template
• assets/agenda-template-fullday.md — 6-8 hour workshop template
• assets/exercise-template.md — Hands-on exercise structure
• assets/feedback-survey.md — Post-workshop feedback template

Architecture Decisions

Why Audience Analysis First

Different audiences need different approaches:

Audience Type	Approach	Example Topics
Beginners	Guided, step-by-step	"Intro to MongoDB", "Your First Query"
Intermediate	Problem-solving focused	"Schema Design Workshop", "Aggregation Deep Dive"
Advanced	Architecture & optimization	"Performance Tuning", "Sharding Strategies"
Mixed	Layered (core + advanced breakouts)	Morning: Foundations, Afternoon: Tracks

Tool: audience_analyzer.py categorizes audience and recommends depth.

Time-Blocking Strategy

Attention spans are finite. Research shows:

• 10-15 min: Maximum lecture without interaction
• 45-60 min: Maximum before needing a break
• 90 min: Maximum hands-on exercise session

Agenda structure:

09:00-09:15  Welcome & Introductions (15 min)
09:15-09:45  Topic 1: Lecture (30 min)
09:45-10:30  Exercise 1: Hands-on (45 min)
10:30-10:45  Break (15 min)
10:45-11:15  Topic 2: Lecture (30 min)
...

Pattern: Lecture → Exercise → Break (repeat)

Hands-On Exercise Philosophy

Progressive Disclosure:

1. Starter code provided (80% complete)
2. Clear TODOs with hints
3. Solution available but not shown upfront
4. Extension challenges for fast finishers

Anti-pattern: Blank slate ("Build X from scratch"). Most participants fail.

Good pattern: Guided scaffolding with specific gaps to fill.

Pacing Rules

Duration	Topics	Exercises	Breaks
Half-day (3-4h)	2-3 topics	2 exercises	1 break
Full-day (6-8h)	4-6 topics	4-5 exercises	3+ breaks
Multi-day	8-12 topics	8-10 exercises	Lunch + breaks each day

Rule of thumb: 1 topic per hour (30 min lecture, 30 min exercise/discussion).

Follow-Up Materials

Participants forget 70% within 24 hours without reinforcement. Provide:

• Slide deck (PDF, not editable)
• Exercise repo (GitHub with solutions)
• Resource links (docs, tutorials, videos)
• Next steps (suggested learning path)
• Contact info (Slack, email, office hours)

Generated Output Structure

Agenda (Markdown)

# MongoDB Developer Day - [Customer Name]
**Date:** [Date] | **Duration:** 6 hours | **Level:** Intermediate

## Goals
- [Learning objective 1]
- [Learning objective 2]

## Prerequisites
- Basic MongoDB knowledge
- Laptop with [requirements]

## Schedule

### Morning Session (9:00 AM - 12:00 PM)

#### 9:00-9:15 - Welcome & Introductions (15 min)
- Icebreaker
- Agenda overview
- Wi-Fi and logistics

#### 9:15-9:45 - Topic 1: [Title] (30 min)
**Learning objective:** [What participants will learn]
**Key concepts:** [Bullet list]

#### 9:45-10:30 - Exercise 1: [Title] (45 min)
**Goal:** [What participants will build]
**Starter code:** `exercises/01-topic-name/`
**Success criteria:** [How to know you're done]

...

## Follow-Up Resources
- [Links]

Exercise Structure

exercises/
├── 01-topic-name/
│   ├── README.md          # Instructions
│   ├── starter/           # Starter code (80% complete)
│   │   └── app.js
│   ├── solution/          # Complete solution
│   │   └── app.js
│   └── extension/         # Bonus challenges
│       └── challenges.md

Python Tool Details

1. Audience Analyzer

Purpose: Analyze customer profile and recommend topics/depth.

