start-3-1-1

name	start-3-1-1
description	Lesson 3.1.1: Welcome & First Generation. Use when the student types /start-3-1-1.
disable-model-invocation	true
allowed-tools	["Read","Write","Bash"]

Setup

Read .claude/skills/_shared/teaching-rules.md and follow it for everything below.

cp -rn .claude/skills/start-3-1-1/assets/* . 2>/dev/null || true

Module 3.1.1: Welcome & First Generation

Teaching Script for Claude Code

---

Teaching Flow

Welcome to Nano Banana Pro in Claude Code for PMs!

I'm going to quote the course creator Carl here: "You aren't going to believe how fucking amazing this is."

He's right. We're going to use Gemini 3 Pro - also known as Nano Banana Pro - Google's most advanced image generation model. It can create photorealistic images, transform reference photos, generate text overlays, and so much more.

STOP: Are you ready to see what it can do?

USER: Yes / Ready

---

Before we generate anything, we need to set up your API key. This takes about 2-3 minutes and you only do it once.

First, go to Google AI Studio:

https://aistudio.google.com/

STOP: Open that link in your browser and let me know when you're there.

USER: I'm there

---

If this is your first time, accept the Terms of Service.

Then:

1. Click "Get API Key" in the left sidebar
2. Click "Create API key" in the upper right
3. Name your key whatever you want and select "Default Gemini Project"
4. Click on the key in the dashboard to reveal it
5. Copy the key - it starts with "AIza..."

STOP: Do you have your API key copied?

USER: Yes

---

Important: You also need to set up billing for Gemini 3 Pro to work.

Don't worry about cost - it's about $0.10 per image, and this whole course will cost less than $5 total. (This goes to Google, not to Carl... unfortunately.)

1. In Google AI Studio, go to Get API key (bottom of left sidebar)
2. Under the "Quota tier" column, click Set up billing
3. Follow the prompts to add a payment method

STOP: Is your billing set up?

USER: Yes / Done

---

Now let's add your API key to this project. You have two options:

Option 1: Just paste your API key here and I'll create the .env file for you.

Option 2: Do it yourself - copy .env.example to .env and add your key: GEMINI_API_KEY=YourKeyHere

STOP: Paste your API key here, or say "Done" if you set it up manually.

USER: [Pastes API key] / Done

---

ACTION: If user provided an API key, create .env file with GEMINI_API_KEY=[their key]. If user said "Done", verify .env exists.

Great, you're all set up!

Now for the fun part - let's generate your first image.

I have a reference photo of Carl, the course creator. You can find it at lesson-modules/3-nano-banana/3.1-intro-to-image-gen/3.1.1-welcome/carl-reference.JPG if you want to see what you're working with. We're going to put him in a banana suit to welcome you to the course.

STOP: Say "Generate Carl in a banana suit welcoming me to Nano Banana"

USER: Generate Carl in a banana suit welcoming me to Nano Banana

---

This will take about 10-15 seconds

ACTION: Run generate() with reference image carl-reference.JPG and prompt: "Carl in a bright yellow banana suit, standing confidently with arms crossed, big friendly smile, ready to teach. Text overlay says 'Welcome to Nano Banana!' Professional course instructor vibe but fun and playful."

Your image has been saved to the outputs/ folder - open it to see the result!

STOP: What do you think?

USER: Response about the image

---

Pretty incredible, right?

That's Gemini 3 Pro. It can take a reference photo and transform it while keeping the person recognizable. And because we're doing this in Claude Code, I handle all the complexity for you:

• API calls
• Session management
• Saving outputs
• Picking smart parameters

Through this course, you'll build your own system of prompts, reference images, and styles. We'll start by covering general use and then move onto PM use cases in the next module.

STOP: Want to hear what's coming next in this module?

USER: Yes

---

Here's what we'll cover in Module 3.1:

• Understanding the Basics - how the generate() function works, available parameters, output structure
• Consistency & Style - using reference images, building a style database, extracting styles from existing images
• Iteration Strategies - sessions, refining images step by step, reverting when needed

Once you've got the fundamentals down, Module 3.2 will show you PM-specific use cases like mockups, personas, and diagrams.

STOP: Ready to continue to the next lesson?

USER: Yes / Ready

---

Great! In the next lesson, you'll learn how the generate() function works and all the options available to you.

Run /start-3-1-2 when you're ready to continue.

ACTION: End module

---

Important Notes for Claude

File operations in this module:

• Read .env.example to show the student the template
• Copy .env.example to .env when student requests
• The student manually pastes their API key - don't try to insert it for them

For the image generation:

• Use the generate() function from image_gen.py
• Pass the reference image path and the exact prompt specified
• The output will be saved automatically to outputs/

If something goes wrong:

• API key errors: Have them double-check the key is pasted correctly in .env
• Billing errors: Confirm billing is set up in Google AI Studio settings
• Generation fails: Check the error message and troubleshoot accordingly

Opening images: If a user is having trouble finding an image, offer to open it for them using open [path] (Mac) or start [path] (Windows).

---

Success Criteria

Module 3.1.1 is successful if the student:

• ✅ Has their Gemini API key set up in .env
• ✅ Has billing configured in Google AI Studio
• ✅ Generated their first image (Carl in banana suit)
• ✅ Understands what they'll learn in this module
• ✅ Knows how to continue to the next lesson

---

Remember: This is the student's first taste of image generation. Make it magical. The "wow" moment with that first generated image sets the tone for the entire module.

ACTION: Silently run the following progress update. Do not mention it to the student.

mkdir -p .fspm
[ -f .fspm/progress.json ] || printf '{"name":null,"completed_lessons":[],"current_lesson":"","last_updated":""}' > .fspm/progress.json
L="cc-pms-3-1-1"; C="cc-pms-3-1-2"; T="$(date -u +%FT%TZ)"
if command -v jq >/dev/null 2>&1; then
  tmp=$(mktemp)
  jq --arg l "$L" --arg c "$C" --arg t "$T" '.completed_lessons = ((.completed_lessons + [$l]) | unique) | .current_lesson = $c | .last_updated = $t' .fspm/progress.json > "$tmp" && mv "$tmp" .fspm/progress.json
else
  python3 - "$L" "$C" "$T" <<'PY'
import json,sys
l,c,t = sys.argv[1:4]
p = ".fspm/progress.json"; d = json.load(open(p))
if l not in d.get("completed_lessons",[]): d.setdefault("completed_lessons",[]).append(l)
d["current_lesson"] = c; d["last_updated"] = t
json.dump(d, open(p,"w"))
PY
fi

Next lesson: Type /start-3-1-2.