The Complete Claude Code Guide for Game Designers — From Specs to Balancing

Introduction

This guide is an educational resource designed so that game designers with no programming experience can immediately apply Claude Code to their work. It focuses on how to make requests to Claude in natural language, without needing to write code yourself.

This document is structured for use as internal training and workshop material.

What This Guide Covers

Part	Topic	Key Question
Part 1	Basic Setup	How do I get started with Claude Code?
Part 2	Spec Workflow	How do I systematically write and validate specs?
Part 3	Data Analysis & Visualization	How do I analyze and present data?
Part 4	Game Balancing	How do I automate numerical design?
Part 5	Comprehensive Agent Usage	Which agent should I use and when?
Appendix	Prompt Collection	Copy-paste-ready prompts

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Part 1: Basic Setup

1-1. What is Claude Code?

Claude Code is a terminal-based AI assistant provided by Anthropic. Unlike regular chat:

• It can directly read and write files
• It can write and execute Python scripts
• It can leverage multiple agents simultaneously
• It understands and remembers project context

1-2. Minimum Environment for Designers

Required:
  - Claude Code installed
  - A working folder (project directory)

Recommended:
  - Python installed (for data analysis/visualization)
  - Google Cloud service account (for Sheets integration)

1-3. First Run — Essential Commands for Designers

After launching Claude Code, just speak in natural language.

You: "Create a folder structure for design docs"
Claude: Creating docs/plan/, docs/design/, docs/data/ folders

No need to memorize commands. Just say what you want to do.

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Part 2: Spec Workflow

Automating the core game designer workflow — spec writing → validation → technical specification conversion — with Claude Code.

2-1. Writing Specs — /pdca plan

Basic Usage

You: /pdca plan gacha-system

This single line makes Claude auto-generate:

• Feature overview
• User stories
• Feature requirements list
• Priority classification
• Risk analysis

Example Generated Document

# Gacha System Spec

## 1. Overview
A system where players spend in-game currency to obtain random items

## 2. User Stories
- Players can do a single pull (1x)
- Players can do a multi-pull (10x)
- Players can receive guaranteed rewards through the pity system
...

## 3. Feature Requirements
### P0 (Must-have)
- [ ] Random item distribution based on probability table
- [ ] Pity counter management
- [ ] Currency deduction and insufficient funds error handling
...

When You Need More Detailed Specs — plan-plus

You: /plan-plus gacha-system

plan-plus is an enhanced version of /pdca plan that:

1. Intent Exploration — First asks “Why is this feature needed?”
2. Alternative Comparison — Compares 2–3 design approaches
3. YAGNI Review — Suggests removing features not needed right now

Effective when brainstorming is needed in the early planning stage.

2-2. Spec Validation — design-validator

After writing the spec, automatically check for missing items or contradictions.

Usage

You: "Validate the spec at docs/plan/gacha-system.md"

What Claude Detects

Validation Item	Example
Missing fields	“Error handling scenarios are not defined”
Numerical contradictions	“Max rewards is 10, but the reward table has 12 entries”
Terminology inconsistency	“Section 2 uses ‘diamonds’, Section 3 uses ‘gems’”
Missing edge cases	“Behavior when currency is 0 is not defined”
Undefined dependencies	“Integration method with the inventory system is not specified”

Practical Tips

You: "Are there any missing items in this spec?"
You: "Find contradictions in this design doc"
You: "Find points that the QA team would likely flag"

2-3. Technical Spec Conversion — /pdca design

Once the spec is complete, convert it into a technical specification that the dev team can work from immediately.

Usage

You: /pdca design gacha-system

Conversion Example

The spec’s “Random item distribution based on probability table” converts to:

## API Design

### POST /api/gacha/pull
- Request: { userId, poolId, count: 1|10 }
- Response: { items: [...], pityCounter, remainCurrency }

## Database Schema

### gacha_pools
| Column | Type | Description |
|--------|------|-------------|
| id | UUID | Gacha pool ID |
| name | VARCHAR | Pool name |
| rates | JSONB | Rates by rarity |

### gacha_history
| Column | Type | Description |
|--------|------|-------------|
| id | UUID | Record ID |
| user_id | UUID | User ID |
| pool_id | UUID | Pool ID |
| items | JSONB | Obtained item list |
| pity_count | INT | Pity counter |

Designers don’t need to design APIs or databases themselves — the tool automatically translates design intent into technical language.

2-4. Spec vs Implementation Gap Analysis — gap-detector

After development progresses, verify whether the implementation matches the spec.