Input: Customer profile JSON

{
  "company": "Acme Corp",
  "industry": "FinTech",
  "audience_size": 25,
  "experience_level": "mixed",
  "current_stack": ["PostgreSQL", "Redis", "Python"],
  "mongodb_experience": "beginner",
  "goals": [
    "Migrate from PostgreSQL to MongoDB",
    "Learn schema design patterns"
  ],
  "constraints": [
    "6 hour duration",
    "Must include hands-on exercises"
  ]
}

Output: Analysis JSON

{
  "recommended_level": "intermediate",
  "suggested_topics": [
    "MongoDB fundamentals (quick refresher)",
    "Relational to document migration patterns",
    "Schema design workshop",
    "Aggregation pipeline deep dive"
  ],
  "pacing": {
    "lecture_ratio": 0.4,
    "exercise_ratio": 0.5,
    "break_ratio": 0.1
  },
  "focus_areas": [
    "Emphasize schema design (coming from relational)",
    "Include PostgreSQL → MongoDB migration example",
    "Address transaction concerns (FinTech requirement)"
  ]
}

2. Agenda Generator

Purpose: Generate time-blocked agenda from analysis.

Usage:

python scripts/agenda_generator.py analysis.json \
  --duration 6 \
  --start-time "9:00 AM" \
  --output agenda.md

Options:

• --duration - Hours (default: 6)
• --start-time - Start time (default: 9:00 AM)
• --include-lunch - Add lunch break for full-day
• --format - Output format (markdown, html, pdf)

Output: Markdown agenda with:

• Time-blocked schedule
• Topic descriptions
• Exercise placeholders
• Break timing
• Prerequisites and setup instructions

3. Exercise Scaffolder

Purpose: Generate hands-on exercise templates from agenda.

Usage:

python scripts/exercise_scaffolder.py agenda.md --output exercises/

Generates:

• Exercise directory structure
• README with instructions
• Starter code templates
• Solution placeholders
• Extension challenges

Example output:

exercises/
├── 01-schema-design/
│   ├── README.md
│   ├── starter/
│   │   ├── schema.js        # 80% complete with TODOs
│   │   └── sample-data.json
│   ├── solution/
│   │   └── schema.js        # Complete solution
│   └── extension/
│       └── challenges.md    # Bonus challenges

Workflow Example

Scenario: Plan a 6-hour MongoDB developer day for FinTech customer

Step 1: Create customer profile

{
  "company": "FinTech Corp",
  "audience_size": 20,
  "experience_level": "intermediate",
  "current_stack": ["PostgreSQL", "Python", "AWS"],
  "mongodb_experience": "beginner",
  "goals": ["Migration from PostgreSQL", "Schema design"],
  "constraints": ["6 hours", "Must include transactions"]
}

Step 2: Analyze audience

python scripts/audience_analyzer.py fintech-profile.json --output analysis.json

Output: Recommends intermediate level, focus on migration patterns and transactions

Step 3: Generate agenda

python scripts/agenda_generator.py analysis.json \
  --duration 6 \
  --start-time "9:00 AM" \
  --include-lunch \
  --output fintech-agenda.md

Output: 6-hour agenda with 4 topics, 4 exercises, lunch, 2 breaks

Step 4: Create exercises

python scripts/exercise_scaffolder.py fintech-agenda.md --output exercises/

Output: 4 exercise directories with starter code, solutions, extensions

Step 5: Customize

• Fill in starter code with FinTech-specific examples
• Add real PostgreSQL → MongoDB migration example
• Customize transaction exercise for financial use case

Step 6: Deliver

• Present agenda
• Walk through exercises
• Collect feedback
• Share follow-up resources

Common Patterns

Pattern 1: The "Hook" Opening

Start with a compelling demo, not slides.

Bad:

"Welcome. Today we'll learn about MongoDB. Here are 30 slides..."

Good:

"Let me show you something cool." [Live demo: Build a real-time dashboard in 10 minutes] "That's what you'll build today. Let's get started."

Pattern 2: "You Try" Checkpoints

Every 15 minutes, pause for a quick "you try" moment.