You: "Compare the spec with the actual implementation code and run a gap analysis"

Result:

📊 Gap Analysis Report
━━━━━━━━━━━━━━━━━━━━━
Match Rate: 78%

❌ Unimplemented Items:
  - Pity counter reset logic (Spec Section 3.2)
  - 10-pull bonus item distribution (Spec Section 3.5)

⚠️ Implementation differs from spec:
  - Probability decimal handling: Spec says round, implementation truncates

✅ Correctly implemented: 12/16 items

If below 90%, you can automatically receive fix suggestions.

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Part 3: Data Analysis & Visualization

The area designers will use the most. Give Claude data and request analysis and visualization — it automatically writes and runs Python scripts.

3-1. CSV/Excel Data Analysis

Basic Pattern

You: "Analyze data/user_logs.csv and find
     user churn points"

What Claude does:

1. Read CSV file (pandas)
2. Analyze per-user play patterns
3. Detect churn points
4. Summarize results and visualize

Practical Request Examples

# Basic statistics
You: "Show me the basic stats (mean, median, distribution) of this CSV"

# Filtering
You: "Extract only users level 30+ and analyze their spending rate"

# Comparative analysis
You: "Compare the retention difference between Group A and Group B"

# Time series analysis
You: "Show me the monthly DAU trend as a graph"

3-2. KPI Metric Design & Analysis

Game KPI Framework Request

You: "Create a core KPI framework for a mobile RPG.
     Organize by category and include calculation formulas
     and healthy benchmark values for each metric"

Framework generated by Claude:

## User Acquisition Metrics
| KPI | Formula | Healthy Benchmark |
|-----|---------|-------------------|
| DAU | Daily active users | - |
| MAU | Monthly active users | - |
| DAU/MAU | DAU ÷ MAU | 20~30% |

## Engagement Metrics
| KPI | Formula | Healthy Benchmark |
|-----|---------|-------------------|
| Session length | Average play time | 15~30 min |
| Session frequency | Daily avg logins | 2~4 times |
| D1 Retention | Day1 logins ÷ installs | 40~50% |
| D7 Retention | Day7 logins ÷ installs | 15~25% |
| D30 Retention | Day30 logins ÷ installs | 5~10% |

## Revenue Metrics
| KPI | Formula | Healthy Benchmark |
|-----|---------|-------------------|
| ARPU | Total revenue ÷ active users | Varies by genre |
| ARPPU | Total revenue ÷ paying users | 10~50x ARPU |
| Conversion rate | Paying users ÷ active users | 2~5% |
| LTV | ARPU × avg lifespan | 3x+ CPI |

Funnel Analysis

You: "Here's the tutorial funnel data.
     Calculate the drop-off rate per step and show me a chart.

     Step1_Start: 10000
     Step2_CharacterCreation: 8500
     Step3_FirstBattle: 7200
     Step4_RewardClaim: 6800
     Step5_SecondBattle: 4100
     Step6_TownArrival: 3800
     Step7_TutorialComplete: 3500"

Claude automatically:

1. Calculates step-by-step drop-off rates
2. Generates a conversion funnel chart
3. Highlights the most severe drop-off points
4. Suggests improvements (e.g., “38% drop-off between Step4→Step5. Review second battle difficulty or reward insufficiency”)

3-3. Data Visualization

Request Methods by Chart Type

# Bar chart
You: "Draw a bar chart of revenue by category"

# Line graph
You: "Show me the 30-day DAU trend as a line graph"

# Heatmap
You: "Show concurrent users by time of day and day of week as a heatmap"

# Pie chart
You: "Show the currency consumption distribution as a pie chart"

# Box plot
You: "Show session time distribution by level bracket as a box plot"

# Scatter plot
You: "Show the correlation between play time and spending as a scatter plot"

Dashboard Generation

You: "Create an HTML dashboard with the following data.
     It would be great if it could be filtered interactively.
     - DAU/MAU trends
     - Revenue trends
     - Retention curves
     - Content consumption by category"

Claude generates an interactive HTML dashboard based on plotly. You can open it directly in your browser.

3-4. Google Workspace Integration

Reading Google Sheets Data

You: "Create a Python script that reads data from Google Sheets.
     Sheet name: 'UserStats_2026Q1'
     Required columns: date, dau, revenue, new_users"

Code structure generated by Claude:

# 1. Google API authentication setup
# 2. Read sheet data
# 3. Convert to pandas DataFrame
# 4. Analysis and visualization

Automated Workflow

You: "Create a script that reads Google Sheets data daily
     and automatically generates a daily report"

Structure:

1. Collect data via Sheets API
2. Calculate day-over-day change rates
3. Detect anomalies (sudden DAU drops, etc.)
4. Auto-generate summary report in markdown

Writing Analysis Results to Sheets

You: "Automatically write the analysis results to the 'AnalysisResults' sheet in Google Sheets"

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Part 4: Game Balancing

4-1. Numerical Table Design

Experience/Level Curve

You: "Create a level 1~100 experience table.
     Conditions:
     - Level 1→2: 100 EXP
     - Level 99→100: 500,000 EXP
     - Early levels (1~20) should be gentle, late levels (80~100) steep
     - Curve type: exponential growth

     Output both CSV and graph"

What Claude does:

1. Fit exponential function parameters (start: 100, end: 500,000)
2. Generate per-level required EXP table as CSV
3. Output cumulative EXP graph
4. Calculate estimated level-up time (based on EXP per minute)

Stat Growth Curves

You: "Create a per-level stat table for the Warrior class.

     Base values (Level 1):
     - HP: 500, ATK: 50, DEF: 30, SPD: 10

     Target values at Level 100:
     - HP: 50000, ATK: 3000, DEF: 2000, SPD: 100

     Growth curves: HP and DEF linear, ATK exponential, SPD logarithmic

     Show me a table and graph in increments of 10 levels"

Currency Economy Model

You: "Create a game economy simulation.

     Daily income:
     - Daily quests: 500 gold
     - Dungeon rewards: 300~800 gold (avg 550)
     - Login rewards: 200 gold

     Daily expenses:
     - Equipment enhancement: 100~2000 gold
     - Consumable purchases: 200~500 gold
     - Gacha: 300 gold (per pull)

     Run a 30-day simulation to check
     whether gold inflation occurs"

Results:

• Daily net income change graph
• Projected gold holdings distribution after 30 days
• Inflation risk zones highlighted
• Adjustment suggestions (e.g., “Income exceeds expenses from Day 15 onward. Additional gold sinks needed”)

4-2. Probability Simulation

Gacha Probability Verification

You: "Verify the gacha probability table through simulation.

     Probabilities:
     - SSR: 1.5%
     - SR: 13.5%
     - R: 85%

     Pity: Guaranteed SSR within 90 pulls
     10-pull bonus: 1 guaranteed SR or above

     Run 100,000 simulations to check:
     1. Whether actual SSR rate matches the displayed rate
     2. Percentage that reaches pity
     3. Average investment to obtain 1 SSR"

Monte Carlo simulation performed by Claude:

📊 Simulation Results (100,000 runs)
━━━━━━━━━━━━━━━━━━━━━━━━━━
Actual SSR rate: 1.52% (displayed: 1.5%) ✅
Pity reach rate: 25.3%
Avg pulls for 1 SSR: 47.2 (median: 42)
Worst case for 1 SSR: 90 pulls (pity)

💡 Analysis:
- 1 in 4 players hits pity
- Average spend (at 300 won per pull): 14,160 won
- Top 10% lucky: SSR within 12 pulls

Drop Rate Simulation

You: "Calculate the expected revenue from the boss drop table.

     Drop table:
     - Legendary weapon (0.5%)
     - Heroic equipment (5%)
     - Rare material (20%)
     - Common material (74.5%)

     Gold value per item:
     - Legendary: 100,000
     - Heroic: 10,000
     - Rare: 1,000
     - Common: 100

     Show me the expected revenue distribution after killing the boss 100 times"

4-3. Combat Balancing

DPS/EHP Analysis

You: "Analyze the combat balance of 5 classes.

     Warrior: HP 5000, ATK 300, DEF 200, SPD 80, skill multiplier 1.5
     Mage: HP 2500, ATK 500, DEF 80, SPD 100, skill multiplier 2.0
     Archer: HP 3000, ATK 400, DEF 100, SPD 120, skill multiplier 1.8
     Healer: HP 3500, ATK 150, DEF 150, SPD 90, skill multiplier 1.0 (heal)
     Assassin: HP 2000, ATK 450, DEF 60, SPD 150, skill multiplier 2.5

     Calculate DPS (damage per second) and EHP (effective health)
     and show me a radar chart to check balance"

Difficulty Curve Analysis

You: "I'll give you the stage 1~50 monster data CSV.
     Analyze whether the difficulty curve is natural,
     and find wall sections where difficulty spikes.

     Comparison criteria:
     - Monster stat ratio vs. same-level player average stats
     - Changes in estimated clear time"

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Part 5: Comprehensive Agent Usage

5-1. Key Agents Designers Should Know

Agent	One-line Description	Designer Use Case
product-manager	Requirements analysis + user story creation	Early planning, organizing requirements
design-validator	Spec completeness validation	Spec review stage
gap-detector	Spec vs implementation comparison	QA or mid-development check
qa-strategist	QA strategy formulation	Writing test plans
code-analyzer	Code quality analysis	Inspecting dev deliverables
report-generator	Completion report generation	At milestone completion