Example:

"I just showed you how to create an index. Open your terminal and try it with your dataset. 2 minutes. Go!"

Prevents passive watching.

Pattern 3: Progressive Complexity

Start simple, layer on complexity.

Exercise 1: Basic CRUD operations Exercise 2: Add indexes for performance Exercise 3: Add aggregation pipeline Exercise 4: Add transactions and error handling

Each builds on the previous.

Pattern 4: Real-World Context

Use customer's industry for examples.

Generic (boring):

"Here's a user schema..."

Industry-specific (engaging):

"Here's how to model a financial transaction with MongoDB..."

Pattern 5: Fast Finisher Extensions

Always have bonus challenges ready.

## Exercise 1: Basic Schema Design

**Success Criteria:**
- [x] Created schema
- [x] Inserted sample data
- [x] Queried data

**Done early? Try these extensions:**
1. Add validation rules
2. Create compound indexes
3. Add data migration script

Keeps advanced participants engaged.

Timing Anti-Patterns

❌ Too Much Content

Cramming 12 topics into 6 hours. Result: Rushed, no hands-on time.

Fix: 4-6 topics maximum for 6 hours.

❌ No Breaks

Running 3 hours straight without a break.

Fix: Break every 60-90 minutes minimum.

❌ Long Lectures

45-minute lecture with no interaction.

Fix: 15-minute chunks with "you try" checkpoints.

❌ Blank Slate Exercises

"Build a complete app from scratch in 45 minutes."

Fix: Provide 80% starter code with specific TODOs.

❌ Skipping Setup Time

Jumping straight into exercises without verifying everyone's environment works.

Fix: 15-minute setup verification at start.

Quality Checklist

Before delivering:

• Agenda has clear learning objectives
• Time blocks total to actual duration
• Breaks scheduled every 60-90 minutes
• Each exercise has starter code + solution
• Extension challenges ready for fast finishers
• Setup instructions tested on fresh machine
• Backup plan if live demos fail
• Feedback survey prepared
• Follow-up resources compiled
• Contact info for post-workshop questions

When to Use Different Formats

Format	Best For	Example
Half-day (3-4h)	Conference tutorials, intro workshops	"Intro to Vector Search"
Full-day (6-8h)	Customer developer days, deep dives	"MongoDB Schema Design Workshop"
Multi-day (2-3 days)	Comprehensive training, bootcamps	"MongoDB Developer Bootcamp"
Workshop series	Ongoing enablement	Weekly 2-hour sessions over 4 weeks

Customization Tips

For Enterprise Customers

• Use their industry terminology
• Reference their specific use cases
• Include compliance/security topics
• Provide enterprise deployment examples

For Startups

• Move faster, less hand-holding
• Focus on practical "get it working" approach
• Include scaling considerations
• Share cost optimization tips

For Mixed Audiences

• Morning: Core fundamentals (everyone)
• Afternoon: Breakout tracks (beginner vs advanced)
• Provide self-paced catch-up materials

Follow-Up Strategy

Immediately after (same day):

• Share slide deck PDF
• Send GitHub repo link with exercises/solutions
• Collect feedback survey

Next day:

• Send thank you email
• Share additional resources
• Offer office hours

One week later:

• Check in on progress
• Answer questions
• Share related content

One month later:

• Follow-up on original goals
• Offer advanced workshop if interest

References

• Workshop design patterns: references/workshop-design-patterns.md
• Field engagement best practices: references/customer-engagement-best-practices.md
• Adult learning theory: https://en.wikipedia.org/wiki/Andragogy
• Hands-on exercise design: https://teaching.cornell.edu/resource/active-learning

Credits

Michael Lynn — mlynn.org · @mlynn · LinkedIn · GitHub

---

Next steps after generating curriculum:

1. Customize exercises with customer-specific examples
2. Test all hands-on exercises on fresh environment
3. Prepare backup demos (in case of connectivity issues)
4. Create feedback survey
5. Compile follow-up resources