5-2. Real-World Workflow — From New Feature Planning to Launch

Step 1: Planning — /pdca plan {feature-name}
         ↓
Step 2: Validation — Check spec with design-validator
         ↓
Step 3: Tech conversion — /pdca design {feature-name}
         ↓
Step 4: Mid-dev check — Gap analysis with gap-detector
         ↓
Step 5: Quality inspection — Code quality check with code-analyzer
         ↓
Step 6: Completion report — /pdca report {feature-name}

5-3. How to Invoke Agents

Agents are automatically triggered by natural language:

"Validate this spec"              → design-validator auto-runs
"Does the implementation match?"  → gap-detector auto-runs
"Analyze code quality"            → code-analyzer auto-runs
"Generate a completion report"    → report-generator auto-runs

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Part 6: Practical Scenario Collection

Scenario 1: New Character Balancing

You: "I'm adding a new character 'Flame Sorcerer'.
     Here's the existing 5-class stat table (CSV).

     Flame Sorcerer concept:
     - High-damage ranged dealer
     - Low HP
     - AoE attack specialist

     Suggest a stat range that won't break
     the existing balance"

Scenario 2: Event Reward Design

You: "Design a 7-day login event reward table.

     Constraints:
     - Total reward value: under 1,000 won worth of premium currency
     - Rewards should get more attractive each day
     - Biggest reward on Day 7 (churn prevention)
     - Include impact analysis on game economy"

Scenario 3: A/B Test Results Analysis

You: "Here are the tutorial A/B test results.

     Group A (existing): 1000 users, 35% completion, 18% D7 retention
     Group B (new): 1000 users, 52% completion, 22% D7 retention

     Test whether the difference is statistically significant.
     95% confidence interval."

Scenario 4: Content Consumption Speed Prediction

You: "Current content status:
     - Main story: 50 chapters
     - Daily quests: 5 types
     - Weekly bosses: 3 types
     - Events: biweekly

     Predict time to content exhaustion for
     heavy users (4 hrs/day) and light users (30 min/day)"

Scenario 5: Competitor Analysis Framework

You: "Create a framework to compare gacha systems of 3 competing RPGs.

     Comparison criteria:
     - Probability structure
     - Pity system
     - Free currency earning rate
     - Spending efficiency (expected SSRs per $10)

     Organize in spreadsheet format"

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Appendix A: Prompt Collection for Designers

Spec-Related

# Spec writing
"Write a spec for the XXX system. Include user stories, feature requirements, and priorities"

# Spec validation
"Find missing items or contradictions in this spec"

# Technical spec conversion
"Convert this design doc into a technical spec the dev team can work from immediately"

# Spec comparison
"Summarize the differences between the v1 and v2 specs"

Data Analysis-Related

# Basic analysis
"Show me the basic statistics and distribution of this CSV data"

# Anomaly detection
"Find abnormal values or patterns in this data"

# Correlation analysis
"Analyze the correlation between play time, level, and spending"

# Cohort analysis
"Draw a retention curve by registration month cohort"

Balancing-Related

# Numerical design
"Create a level 1~N growth table. Conditions: ..."

# Probability verification
"Verify this probability table with N0,000 simulation runs"

# Economy analysis
"Run an inflation simulation with currency inflow/outflow data"

# Balance check
"Calculate DPS/EHP with these class stats and check the balance"

Visualization-Related

# Single chart
"Draw the XXX data as a [bar/line/pie/heatmap] chart"

# Dashboard
"Create an interactive HTML dashboard with this dataset"

# Google Sheets integration
"Read and analyze data from the 'XXX' sheet in Google Sheets"

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Appendix B: Troubleshooting

Common Issues and Solutions

Problem	Cause	Solution
“Can’t read the CSV”	Wrong file path	Check absolute or relative path
“Chart isn’t showing”	Python/matplotlib not installed	Run pip install matplotlib pandas
“Can’t connect to Google Sheets”	Auth not configured	Service account key file setup needed
“Simulation is too slow”	Too many simulation runs	Adjust to 10K~100K level

Keys to Effective Requests

1. Include specific numbers — “Balance this” ❌ → “Calculate DPS based on HP 5000, ATK 300” ✅
2. Specify output format — “Analyze this” ❌ → “Show me as CSV and bar chart” ✅
3. State constraints — “Make rewards” ❌ → “Total value under 1000 won, 7-day login basis” ✅
4. Provide comparison criteria — “Is this okay?” ❌ → “Check if D7 retention improved vs. 20% benchmark” ✅

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Conclusion

Claude Code is not a tool that replaces designers — it’s a tool that backs up designers’ judgment with data.

• Specs: Automates structuring and validation, but the designer decides the design intent
• Data: Automates analysis and visualization, but interpretation and decisions are the designer’s responsibility
• Balancing: Automates simulation and calculation, but the designer sets the standard for fun

Claude is not perfect. Always verify important numbers and decisions yourself.